https://www.polymtl.ca/api.php?action=feedcontributions&feedformat=atom&user=NicolasSaunierTransport - Contributions de l’utilisateur [fr]2026-08-04T13:11:30ZContributions de l’utilisateurMediaWiki 1.24.0https://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1050PythonHowTo2026-07-17T12:00:56Z<p>NicolasSaunier : /* Random Topics */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [https://ipython.org/ IPython interpreter] and, to a lesser degree, [https://jupyter.org/ Jupyter] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them (if installed through pip, it should wordk without any other action). The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements (it also helps diagnose if your path is wrong by looking at the content of sys.path):<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
= Random Topics=<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1049PythonHowTo2026-07-17T12:00:43Z<p>NicolasSaunier : /* Links */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [https://ipython.org/ IPython interpreter] and, to a lesser degree, [https://jupyter.org/ Jupyter] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them (if installed through pip, it should wordk without any other action). The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements (it also helps diagnose if your path is wrong by looking at the content of sys.path):<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1048PythonHowTo2026-07-17T12:00:18Z<p>NicolasSaunier : /* Tools and Scientific libraries */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [https://ipython.org/ IPython interpreter] and, to a lesser degree, [https://jupyter.org/ Jupyter] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them (if installed through pip, it should wordk without any other action). The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements (it also helps diagnose if your path is wrong by looking at the content of sys.path):<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1047PythonHowTo2026-07-17T12:00:01Z<p>NicolasSaunier : /* Environment Variables for your Code and Other Downloaded Libraries */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [https://ipython.org/ IPython interpreter] and, to a lesser degree, [https://jupyter.org/ Jupyter] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them (if installed through pip, it should wordk without any other action). The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements (it also helps diagnose if your path is wrong by looking at the content of sys.path):<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1046PythonHowTo2026-07-17T11:58:45Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [https://ipython.org/ IPython interpreter] and, to a lesser degree, [https://jupyter.org/ Jupyter] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1045PythonHowTo2026-07-17T11:58:19Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the [[https://ipython.org/|IPython interpreter]] and, to a lesser degree, [[https://jupyter.org/|Jupyter]] (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1044PythonHowTo2026-07-17T11:56:39Z<p>NicolasSaunier : /* Old Material */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, Jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1043PythonHowTo2026-07-17T11:55:20Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, Jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>):<br />
<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV, scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Old Material=<br />
<br />
I recommend a good text editor with indispensable functionalities such as text coloring and parenthesis highlighting such as [http://notepad-plus-plus.org notepad++] on Windows, or [https://atom.io/ atom] and [http://projects.gnome.org/gedit/ gedit] (for Windows, Mac and Linux).<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1042PythonHowTo2026-07-17T11:50:01Z<p>NicolasSaunier : /* Old Material */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>).<br />
<br />
= Old Material=<br />
<br />
I recommend a good text editor with indispensable functionalities such as text coloring and parenthesis highlighting such as [http://notepad-plus-plus.org notepad++] on Windows, or [https://atom.io/ atom] and [http://projects.gnome.org/gedit/ gedit] (for Windows, Mac and Linux).<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Programming_Resources&diff=1041Programming Resources2026-07-16T05:40:18Z<p>NicolasSaunier : /* General */</p>
<hr />
<div><br />
=General=<br />
* Getting started (Creative Coding for Beginners - Full Course! by Adam Shiffman) https://thecodingtrain.com/guides/getting-started<br />
* [[DataScienceResources|Data science resources]]<br />
<br />
It is indispensable to use a good text editor with essential functionalities such as text coloring and parenthesis highlighting like [http://notepad-plus-plus.org notepad++] on Windows, [http://projects.gnome.org/gedit/ gedit] or [https://www.gnu.org/software/emacs/ Emacs] (for Windows, Mac and Linux).<br />
<br />
=Python=<br />
* Resources and tutorials for the transport data management course CIV8760 (mostly in French) https://github.com/nsaunier/CIV8760/blob/master/Python/README.md, and simple [[PythonHowTo|how-to]] to install Python<br />
* Official Python tutorial https://docs.python.org/3/tutorial/index.html (in [https://docs.python.org/fr/3/tutorial/ French] and the tutor (step by step visualization of the program) https://pythontutor.com/<br />
* Python 1-h video tutorial https://www.youtube.com/watch?v=kqtD5dpn9C8<br />
* [http://software-carpentry.org/lessons Software Carpentry lessons], in particular https://swcarpentry.github.io/python-novice-inflammation/ and http://swcarpentry.github.io/python-novice-gapminder (lessons and videos)<br />
* [https://www.w3resource.com/python-exercises Hundreds of Python exercises]<br />
* [https://github.com/jakevdp/WhirlwindTourOfPython A Whirlwind Tour of Python]<br />
* [https://www.learnpython.org LearnPython.org]<br />
* https://github.com/mpcs-51042/materials<br />
* in French<br />
** [https://www.youtube.com/playlist?list=PLfALbWbNPl4bSWH-Pbj3iIb_TVQdr99pH liste des vidéos du cours INF1005D]<br />
** [https://openclassrooms.com/fr/courses/235344-apprenez-a-programmer-en-python Openclassrooms - Apprenez à programmer en Python]<br />
** [https://python.doctor/ Apprendre le langage de programmation python]<br />
* Online books:<br />
** [http://greenteapress.com/wp/think-python/ Think Python]<br />
** [http://www.diveinto.org/python3/ Dive into Python 3] and [http://www.diveintopython.net/ Dive into Python]<br />
** [https://python.developpez.com/cours/apprendre-python3/ Apprendre à programmer avec Python 3] (en français)<br />
** Python Programming And Numerical Methods: A Guide For Engineers And Scientists https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html<br />
** Research Software Engineering with Python https://third-bit.com/py-rse/<br />
* Older tutorials http://n.saunier.free.fr/saunier/teaching/workshop/12-09-script-ta-ville.html, [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
<br />
=R=<br />
* Introduction to R programming (in French), Vincent Goulet https://cran.r-project.org/doc/contrib/Goulet_introduction_programmation_R.pdf<br />
* Workshop on R software https://github.com/sahirbhatnagar/atelier-R-GERAD<br />
* Guide pour l’analyse de données d’enquêtes avec R (in French) https://larmarange.github.io/guide-R/<br />
<br />
=Databases=<br />
* Wikibook SQL http://en.wikibooks.org/wiki/SQL<br />
* Murrell P., Introduction to Data Technologies 2009 http://www.stat.auckland.ac.nz/~paul/ItDT/<br />
* SQL for Web Nerds http://philip.greenspun.com/sql/<br />
* Software Carpentry: Using Databases and SQL http://swcarpentry.github.io/sql-novice-survey<br />
<br />
=Version Control=<br />
* [[Mercurial|Mercurial]]<br />
* Others: Git https://glasskube.dev/guides/git/, Subversion<br />
<br />
=Other Languages=<br />
* [https://p5js.org/ p5js]: porting of processing in javascript (video lessons by Adam Shiffman at https://thecodingtrain.com)<br />
* [http://www.gnu.org/software/octave Octave]: Matlab clone<br />
* C/C++: GCC compiler, cygwing or MinGW environnements (Windows)</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1040PythonHowTo2026-07-16T05:37:49Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages (<tt>pip install -r requirements.txt</tt>).<br />
<br />
= Old Material=<br />
<br />
* It is easy to install the necessary packages using easy_install/pip/conda once you have the basics<br />
** pip is recommended with the online script https://bootstrap.pypa.io/get-pip.py. Any <tt>module</tt> can be installed through and updated: <tt>pip install <module> --upgrade</tt> (will install in the user home directory).<br />
<br />
* I recommend a scientific distribution of Python, ie Python with the improved interpreter iPython and the core scientific libraries (Numpy, Scipy, Matplotlib, Pandas, etc.) that are used for example in the [https://trafficintelligence.confins.net Traffic Intelligence] project:<br />
** There are three main choices: [https://www.continuum.io/why-anaconda anaconda], [https://python-xy.github.io/ PythonXY] and [https://assets.enthought.com/downloads/ Enthought Python Distribution]. <br />
** In addition, if you want to install other Python packages not provided by default, such as OpenCV that is needed to display video data and replay extracted trajectories over the video (used in the cvutils module). For example:<br />
<br />
$ conda install opencv3<br />
$ pip install opencv-python<br />
<br />
or (you can search your preferred version using)<br />
<br />
$ anaconda search -t conda opencv<br />
$ anaconda show <user/package><br />
$ conda install --channel https://conda.anaconda.org/menpo opencv3 # for example<br />
<br />
I recommend a good text editor with indispensable functionalities such as text coloring and parenthesis highlighting such as [http://notepad-plus-plus.org notepad++] on Windows, or [https://atom.io/ atom] and [http://projects.gnome.org/gedit/ gedit] (for Windows, Mac and Linux).<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1039PythonHowTo2026-07-16T05:35:34Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt Traffic Intelligence requirements.txt] for the recommended scientific packages.<br />
<br />
= Old Material=<br />
<br />
* It is easy to install the necessary packages using easy_install/pip/conda once you have the basics<br />
** pip is recommended with the online script https://bootstrap.pypa.io/get-pip.py. Any <tt>module</tt> can be installed through and updated: <tt>pip install <module> --upgrade</tt> (will install in the user home directory).<br />
<br />
* I recommend a scientific distribution of Python, ie Python with the improved interpreter iPython and the core scientific libraries (Numpy, Scipy, Matplotlib, Pandas, etc.) that are used for example in the [https://trafficintelligence.confins.net Traffic Intelligence] project:<br />
** There are three main choices: [https://www.continuum.io/why-anaconda anaconda], [https://python-xy.github.io/ PythonXY] and [https://assets.enthought.com/downloads/ Enthought Python Distribution]. <br />
** In addition, if you want to install other Python packages not provided by default, such as OpenCV that is needed to display video data and replay extracted trajectories over the video (used in the cvutils module). For example:<br />
<br />
$ conda install opencv3<br />
$ pip install opencv-python<br />
<br />
or (you can search your preferred version using)<br />
<br />
$ anaconda search -t conda opencv<br />
$ anaconda show <user/package><br />
$ conda install --channel https://conda.anaconda.org/menpo opencv3 # for example<br />
<br />
I recommend a good text editor with indispensable functionalities such as text coloring and parenthesis highlighting such as [http://notepad-plus-plus.org notepad++] on Windows, or [https://atom.io/ atom] and [http://projects.gnome.org/gedit/ gedit] (for Windows, Mac and Linux).<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=PythonHowTo&diff=1038PythonHowTo2026-07-16T05:32:15Z<p>NicolasSaunier : /* Install Modules */</p>
<hr />
<div>= Where to start =<br />
[[Programming_Resources#Python|Python tutorials]]<br />
== Virtual Environments for Python Libraries ==<br />
<br />
The new recommended way to install Python modules (libraries) is to use virtual environments to avoid mixing your installed packages with the system Python: <br />
<br />
$ python -m venv <directory-name><br />
$ source <directory-name>/bin/activate<br />
<br />
Once one wants to exit the Python environment, one should run the deactivate command:<br />
<br />
$ deactivate<br />
<br />
There is otherwise a more recent and very interesting tool called [https://docs.astral.sh/uv/ uv] to manage Python environments. <br />
<br />
On the group Linux computers, the libraries installed on the <tt>~nicolas</tt> account are accessible to avoid duplication, in particular for the deep learning ultralytics library.<br />
<br />
== Install Modules ==<br />
<br />
Ref https://docs.python.org/3/installing/index.html<br />
<br />
Once you have a virtual environment, activate and install the desired modules:<br />
<br />
$ pip install SomePackage<br />
<br />
or<br />
<br />
$ python -m pip install SomePackage<br />
<br />
I highly recommend the IPython interpreter and, to a lesser degree, jupyter (for the notebooks). See [https://sourceforge.net/p/traffic-intelligence/code/ci/default/tree/python-requirements.txt|Traffic Intelligence requirements.txt] for the recommended scientific packages.<br />
<br />
= Old Material=<br />
<br />
* It is easy to install the necessary packages using easy_install/pip/conda once you have the basics<br />
** pip is recommended with the online script https://bootstrap.pypa.io/get-pip.py. Any <tt>module</tt> can be installed through and updated: <tt>pip install <module> --upgrade</tt> (will install in the user home directory).<br />
<br />
* I recommend a scientific distribution of Python, ie Python with the improved interpreter iPython and the core scientific libraries (Numpy, Scipy, Matplotlib, Pandas, etc.) that are used for example in the [https://trafficintelligence.confins.net Traffic Intelligence] project:<br />
** There are three main choices: [https://www.continuum.io/why-anaconda anaconda], [https://python-xy.github.io/ PythonXY] and [https://assets.enthought.com/downloads/ Enthought Python Distribution]. <br />
** In addition, if you want to install other Python packages not provided by default, such as OpenCV that is needed to display video data and replay extracted trajectories over the video (used in the cvutils module). For example:<br />
<br />
$ conda install opencv3<br />
$ pip install opencv-python<br />
<br />
or (you can search your preferred version using)<br />
<br />
$ anaconda search -t conda opencv<br />
$ anaconda show <user/package><br />
$ conda install --channel https://conda.anaconda.org/menpo opencv3 # for example<br />
<br />
I recommend a good text editor with indispensable functionalities such as text coloring and parenthesis highlighting such as [http://notepad-plus-plus.org notepad++] on Windows, or [https://atom.io/ atom] and [http://projects.gnome.org/gedit/ gedit] (for Windows, Mac and Linux).<br />
<br />
== Environment Variables for your Code and Other Downloaded Libraries ==<br />
<br />
To use Python modules from your code or downloaded code, the Python interpreter needs to know where to look for them. The first and preferred way is to set (or add to) the environment variable <tt>PYTHONPATH</tt> to refer to the location of the Python modules to load:<br />
<br />
* on Windows, add or modify the <tt>PYTHONPATH</tt> environment variable. Given the example installation path <tt>C:\Code\my_python_modules\</tt>, it should be set to <tt>C:\Code\my_python_modules\</tt>, e.g. <tt>C:\Users\username\traffic-intelligence\</tt> for the [https://bitbucket.org/Nicolas/trafficintelligence/ Traffic Intelligence] modules (in the trafficintelligence package); <br />
* on Linux, add or modify the <tt>PYTHONPATH</tt> environment variable by typing <tt>$export PYTHONPATH=/home/username/Code/my_python_modules/</tt> in a terminal. However, you have to do that for each terminal before running a Python interpreter. The better way is to set the environment variable when your shell starts. If using bash, simply add the previous command at the end of the user <tt>.bashrc</tt> file (in the home directory);<br />
* on both platforms, other paths can be added, separated by <tt>:</tt>.<br />
<br />
If that did not work, you can add the path to the sys.path system variable in the Python interpreter or at the beginning of your code, before your import statements:<br />
<br />
$ import sys<br />
$ sys.append('path_to_python_modules')<br />
$ import your_module<br />
<br />
To test your installation, start a Python interpreter (or even better, ipython) in a '''new terminal''' (the variable will not be added to the ones that were started before the <tt>PYTHONPATH</tt> variable was added) and try to import one of your modules in the directory referred to by <tt>PYTHONPATH</tt>:<br />
<br />
$ import your_module<br />
<br />
== Tools and Scientific libraries == <br />
<br />
Tools<br />
* Better interpreter: ipython https://ipython.org/<br />
* Running and presenting code with Jupyter Notebooks https://jupyter.org/<br />
Libraries<br />
* Matplotlib http://matplotlib.org<br />
* Numpy http://www.numpy.org<br />
* Computer Vision: OpenCV , scikit-image<br />
* Statistics and machine learning: scipy, scikit-learn<br />
* Geometry and GIS: shapely<br />
* Tabular data loading/processing: pandas<br />
* ORM: sqlalchemy<br />
<br />
= Links =<br />
* Finding the shortest cycling path in the shade https://medium.com/@tanyamarleytsui/shady-streets-6dad0979c13a<br />
<br />
= Random Topics=<br />
== Shapely ==<br />
- I left functions in moving (in Point) that returns whether a point is in a polygon or not.<br />
inPolygonNoShapely(polygon) where polygon is a Nx2 numpy array representing the polygon<br />
<br />
- with Shapely, use their polygon and point class, eg<br />
<br />
from shapely.geometry import Polygon, Point<br />
poly = Polygon(array([[0,0],[0,1],[1,1],[1,0]]))<br />
p = Point(0.5,0.5)<br />
poly.contains(p)<br />
-> returns True<br />
poly.contains(Point(-1,-1))<br />
-> returns False<br />
<br />
You can convert a moving.Point to a shapely point: p = moving.Point(1,2)<br />
p.asShapely() returns the equivalent shapely point<br />
<br />
If you have several points to test, use moving.pointsInPolygon(points, polygon) where points are moving.Point and polygon is a shapely polygon.<br />
<br />
You should dig in shapely for functions that compute intersections (there is one in our library, but it is slow):<br />
http://toblerity.github.io/shapely/manual.html#object.crosses<br />
from shapely.geometry import Polygon, Point, LineString<br />
coords = [(0, 0), (1, 1), (1, -1), (0, 1)]<br />
LineString(coords).crosses(LineString([(0, 1), (1, 0)]))</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Programming_Resources&diff=1037Programming Resources2026-07-16T05:28:20Z<p>NicolasSaunier : /* Python */</p>
<hr />
<div><br />
=General=<br />
* Getting started (Creative Coding for Beginners - Full Course! by Adam Shiffman) https://thecodingtrain.com/guides/getting-started<br />
* [[DataScienceResources|Data science resources]]<br />
<br />
=Python=<br />
* Resources and tutorials for the transport data management course CIV8760 (mostly in French) https://github.com/nsaunier/CIV8760/blob/master/Python/README.md, and simple [[PythonHowTo|how-to]] to install Python<br />
* Official Python tutorial https://docs.python.org/3/tutorial/index.html (in [https://docs.python.org/fr/3/tutorial/ French] and the tutor (step by step visualization of the program) https://pythontutor.com/<br />
* Python 1-h video tutorial https://www.youtube.com/watch?v=kqtD5dpn9C8<br />
* [http://software-carpentry.org/lessons Software Carpentry lessons], in particular https://swcarpentry.github.io/python-novice-inflammation/ and http://swcarpentry.github.io/python-novice-gapminder (lessons and videos)<br />
* [https://www.w3resource.com/python-exercises Hundreds of Python exercises]<br />
* [https://github.com/jakevdp/WhirlwindTourOfPython A Whirlwind Tour of Python]<br />
* [https://www.learnpython.org LearnPython.org]<br />
* https://github.com/mpcs-51042/materials<br />
* in French<br />
** [https://www.youtube.com/playlist?list=PLfALbWbNPl4bSWH-Pbj3iIb_TVQdr99pH liste des vidéos du cours INF1005D]<br />
** [https://openclassrooms.com/fr/courses/235344-apprenez-a-programmer-en-python Openclassrooms - Apprenez à programmer en Python]<br />
** [https://python.doctor/ Apprendre le langage de programmation python]<br />
* Online books:<br />
** [http://greenteapress.com/wp/think-python/ Think Python]<br />
** [http://www.diveinto.org/python3/ Dive into Python 3] and [http://www.diveintopython.net/ Dive into Python]<br />
** [https://python.developpez.com/cours/apprendre-python3/ Apprendre à programmer avec Python 3] (en français)<br />
** Python Programming And Numerical Methods: A Guide For Engineers And Scientists https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html<br />
** Research Software Engineering with Python https://third-bit.com/py-rse/<br />
* Older tutorials http://n.saunier.free.fr/saunier/teaching/workshop/12-09-script-ta-ville.html, [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
<br />
=R=<br />
* Introduction to R programming (in French), Vincent Goulet https://cran.r-project.org/doc/contrib/Goulet_introduction_programmation_R.pdf<br />
* Workshop on R software https://github.com/sahirbhatnagar/atelier-R-GERAD<br />
* Guide pour l’analyse de données d’enquêtes avec R (in French) https://larmarange.github.io/guide-R/<br />
<br />
=Databases=<br />
* Wikibook SQL http://en.wikibooks.org/wiki/SQL<br />
* Murrell P., Introduction to Data Technologies 2009 http://www.stat.auckland.ac.nz/~paul/ItDT/<br />
* SQL for Web Nerds http://philip.greenspun.com/sql/<br />
* Software Carpentry: Using Databases and SQL http://swcarpentry.github.io/sql-novice-survey<br />
<br />
=Version Control=<br />
* [[Mercurial|Mercurial]]<br />
* Others: Git https://glasskube.dev/guides/git/, Subversion<br />
<br />
=Other Languages=<br />
* [https://p5js.org/ p5js]: porting of processing in javascript (video lessons by Adam Shiffman at https://thecodingtrain.com)<br />
* [http://www.gnu.org/software/octave Octave]: Matlab clone<br />
* C/C++: GCC compiler, cygwing or MinGW environnements (Windows)</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Accueil&diff=1036Accueil2026-06-02T13:54:06Z<p>NicolasSaunier : /* Resources / Ressources */</p>
<hr />
<div><big>'''Bienvenue sur PolyWikiTI, le wiki de la recherche en Transports Intelligents, Actifs et Sécuritaires à Polytechnique Montréal (PolyTI)'''</big><br />
<br />
<big>'''Welcome to PolyWikiTI, the wiki about Research in Intelligent, Active and Safe Transport at Polytechnique Montréal (PolyIT)'''</big><br />
<br />
maintenu par / maintained by [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
<br />
* Professors in the Groupe Transport et Mobilité Durables (Sustainable Transport and Mobility)<br />
** [https://www.polymtl.ca/expertises/boisjoly-genevieve Geneviève Boisjoly]<br />
** [https://www.polymtl.ca/expertises/ciari-francesco Francesco Ciari]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=322 Catherine Morency]<br />
** [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=190 Martin Trépanier]<br />
** [https://www.polymtl.ca/expertises/waygood-owen Owen Waygood]<br />
* Former professors<br />
** [https://scholar.google.com/scholar?&q=karsten%20baass Karsten Baass]<br />
** [http://www.transport.polymtl.ca/titre1.htm Robert Chapleau]<br />
* [[students|Étudiants / students]]<br />
<br />
=Activities / Activités=<br />
* [[SeminaireGroupe|Séminaires du groupe PolyTI]]<br />
* [[SeminaireTransport|Séminaires Transport des professeurs et étudiants de l'École Polytechnique de Montréal]], organisés sur Teams en 2020-2021<br />
* [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
* [[ModelisationCirculation|Comité pour la modélisation (microscopique) de la circulation (CoMCI)]]<br />
<br />
=Research / Recherche=<br />
Maybe we'll use https://polyit.pubpub.org/.<br />
* [[PolyDatasets|Data]]<br />
* Research Themes<br />
** [[ResearchIdeas|Research project ideas]]<br />
** [[BikeLab|Bike laboratory to collect traffic and infrastructure data]]<br />
** [[StreetEvaluationFramework|Framework to evaluate the functions and impacts of streets]]<br />
** [[RoadSafety|Road Safety]] and [[Surrogate_Measures_of_Safety|Surrogate Measures of Safety]]<br />
** [[VideoTracking|Video-based transportation data collection]]<br />
** [[TrajectoryManagement|Trajectory Management and Analysis]]<br />
** [[TrackingOptimization|Optimization of tracking performance]]<br />
** [[VideoAnnotation|Annotation of video data, user trajectories and characteristics]]<br />
** [[Trajectory_Learning|Methods to learn (cluster) trajectories (motion patterns)]]<br />
** [[SimulationCalibration|Traffic micro-simulation calibration and validation]]<br />
* [[OldProjects|Past projects]]<br />
<br />
=Resources / Ressources=<br />
* Études supérieures et recherche à Polytechnique<br />
** Faire un [[PolyPlanEtude|plan d'étude au cycle supérieur en transport]], [[StageMaitrise|un rapport de stage en maîtrise professionnelle]]<br />
** [[Getting_Started_in_Graduate_Programs|Commencer une maîtrise ou un doctorat / Getting started in a master's or PhD]]<br />
** [[GestionEquipe|Gestion du groupe de recherche / Research group management]]<br />
** [[Integrité en recherche|Intégrité en recherche / Research integrity]]<br />
* Ressources pour les cours de [[Ressources_pour_les_cours_de_circulation|circulation et transport]]<br />
* Data Analysis / Analyse de données<br />
** [[Programming_Resources|Programming resources]]<br />
** [[Data_Science_Resources|Data Science resources]]<br />
** [[SafetyResources|(Road) Safety resources]]<br />
** [[MapTrafficResources|Map and Traffic Processing (Simulation) Resources]]<br />
** [[PythonHowTo|Python how-to]]<br />
** [[Computing_Tools_for_Research|Computing tools for research (Liste d'outils informatiques pour la recherche)]]<br />
** [[OpenScience]]<br />
** [[Computing,_Data_Management_and_Linux_Resources|Computing, data management and Linux resources]]<br />
** [[ProgrammingStyle|Programming naming convention and other coding styles]]<br />
* [[Conseils_pour_faire_de_la_recherche|Conseils pour faire de la recherche]]<br />
* [[TripPlanning|Tools for trip planning]]<br />
* [[Public_Transportation_Datasets|Public transportation datasets]]<br />
* [[Equipment|Equipment]] for data collection<br />
** [[Rules_for_research_activities_during_the_COVID-19_pandemic|Rules for data collection during the COVID-19 pandemic]]<br />
** [[VideoDataCollectionHowTo|How to collect video data]]<br />
** [[Survol_des_équipements_vidéos_pour_collection_de_données|Overview of video data collection gear]]<br />
* Rules for room/règles pour la salle [[B344|B344]]<br />
* [[BoursesTransport|Liste des bourses pour les étudiants en transport]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Computing,_Data_Management_and_Linux_Resources&diff=1034Computing, Data Management and Linux Resources2026-06-02T13:53:09Z<p>NicolasSaunier : NicolasSaunier a déplacé la page Linux Resources vers Computing, Data Management and Linux Resources</p>
<hr />
<div>== Connecting to Polytechnique desktops ==<br />
<br />
* the first way to connect is though ssh, which, to put it simply, gives you the same access as to a terminal locally (see below for terminal resources)<br />
** ask the responsible person for the computer ip address and ssh port. You can then connect through <code>ssh -p [port] [user]@[ip_address]</code> using your local user name and password.<br />
* one can use a proprietary tool like Teamviewer for remote graphical access, but the free version has quirks and sometimes won't work anymore. In that case, one can use a 100% free solution relying on VNC (included on MacOS). <br />
** make sure a VNC server (we'll use [https://wiki.archlinux.org/title/X11vnc x11vnc] since it is lightweight and full featured) is installed on the target computer<br />
** create a tunnel to the computer and start by typing <br />
ssh -t -L 5900:localhost:5900 username@ipaddress -p port 'x11vnc -localhost -display :0<br />
<br />
where the username, ipaddress and port are replaced by the appropriate values (removing the last part in single quotes creates the tunnel, and one has to start x11vnc after connection)<br />
:* if a user is connected locally on the computer, you must connect as the same user through ssh.<br />
:* it may be necessary to type <code>xhost + local:</code> before starting x11vnc.<br />
:* connect to the computer using a VNC client, eg vinagre on Linux or [https://www.realvnc.com/en/connect/download/vnc/ realVNC] (the integrated client in MacOS insists on having a password, but we are not using one here): the address is simply 'localhost' since the tunnel does as if the remote computer is local on the specific VNC port (5900)<br />
* access to files can be done through secure ftp (sftp) using a client like [https://filezilla-project.org/ filezilla] or on the command line using scp with the same information as for ssh.<br />
<br />
== Other ==<br />
* Taming the terminal https://ttt.bartificer.net/book.html</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Linux_Resources&diff=1035Linux Resources2026-06-02T13:53:09Z<p>NicolasSaunier : NicolasSaunier a déplacé la page Linux Resources vers Computing, Data Management and Linux Resources</p>
<hr />
<div>#REDIRECTION [[Computing, Data Management and Linux Resources]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1033Surrogate Measures of Safety2026-05-22T18:44:49Z<p>NicolasSaunier : </p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* [https://en.wikipedia.org/wiki/Vision_Zero Vision zero] and its operationalization (safe systems)<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/DE/Themen/Sicherheit/Daten/InDeV/InDev-Projekt.html InDeV project], in particular the [https://www.bast.de/DE/Themen/Sicherheit/Daten/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/DE/Themen/Sicherheit/Daten/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [https://www.ictct.net/smos/ subcommittee]. There was a [https://sites.google.com/site/surrogatesafety/ TRB subcommittee] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* Yastremska-Kravchenko, Oksana Microscopic behaviour analysis using video recordings : A perspective on vulnerable road users https://lup.lub.lu.se/search/publication/afdb397a-ba27-4685-8c57-1334046e2d5b<br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* Journals: [https://tsr.international Traffic Safety Research]<br />
* Vision zero and safe systems: https://carsp.ca/en/news-and-resources/road-safety-information/vision-zero-and-the-safe-system-approach/ https://www.tac-atc.ca/wp-content/uploads/prm-vzss-e.pdf<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1032SeminaireGroupe2026-05-08T17:13:15Z<p>NicolasSaunier : </p>
<hr />
<div>* 2026<br />
** 08/05/26: A Maneuver Vocabulary for Vehicle Trajectories at Urban Intersections, Munkh-Erdene Munkhbat, intern from Seoul National University<br />
** 17/04.26: How much data, how often? Scaling Laws and Temporal Decay in Traffic Forecasting, Nicolas Rodwell Bent<br />
** 03/03/26: final research presentation by Wang Luo, visiting PhD student from Central South University<br />
** 16/01/26: Traffic Modelling Visualization techniques, Nicolas Rodwell Bent<br />
* 2025<br />
** 21/11/2025: group meeting and discussion of data organization (see [[OpenScience]])<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Road_Safety&diff=1030Road Safety2026-04-20T17:02:11Z<p>NicolasSaunier : NicolasSaunier a déplacé la page RoadSafety vers Road Safety</p>
<hr />
<div>Road safety is characterized, based on the vision zero, as the absence of fatalities or severe injuries when people move on roads (including sidewalks). <br />
<br />
Data is needed to measure safety itself, e.g., based on crashes, and on factors associated with crashes, which can be related to users, vehicles and infrastructure, as well as some elements of the larger environment such as the weather. <br />
<br />
Safety can also be measured proactively using [[Surrogate_Measures_of_Safety|surrogate measures of safety]].<br />
<br />
== Data Sources==<br />
<br />
Public or semi-public data sources, see the [https://bellingcat.gitbook.io/toolkit open source intelligence community].<br />
<br />
Data for safety diagnosis often needs to be extracted from the raw data, e.g., images, through various methods including recent advances in AI (computer vision) methods.<br />
<br />
TODO: upload sketch of data sources. <br />
<br />
== Analysis ==<br />
<br />
Statistical and AI methods.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=RoadSafety&diff=1031RoadSafety2026-04-20T17:02:11Z<p>NicolasSaunier : NicolasSaunier a déplacé la page RoadSafety vers Road Safety</p>
<hr />
<div>#REDIRECTION [[Road Safety]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Road_Safety&diff=1029Road Safety2026-04-20T17:01:58Z<p>NicolasSaunier : Page créée avec « Road safety is characterized, based on the vision zero, as the absence of fatalities or severe injuries when people move on roads (including sidewalks). Data is needed t... »</p>
<hr />
<div>Road safety is characterized, based on the vision zero, as the absence of fatalities or severe injuries when people move on roads (including sidewalks). <br />
<br />
Data is needed to measure safety itself, e.g., based on crashes, and on factors associated with crashes, which can be related to users, vehicles and infrastructure, as well as some elements of the larger environment such as the weather. <br />
<br />
Safety can also be measured proactively using [[Surrogate_Measures_of_Safety|surrogate measures of safety]].<br />
<br />
== Data Sources==<br />
<br />
Public or semi-public data sources, see the [https://bellingcat.gitbook.io/toolkit open source intelligence community].<br />
<br />
Data for safety diagnosis often needs to be extracted from the raw data, e.g., images, through various methods including recent advances in AI (computer vision) methods.<br />
<br />
TODO: upload sketch of data sources. <br />
<br />
== Analysis ==<br />
<br />
Statistical and AI methods.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Accueil&diff=1028Accueil2026-04-20T16:53:46Z<p>NicolasSaunier : /* Research / Recherche */</p>
<hr />
<div><big>'''Bienvenue sur PolyWikiTI, le wiki de la recherche en Transports Intelligents, Actifs et Sécuritaires à Polytechnique Montréal (PolyTI)'''</big><br />
<br />
<big>'''Welcome to PolyWikiTI, the wiki about Research in Intelligent, Active and Safe Transport at Polytechnique Montréal (PolyIT)'''</big><br />
<br />
maintenu par / maintained by [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
<br />
* Professors in the Groupe Transport et Mobilité Durables (Sustainable Transport and Mobility)<br />
** [https://www.polymtl.ca/expertises/boisjoly-genevieve Geneviève Boisjoly]<br />
** [https://www.polymtl.ca/expertises/ciari-francesco Francesco Ciari]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=322 Catherine Morency]<br />
** [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=190 Martin Trépanier]<br />
** [https://www.polymtl.ca/expertises/waygood-owen Owen Waygood]<br />
* Former professors<br />
** [https://scholar.google.com/scholar?&q=karsten%20baass Karsten Baass]<br />
** [http://www.transport.polymtl.ca/titre1.htm Robert Chapleau]<br />
* [[students|Étudiants / students]]<br />
<br />
=Activities / Activités=<br />
* [[SeminaireGroupe|Séminaires du groupe PolyTI]]<br />
* [[SeminaireTransport|Séminaires Transport des professeurs et étudiants de l'École Polytechnique de Montréal]], organisés sur Teams en 2020-2021<br />
* [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
* [[ModelisationCirculation|Comité pour la modélisation (microscopique) de la circulation (CoMCI)]]<br />
<br />
=Research / Recherche=<br />
Maybe we'll use https://polyit.pubpub.org/.<br />
* [[PolyDatasets|Data]]<br />
* Research Themes<br />
** [[ResearchIdeas|Research project ideas]]<br />
** [[BikeLab|Bike laboratory to collect traffic and infrastructure data]]<br />
** [[StreetEvaluationFramework|Framework to evaluate the functions and impacts of streets]]<br />
** [[RoadSafety|Road Safety]] and [[Surrogate_Measures_of_Safety|Surrogate Measures of Safety]]<br />
** [[VideoTracking|Video-based transportation data collection]]<br />
** [[TrajectoryManagement|Trajectory Management and Analysis]]<br />
** [[TrackingOptimization|Optimization of tracking performance]]<br />
** [[VideoAnnotation|Annotation of video data, user trajectories and characteristics]]<br />
** [[Trajectory_Learning|Methods to learn (cluster) trajectories (motion patterns)]]<br />
** [[SimulationCalibration|Traffic micro-simulation calibration and validation]]<br />
* [[OldProjects|Past projects]]<br />
<br />
=Resources / Ressources=<br />
* Études supérieures et recherche à Polytechnique<br />
** Faire un [[PolyPlanEtude|plan d'étude au cycle supérieur en transport]], [[StageMaitrise|un rapport de stage en maîtrise professionnelle]]<br />
** [[Getting_Started_in_Graduate_Programs|Commencer une maîtrise ou un doctorat / Getting started in a master's or PhD]]<br />
** [[GestionEquipe|Gestion du groupe de recherche / Research group management]]<br />
** [[Integrité en recherche|Intégrité en recherche / Research integrity]]<br />
* Ressources pour les cours de [[Ressources_pour_les_cours_de_circulation|circulation et transport]]<br />
* Data Analysis / Analyse de données<br />
** [[Programming_Resources|Programming resources]]<br />
** [[Data_Science_Resources|Data Science resources]]<br />
** [[SafetyResources|(Road) Safety resources]]<br />
** [[MapTrafficResources|Map and Traffic Processing (Simulation) Resources]]<br />
** [[PythonHowTo|Python how-to]]<br />
** [[Computing_Tools_for_Research|Computing tools for research (Liste d'outils informatiques pour la recherche)]]<br />
** [[OpenScience]]<br />
** [[Linux_Resources|Linux resources]]<br />
** [[ProgrammingStyle|Programming naming convention and other coding styles]]<br />
* [[Conseils_pour_faire_de_la_recherche|Conseils pour faire de la recherche]]<br />
* [[TripPlanning|Tools for trip planning]]<br />
* [[Public_Transportation_Datasets|Public transportation datasets]]<br />
* [[Equipment|Equipment]] for data collection<br />
** [[Rules_for_research_activities_during_the_COVID-19_pandemic|Rules for data collection during the COVID-19 pandemic]]<br />
** [[VideoDataCollectionHowTo|How to collect video data]]<br />
** [[Survol_des_équipements_vidéos_pour_collection_de_données|Overview of video data collection gear]]<br />
* Rules for room/règles pour la salle [[B344|B344]]<br />
* [[BoursesTransport|Liste des bourses pour les étudiants en transport]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1027SeminaireGroupe2026-04-17T17:16:13Z<p>NicolasSaunier : </p>
<hr />
<div>* 2026<br />
** 17/04.26: Nicolas Rodwell Bent How much data, how often? Scaling Laws and Temporal Decay in Traffic Forecasting<br />
** 03/03/26: research presentation by Wang Luo, visiting PhD student from Central South University<br />
** 16/01/26: Nicolas Rodwell Bent presented on Traffic Modelling Visualization techniques<br />
* 2025<br />
** 21/11/2025: group meeting and discussion of data organization (see [[OpenScience]])<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1026Surrogate Measures of Safety2026-03-10T20:32:41Z<p>NicolasSaunier : /* Resources */</p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* [https://en.wikipedia.org/wiki/Vision_Zero Vision zero] and its operationalization (safe systems)<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [https://www.ictct.net/smos/ subcommittee]. There was a [https://sites.google.com/site/surrogatesafety/ TRB subcommittee] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* Yastremska-Kravchenko, Oksana Microscopic behaviour analysis using video recordings : A perspective on vulnerable road users https://lup.lub.lu.se/search/publication/afdb397a-ba27-4685-8c57-1334046e2d5b<br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* Journals: [https://tsr.international Traffic Safety Research]<br />
* Vision zero and safe systems: https://carsp.ca/en/news-and-resources/road-safety-information/vision-zero-and-the-safe-system-approach/ https://www.tac-atc.ca/wp-content/uploads/prm-vzss-e.pdf<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=GestionEquipe&diff=1025GestionEquipe2026-03-03T15:28:28Z<p>NicolasSaunier : </p>
<hr />
<div>==Formation éthique / Ethics training==<br />
Les étudiants doivent connaître les règles d'intégrité en recherche. <br />
<br />
Students must know ethics rules. <br />
<br />
Voir [[Integrité en recherche]] / See [[Integrité_en_recherche|research integrity]]<br />
<br />
==Santé et sécurité au travail (SST)==<br />
Faire une évaluation avant une sortie sur le terrain si l'activité implique des risques plus importants que des déplacements personnels. <br />
<br />
==Bonnes pratiques==<br />
* Assurer la transparence et l’équité des pratiques<br />
* Faire connaitre les opportunités et ressources sur le campus<br />
* Soutenir l’intégration des nouvelles recrues<br />
* Encourager les échanges et la convivialité<br />
* Renforcer la cohésion et l’esprit d’équipe<br />
* Réduire la pression de performance et ses effets contreproductifs<br />
* Favoriser la santé globale, le bien-être et l’équilibre de vie<br />
* Aménager des espaces agréables et confortables<br />
* Rester à l’écoute des besoins<br />
<br />
Tiré de [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Labos-inclusifs_Recommandations.pdf?guest=true Document synthèse des bonnes pratiques]<br />
<br />
==Ressources==<br />
* https://www.polymtl.ca/edi/labos-inclusifs<br />
* [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-sante-mentale.pdf?guest=true Continuum en santé mentale] [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-mental-health.pdf?guest=true Mental Health Continuum]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Integrit%C3%A9_en_recherche&diff=1024Integrité en recherche2026-03-03T15:27:15Z<p>NicolasSaunier : </p>
<hr />
<div>==Éthique / Ethics==<br />
<br />
Les étudiants doivent connaître les règles d'intégrité en recherche.<br />
<br />
L'approbation éthique doit être obtenue (entièrement, et non conditionnellement) auprès du comité d’éthique de la recherche de Polytechnique pour toutes les données personnelles, qu’elles soient collectées par nous, comme les données vidéo, ou partagées par un tiers.<br />
<br />
Students must know ethics rules. <br />
<br />
Ethics approval must be obtained (completely, not conditionally) from the research ethics committee of Polytechnique for any personal data, whether collected by us like video data or shared by a third party. <br />
<br />
https://www.polymtl.ca/renseignements-generaux/documents-officiels/6-recherche-et-innovation<br />
<br />
==Academic integrity==<br />
<br />
Plagiarism and academic fraud are obviously forbidden. <br />
<br />
This includes using AI to generate text or images for you without disclosing it. You must also verify every output, in particular text and code, for errors. You are responsible in the end of the submitted document. This includes easier to verify telltales like citations that do not exist. You will be reported for academic misconduct if that happens. It should be obvious that you cite only work you've read. <br />
<br />
Scientific rigor takes time. There is basically no shortcut.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Integrit%C3%A9_en_recherche&diff=1023Integrité en recherche2026-03-03T15:24:52Z<p>NicolasSaunier : Page créée avec « ==Ethics== Ethics approval must be obtained (completely, not conditionally) from the ethics committee of Polytechnique for any personal data, whether collected by us lik... »</p>
<hr />
<div>==Ethics==<br />
<br />
<br />
Ethics approval must be obtained (completely, not conditionally) from the ethics committee of Polytechnique for any personal data, whether collected by us like video data or shared by a third party. <br />
<br />
https://www.polymtl.ca/renseignements-generaux/documents-officiels/6-recherche-et-innovation<br />
<br />
==Academic integrity==<br />
<br />
Plagiarism and academic fraud are obviously forbidden. <br />
<br />
This includes using AI to generate text or images for you without disclosing it. You must also verify every output, in particular text and code, for errors. You are responsible in the end of the submitted document. This includes easier to verify telltales like citations that do not exist. You will be reported for academic misconduct if that happens. It should be obvious that you cite only work you've read. <br />
<br />
Scientific rigor takes time. There is basically no shortcut.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Accueil&diff=1022Accueil2026-03-03T15:18:40Z<p>NicolasSaunier : /* Resources / Ressources */</p>
<hr />
<div><big>'''Bienvenue sur PolyWikiTI, le wiki de la recherche en Transports Intelligents, Actifs et Sécuritaires à Polytechnique Montréal (PolyTI)'''</big><br />
<br />
<big>'''Welcome to PolyWikiTI, the wiki about Research in Intelligent, Active and Safe Transport at Polytechnique Montréal (PolyIT)'''</big><br />
<br />
maintenu par / maintained by [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
<br />
* Professors in the Groupe Transport et Mobilité Durables (Sustainable Transport and Mobility)<br />
** [https://www.polymtl.ca/expertises/boisjoly-genevieve Geneviève Boisjoly]<br />
** [https://www.polymtl.ca/expertises/ciari-francesco Francesco Ciari]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=322 Catherine Morency]<br />
** [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=190 Martin Trépanier]<br />
** [https://www.polymtl.ca/expertises/waygood-owen Owen Waygood]<br />
* Former professors<br />
** [https://scholar.google.com/scholar?&q=karsten%20baass Karsten Baass]<br />
** [http://www.transport.polymtl.ca/titre1.htm Robert Chapleau]<br />
* [[students|Étudiants / students]]<br />
<br />
=Activities / Activités=<br />
* [[SeminaireGroupe|Séminaires du groupe PolyTI]]<br />
* [[SeminaireTransport|Séminaires Transport des professeurs et étudiants de l'École Polytechnique de Montréal]], organisés sur Teams en 2020-2021<br />
* [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
* [[ModelisationCirculation|Comité pour la modélisation (microscopique) de la circulation (CoMCI)]]<br />
<br />
=Research / Recherche=<br />
Maybe we'll use https://polyit.pubpub.org/.<br />
* [[PolyDatasets|Data]]<br />
* Research Themes<br />
** [[ResearchIdeas|Research project ideas]]<br />
** [[BikeLab|Bike laboratory to collect traffic and infrastructure data]]<br />
** [[StreetEvaluationFramework|Framework to evaluate the functions and impacts of streets]]<br />
** [[Surrogate_Measures_of_Safety|Surrogate Measures of Safety]]<br />
** [[VideoTracking|Video-based transportation data collection]]<br />
** [[TrajectoryManagement|Trajectory Management and Analysis]]<br />
** [[TrackingOptimization|Optimization of tracking performance]]<br />
** [[VideoAnnotation|Annotation of video data, user trajectories and characteristics]]<br />
** [[Trajectory_Learning|Methods to learn (cluster) trajectories (motion patterns)]]<br />
** [[SimulationCalibration|Traffic micro-simulation calibration and validation]]<br />
* [[OldProjects|Past projects]]<br />
<br />
=Resources / Ressources=<br />
* Études supérieures et recherche à Polytechnique<br />
** Faire un [[PolyPlanEtude|plan d'étude au cycle supérieur en transport]], [[StageMaitrise|un rapport de stage en maîtrise professionnelle]]<br />
** [[Getting_Started_in_Graduate_Programs|Commencer une maîtrise ou un doctorat / Getting started in a master's or PhD]]<br />
** [[GestionEquipe|Gestion du groupe de recherche / Research group management]]<br />
** [[Integrité en recherche|Intégrité en recherche / Research integrity]]<br />
* Ressources pour les cours de [[Ressources_pour_les_cours_de_circulation|circulation et transport]]<br />
* Data Analysis / Analyse de données<br />
** [[Programming_Resources|Programming resources]]<br />
** [[Data_Science_Resources|Data Science resources]]<br />
** [[SafetyResources|(Road) Safety resources]]<br />
** [[MapTrafficResources|Map and Traffic Processing (Simulation) Resources]]<br />
** [[PythonHowTo|Python how-to]]<br />
** [[Computing_Tools_for_Research|Computing tools for research (Liste d'outils informatiques pour la recherche)]]<br />
** [[OpenScience]]<br />
** [[Linux_Resources|Linux resources]]<br />
** [[ProgrammingStyle|Programming naming convention and other coding styles]]<br />
* [[Conseils_pour_faire_de_la_recherche|Conseils pour faire de la recherche]]<br />
* [[TripPlanning|Tools for trip planning]]<br />
* [[Public_Transportation_Datasets|Public transportation datasets]]<br />
* [[Equipment|Equipment]] for data collection<br />
** [[Rules_for_research_activities_during_the_COVID-19_pandemic|Rules for data collection during the COVID-19 pandemic]]<br />
** [[VideoDataCollectionHowTo|How to collect video data]]<br />
** [[Survol_des_équipements_vidéos_pour_collection_de_données|Overview of video data collection gear]]<br />
* Rules for room/règles pour la salle [[B344|B344]]<br />
* [[BoursesTransport|Liste des bourses pour les étudiants en transport]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1021Surrogate Measures of Safety2026-02-25T03:56:41Z<p>NicolasSaunier : </p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* [https://en.wikipedia.org/wiki/Vision_Zero Vision zero] and its operationalization (safe systems)<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [https://www.ictct.net/smos/ subcommittee]. There was a [https://sites.google.com/site/surrogatesafety/ TRB subcommittee] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* Journals: [https://tsr.international Traffic Safety Research]<br />
* Vision zero and safe systems: https://carsp.ca/en/news-and-resources/road-safety-information/vision-zero-and-the-safe-system-approach/ https://www.tac-atc.ca/wp-content/uploads/prm-vzss-e.pdf<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1020Surrogate Measures of Safety2026-02-25T03:52:46Z<p>NicolasSaunier : /* Projects */</p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* [https://en.wikipedia.org/wiki/Vision_Zero|Vision zero] and its operationalization (safe systems)<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [[https://www.ictct.net/smos/|subcommittee]]. There was a [[https://sites.google.com/site/surrogatesafety/|TRB subcommittee]] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* [https://tsr.international/|Traffic Safety Research]<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1019Surrogate Measures of Safety2026-02-25T03:51:20Z<p>NicolasSaunier : </p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* Vision zero and its operationalization<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [[https://www.ictct.net/smos/|subcommittee]]. There was a [[https://sites.google.com/site/surrogatesafety/|TRB subcommittee]] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* [https://tsr.international/|Traffic Safety Research]<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1018Surrogate Measures of Safety2026-02-25T03:51:06Z<p>NicolasSaunier : </p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* Vision zero and its operationalization<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [[https://www.ictct.net/smos/|subcommittee]]. There was a [[https://sites.google.com/site/surrogatesafety/|TRB subcommittee]] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* [[https://tsr.international/|Traffic Safety Research]]<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf.</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1017Surrogate Measures of Safety2026-02-24T20:13:40Z<p>NicolasSaunier : /* Projects */</p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
* Vision zero and its operationalization<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [[https://www.ictct.net/smos/|subcommittee]]. There was a [[https://sites.google.com/site/surrogatesafety/|TRB subcommittee]] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Surrogate_Measures_of_Safety&diff=1016Surrogate Measures of Safety2026-02-24T20:13:16Z<p>NicolasSaunier : </p>
<hr />
<div>==Projects==<br />
* [[Surrogate_Measures_of_Safety/Interaction_Similarity|Similarity of interactions]]<br />
* [[Surrogate_Measures_of_Safety/Minimal_Road_User_Interaction_Simulation|Minimal road user interaction simulation]]<br />
<br />
==Resources==<br />
Good starting points are the reports from the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Home/home_node.html InDeV project], in particular the [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Documents/pdf/2-1-4.pdf?__blob=publicationFile&v=1 review of current study methods for VRU safety (Appendix 6)] and [https://www.bast.de/EN/Traffic_Safety/Subjects/InDeV/Handbook/Handbook_node.html handbook for safety of vulnerable road users]. <br />
<br />
A natural home for everything related to SMoS is the International Cooperation on Theories and Concepts in Traffic safety (ICTCT), with its [[https://www.ictct.net/smos/|subcommittee]]. There was a [[https://sites.google.com/site/surrogatesafety/|TRB subcommittee]] for at least two decades, but it last met in 2025. <br />
<br />
Another are the following papers:<br />
* Andrew Tarko: book https://www.elsevier.com/books/measuring-road-safety-with-surrogate-events/tarko/978-0-12-810504-7 and chapter https://www.emerald.com/insight/content/doi/10.1108/S2044-994120180000011019/full/html?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Transport_and_Sustainability_TrendMD_0<br />
* N. Saunier and A. Laureshyn. Surrogate Measures of Safety, Encyclopedia of Transportation, 2:662-667, Elsevier, 2021 http://doi.org/10.1016/B978-0-08-102671-7.10197-6<br />
<br />
The following PhD theses are also very good starting points to understand the topic, in particular the work of Svensson which bridges the traditional traffic conflict techniques and new, more disaggregated, approaches. <br />
* St-Aubin, P. Driver Behaviour and Road Safety Analysis Using Computer Vision and Applications in Roundabout Safety, 2016 https://publications.polymtl.ca/2272/<br />
* Mohamed, M. G. Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors, 2015 Polytechnique Montréal https://publications.polymtl.ca/1784/<br />
* Bagadadi, O. The development of methods for detection and assessment of safety critical events in car driving, 2012 http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A674143&dswid=2561 ('''warnings''' about the unclear vocabulary and limited sample sizes)<br />
* Laureshyn, A. Application of automated video analysis to road user behaviour Lund University, 2010 https://portal.research.lu.se/en/publications/application-of-automated-video-analysis-to-road-user-behaviour (Paper 1 on p 93 (unpublished as far as I know) provides a very exhaustive list of safety indicators)<br />
* Svensson, A. A Method for Analyzing the Traffic Process in a Safety Perspective University of Lund, 1998. https://portal.research.lu.se/en/publications/a-method-for-analysing-the-traffic-process-in-a-safety-perspectiv<br />
* Ismail, K. Application of computer vision techniques for automated road safety analysis and traffic data collection University of British Columbia, 2010. http://hdl.handle.net/2429/29546<br />
* Archer, J. Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and their Application in Micro-simulation Modelling Royal Institute of Technology, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-143<br />
* Cunto, F. Assessing Safety Performance of Transportation Systems using Microscopic Simulation University of Waterloo, 2008. http://hdl.handle.net/10012/4111<br />
<br />
Other ressources:<br />
* white paper by the TRB subcommittee on surrogate measures of safety: http://n.saunier.free.fr/saunier/stock/tarko09surrogate.pdf<br />
* white paper of the [https://www.sae.org/servlets/works/committeeHome.do?comtID=TEVSMEASURES SAE committee on surrogate measures of safety]: http://papers.sae.org/wp-0005/<br />
* the software tool to evaluate traffic micro-simulation in terms of safety: <br />
** new SSAM https://www.itsforge.net/index.php/community/explore-applications#/35/143<br />
** old version of SSAM from FHWA (reports http://www.tfhrc.gov/safety/pubs/03050/index.htm http://www.fhwa.dot.gov/publications/research/safety/08051/)<br />
Old documents:<br />
* FHWA's manual: Traffic Conflict Techniques for Safety and Operations: Observer's Manual http://www.fhwa.dot.gov/publications/research/safety/88027/index.cfm<br />
<br />
I wrote lecture notes for a course given at McGill and online in Canada's national transportation course: https://dl.dropbox.com/u/179169/notes-surrogates.pdf</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1015SeminaireGroupe2026-02-24T20:05:28Z<p>NicolasSaunier : </p>
<hr />
<div>* 2026<br />
** 03/03/26: research presentation by Wang Luo, visiting PhD student from Central South University<br />
** 16/01/26: Nicolas Rodwell Bent presented on Traffic Modelling Visualization techniques<br />
* 2025<br />
** 21/11/2025: group meeting and discussion of data organization (see [[OpenScience]])<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Accueil&diff=1014Accueil2026-02-24T19:59:27Z<p>NicolasSaunier : /* Resources / Ressources */</p>
<hr />
<div><big>'''Bienvenue sur PolyWikiTI, le wiki de la recherche en Transports Intelligents, Actifs et Sécuritaires à Polytechnique Montréal (PolyTI)'''</big><br />
<br />
<big>'''Welcome to PolyWikiTI, the wiki about Research in Intelligent, Active and Safe Transport at Polytechnique Montréal (PolyIT)'''</big><br />
<br />
maintenu par / maintained by [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
<br />
* Professors in the Groupe Transport et Mobilité Durables (Sustainable Transport and Mobility)<br />
** [https://www.polymtl.ca/expertises/boisjoly-genevieve Geneviève Boisjoly]<br />
** [https://www.polymtl.ca/expertises/ciari-francesco Francesco Ciari]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=322 Catherine Morency]<br />
** [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=190 Martin Trépanier]<br />
** [https://www.polymtl.ca/expertises/waygood-owen Owen Waygood]<br />
* Former professors<br />
** [https://scholar.google.com/scholar?&q=karsten%20baass Karsten Baass]<br />
** [http://www.transport.polymtl.ca/titre1.htm Robert Chapleau]<br />
* [[students|Étudiants / students]]<br />
<br />
=Activities / Activités=<br />
* [[SeminaireGroupe|Séminaires du groupe PolyTI]]<br />
* [[SeminaireTransport|Séminaires Transport des professeurs et étudiants de l'École Polytechnique de Montréal]], organisés sur Teams en 2020-2021<br />
* [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
* [[ModelisationCirculation|Comité pour la modélisation (microscopique) de la circulation (CoMCI)]]<br />
<br />
=Research / Recherche=<br />
Maybe we'll use https://polyit.pubpub.org/.<br />
* [[PolyDatasets|Data]]<br />
* Research Themes<br />
** [[ResearchIdeas|Research project ideas]]<br />
** [[BikeLab|Bike laboratory to collect traffic and infrastructure data]]<br />
** [[StreetEvaluationFramework|Framework to evaluate the functions and impacts of streets]]<br />
** [[Surrogate_Measures_of_Safety|Surrogate Measures of Safety]]<br />
** [[VideoTracking|Video-based transportation data collection]]<br />
** [[TrajectoryManagement|Trajectory Management and Analysis]]<br />
** [[TrackingOptimization|Optimization of tracking performance]]<br />
** [[VideoAnnotation|Annotation of video data, user trajectories and characteristics]]<br />
** [[Trajectory_Learning|Methods to learn (cluster) trajectories (motion patterns)]]<br />
** [[SimulationCalibration|Traffic micro-simulation calibration and validation]]<br />
* [[OldProjects|Past projects]]<br />
<br />
=Resources / Ressources=<br />
* Études supérieures et recherche à Polytechnique<br />
** Faire un [[PolyPlanEtude|plan d'étude au cycle supérieur en transport]], [[StageMaitrise|un rapport de stage en maîtrise professionnelle]]<br />
** [[Getting_Started_in_Graduate_Programs|Getting started in a master's or PhD]]<br />
** [[GestionEquipe|Organisation du groupe de recherche]]<br />
* Ressources pour les cours de [[Ressources_pour_les_cours_de_circulation|circulation et transport]]<br />
* Data Analysis / Analyse de données<br />
** [[Programming_Resources|Programming resources]]<br />
** [[Data_Science_Resources|Data Science resources]]<br />
** [[SafetyResources|(Road) Safety resources]]<br />
** [[MapTrafficResources|Map and Traffic Processing (Simulation) Resources]]<br />
** [[PythonHowTo|Python how-to]]<br />
** [[Computing_Tools_for_Research|Computing tools for research (Liste d'outils informatiques pour la recherche)]]<br />
** [[OpenScience]]<br />
** [[Linux_Resources|Linux resources]]<br />
** [[ProgrammingStyle|Programming naming convention and other coding styles]]<br />
* [[Conseils_pour_faire_de_la_recherche|Conseils pour faire de la recherche]]<br />
* [[TripPlanning|Tools for trip planning]]<br />
* [[Public_Transportation_Datasets|Public transportation datasets]]<br />
* [[Equipment|Equipment]] for data collection<br />
** [[Rules_for_research_activities_during_the_COVID-19_pandemic|Rules for data collection during the COVID-19 pandemic]]<br />
** [[VideoDataCollectionHowTo|How to collect video data]]<br />
** [[Survol_des_équipements_vidéos_pour_collection_de_données|Overview of video data collection gear]]<br />
* Rules for room/règles pour la salle [[B344|B344]]<br />
* [[BoursesTransport|Liste des bourses pour les étudiants en transport]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Getting_Started_in_Graduate_Programs&diff=1012Getting Started in Graduate Programs2026-02-24T19:58:43Z<p>NicolasSaunier : NicolasSaunier a déplacé la page GettingStartedPhD vers Getting Started in Graduate Programs</p>
<hr />
<div>==Generic==<br />
<br />
* Study plan<br />
* CAP workshops<br />
* Literature review (possible relevant use of LLM tools for search and synthesis)<br />
* [[OpenScience#Research_Data_Management|Data management plan]]<br />
** identify reproducible research<br />
** build benchmark to demonstrate the contributions<br />
<br />
== PhD specific==<br />
<br />
* Comprehensive exam: to be passed by the 4th semester, with automatic extension to 5th<br />
** written part<br />
** research proposal</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=GettingStartedPhD&diff=1013GettingStartedPhD2026-02-24T19:58:43Z<p>NicolasSaunier : NicolasSaunier a déplacé la page GettingStartedPhD vers Getting Started in Graduate Programs</p>
<hr />
<div>#REDIRECTION [[Getting Started in Graduate Programs]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Getting_Started_in_Graduate_Programs&diff=1011Getting Started in Graduate Programs2026-02-24T19:58:23Z<p>NicolasSaunier : Page créée avec « ==Generic== * Study plan * CAP workshops * Literature review (possible relevant use of LLM tools for search and synthesis) * OpenScience#Research_Data_Management|Data m... »</p>
<hr />
<div>==Generic==<br />
<br />
* Study plan<br />
* CAP workshops<br />
* Literature review (possible relevant use of LLM tools for search and synthesis)<br />
* [[OpenScience#Research_Data_Management|Data management plan]]<br />
** identify reproducible research<br />
** build benchmark to demonstrate the contributions<br />
<br />
== PhD specific==<br />
<br />
* Comprehensive exam: to be passed by the 4th semester, with automatic extension to 5th<br />
** written part<br />
** research proposal</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Accueil&diff=1010Accueil2026-02-24T19:49:56Z<p>NicolasSaunier : /* Resources / Ressources */</p>
<hr />
<div><big>'''Bienvenue sur PolyWikiTI, le wiki de la recherche en Transports Intelligents, Actifs et Sécuritaires à Polytechnique Montréal (PolyTI)'''</big><br />
<br />
<big>'''Welcome to PolyWikiTI, the wiki about Research in Intelligent, Active and Safe Transport at Polytechnique Montréal (PolyIT)'''</big><br />
<br />
maintenu par / maintained by [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
<br />
* Professors in the Groupe Transport et Mobilité Durables (Sustainable Transport and Mobility)<br />
** [https://www.polymtl.ca/expertises/boisjoly-genevieve Geneviève Boisjoly]<br />
** [https://www.polymtl.ca/expertises/ciari-francesco Francesco Ciari]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=322 Catherine Morency]<br />
** [http://nicolas.saunier.confins.net Nicolas Saunier]<br />
** [http://www.polymtl.ca/recherche/rc/professeurs/details.php?NoProf=190 Martin Trépanier]<br />
** [https://www.polymtl.ca/expertises/waygood-owen Owen Waygood]<br />
* Former professors<br />
** [https://scholar.google.com/scholar?&q=karsten%20baass Karsten Baass]<br />
** [http://www.transport.polymtl.ca/titre1.htm Robert Chapleau]<br />
* [[students|Étudiants / students]]<br />
<br />
=Activities / Activités=<br />
* [[SeminaireGroupe|Séminaires du groupe PolyTI]]<br />
* [[SeminaireTransport|Séminaires Transport des professeurs et étudiants de l'École Polytechnique de Montréal]], organisés sur Teams en 2020-2021<br />
* [[SeminaireInfo|Séminaire appliqué sur les outils informatiques de traitement de données (2011)]]<br />
* [[ModelisationCirculation|Comité pour la modélisation (microscopique) de la circulation (CoMCI)]]<br />
<br />
=Research / Recherche=<br />
Maybe we'll use https://polyit.pubpub.org/.<br />
* [[PolyDatasets|Data]]<br />
* Research Themes<br />
** [[ResearchIdeas|Research project ideas]]<br />
** [[BikeLab|Bike laboratory to collect traffic and infrastructure data]]<br />
** [[StreetEvaluationFramework|Framework to evaluate the functions and impacts of streets]]<br />
** [[Surrogate_Measures_of_Safety|Surrogate Measures of Safety]]<br />
** [[VideoTracking|Video-based transportation data collection]]<br />
** [[TrajectoryManagement|Trajectory Management and Analysis]]<br />
** [[TrackingOptimization|Optimization of tracking performance]]<br />
** [[VideoAnnotation|Annotation of video data, user trajectories and characteristics]]<br />
** [[Trajectory_Learning|Methods to learn (cluster) trajectories (motion patterns)]]<br />
** [[SimulationCalibration|Traffic micro-simulation calibration and validation]]<br />
* [[OldProjects|Past projects]]<br />
<br />
=Resources / Ressources=<br />
* Études supérieures et recherche à Polytechnique<br />
** Faire un [[PolyPlanEtude|plan d'étude au cycle supérieur en transport]], [[StageMaitrise|un rapport de stage en maîtrise professionnelle]]<br />
** [[GettingStartedPhD|Getting started in a master's or PhD]]<br />
** [[GestionEquipe|Organisation du groupe de recherche]]<br />
* Ressources pour les cours de [[Ressources_pour_les_cours_de_circulation|circulation et transport]]<br />
* Data Analysis / Analyse de données<br />
** [[Programming_Resources|Programming resources]]<br />
** [[Data_Science_Resources|Data Science resources]]<br />
** [[SafetyResources|(Road) Safety resources]]<br />
** [[MapTrafficResources|Map and Traffic Processing (Simulation) Resources]]<br />
** [[PythonHowTo|Python how-to]]<br />
** [[Computing_Tools_for_Research|Computing tools for research (Liste d'outils informatiques pour la recherche)]]<br />
** [[OpenScience]]<br />
** [[Linux_Resources|Linux resources]]<br />
** [[ProgrammingStyle|Programming naming convention and other coding styles]]<br />
* [[Conseils_pour_faire_de_la_recherche|Conseils pour faire de la recherche]]<br />
* [[TripPlanning|Tools for trip planning]]<br />
* [[Public_Transportation_Datasets|Public transportation datasets]]<br />
* [[Equipment|Equipment]] for data collection<br />
** [[Rules_for_research_activities_during_the_COVID-19_pandemic|Rules for data collection during the COVID-19 pandemic]]<br />
** [[VideoDataCollectionHowTo|How to collect video data]]<br />
** [[Survol_des_équipements_vidéos_pour_collection_de_données|Overview of video data collection gear]]<br />
* Rules for room/règles pour la salle [[B344|B344]]<br />
* [[BoursesTransport|Liste des bourses pour les étudiants en transport]]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=GestionEquipe&diff=1009GestionEquipe2026-01-26T14:26:30Z<p>NicolasSaunier : </p>
<hr />
<div>==Formation éthique==<br />
les étudiants doivent connaître les règles d'intégrité en recherche<br />
<br />
https://www.polymtl.ca/renseignements-generaux/documents-officiels/6-recherche-et-innovation<br />
<br />
==Santé et sécurité au travail (SST)==<br />
Faire une évaluation avant une sortie sur le terrain si l'activité implique des risques plus importants que des déplacements personnels. <br />
<br />
==Bonnes pratiques==<br />
* Assurer la transparence et l’équité des pratiques<br />
* Faire connaitre les opportunités et ressources sur le campus<br />
* Soutenir l’intégration des nouvelles recrues<br />
* Encourager les échanges et la convivialité<br />
* Renforcer la cohésion et l’esprit d’équipe<br />
* Réduire la pression de performance et ses effets contreproductifs<br />
* Favoriser la santé globale, le bien-être et l’équilibre de vie<br />
* Aménager des espaces agréables et confortables<br />
* Rester à l’écoute des besoins<br />
<br />
Tiré de [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Labos-inclusifs_Recommandations.pdf?guest=true Document synthèse des bonnes pratiques]<br />
<br />
==Ressources==<br />
* https://www.polymtl.ca/edi/labos-inclusifs<br />
* [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-sante-mentale.pdf?guest=true Continuum en santé mentale] [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-mental-health.pdf?guest=true Mental Health Continuum]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1008SeminaireGroupe2026-01-16T21:10:26Z<p>NicolasSaunier : </p>
<hr />
<div>* 2026<br />
** 16/01/26: Nicolas Rodwell Bent presented on Traffic Modelling Visualization techniques<br />
* 2025<br />
** 21/11/2025: group meeting and discussion of data organization (see [[OpenScience]])<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Public_Transportation_Datasets&diff=1007Public Transportation Datasets2026-01-02T13:22:40Z<p>NicolasSaunier : /* Traffic Data */</p>
<hr />
<div>See [[PolyDatasets|PolyIT Datasets]], generally not public for privacy reasons.<br />
<br />
Another resource: https://sites.google.com/view/transport-research-network/shared-resources/datasets<br />
<br />
==Traffic Data==<br />
* DLR Urban Traffic dataset (DLR-UT) https://elib.dlr.de/207287/<br />
* INTERnational, Adversarial and Cooperative moTION Dataset https://interaction-dataset.com/<br />
* NeurIPS 2022 Traffic4cast competition https://github.com/iarai/NeurIPS2022-traffic4cast<br />
* CitySim https://github.com/ozheng1993/UCF-SST-CitySim-Dataset<br />
* PEMS-BAY / METR-LA liyaguang/DCRNN: Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow https://github.com/liyaguang/DCRNN<br />
* NAVER-SEOUL, HyunWookL/PM-MemNet https://github.com/HyunWookL/PM-MemNet<br />
* Uber movement data https://movement.uber.com/?lang=en-CA<br />
* Detector-based traffic data from many countries https://utd19.ethz.ch/index.html<br />
* Trajectories<br />
** BirdsEyeTrajectoryReconstructionSHRP2NDS https://doi.org/10.15787/VTT1/EFYEJR https://github.com/Yiru-Jiao/BirdsEyeTrajectoryReconstructionSHRP2NDS<br />
** TJRD TS: http://tjrdts.linknova.cn (vehicle were tracked through millimeter wave radar sensors installed along the freeways in sequence, and trajectories were spliced)<br />
** Trajnet++ pedestrian trajectory detection benchmark https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge<br />
** pNEUMA https://open-traffic.epfl.ch/<br />
** Vehicle-Crowd Intraction (VCI): DUT dataset https://github.com/dongfang-steven-yang/vci-dataset-dut, CITR dataset https://github.com/dongfang-steven-yang/vci-dataset-citr<br />
** Toyota Woven Prediction Dataset https://woven.toyota/en/prediction-dataset/<br />
** highD dataset (and more recent: inD, roundD, uniD, etc): new dataset of naturalistic vehicle trajectories recorded on German highways, using a drone https://www.highd-dataset.com/<br />
** Trajectories from Shanghai intersections https://www.kaggle.com/datasets/zcyan2/mixed-traffic-trajectory-dataset-in-from-shanghai<br />
** Zen traffic data (2-km Japanese highway) https://zen-traffic-data.net/english/<br />
* Open Data portals<br />
** Montreal: counts, [http://donnees.ville.montreal.qc.ca/dataset/temps-de-parcours-sur-des-segments-routiers-historique Bluetooth travel times (continuous, 2016-2017)], [http://donnees.ville.montreal.qc.ca/dataset/mtl-trajet MTL trajet travel survey with trajectories (2016, 2017)], [http://donnees.ville.montreal.qc.ca/dataset/trajets-individuels-velo-enregistre-mon-resovelo cyclist trajectories (2014)]<br />
** Quebec: [https://www.donneesquebec.ca/recherche/fr/dataset/rapports-d-accident road accidents], [https://www.donneesquebec.ca/recherche/fr/dataset/debit-de-circulation AADT]<br />
* TRB Traffic flow theory and characteristics committee (AHB45) http://tft.eng.usf.edu/docs.htm (bottom of the page)<br />
** includes links to Portland ITS Portal, PeMS in California, etc.<br />
** NGSIM dataset: traffic data for highways and urban corridors taken from multiple cameras on high buildings https://catalog.data.gov/dataset/next-generation-simulation-ngsim-vehicle-trajectories<br />
* Mobile century data https://bayen.berkeley.edu/downloads/mobile-century-data<br />
* Traffic control data from the US<br />
** Nevada http://challenger.nvfast.org/SPM/<br />
** Utah http://udottraffic.utah.gov/ATSPM/<br />
<br />
==Automated Vehicles==<br />
* Argoverse https://www.argoverse.org/<br />
* Waymo open https://waymo.com/open<br />
* Nuscenes https://www.nuscenes.org<br />
* Volvo https://developer.volvocars.com/open-datasets/cirrus/<br />
<br />
==Video-related Datasets==<br />
Image datasets of known objects are useful to train and test object classifiers<br />
* Public video data set for road transportation applications (PDTV) http://www.tft.lth.se/video/co-operation/data-exchange/ ftp://barbapappa.tft.lth.se/<br />
* Old synthetic and traffic video data http://i21www.ira.uka.de/image_sequences/<br />
* Comprehensive cars dataset: http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html<br />
* MIT car data http://cbcl.mit.edu/software-datasets/CarData.html and person data http://cbcl.mit.edu/software-datasets/PedestrianData.html<br />
* MIT Traffic Dataset http://www.ee.cuhk.edu.hk/~xgwang/MITtraffic.html<br />
* UIUC car detection http://cogcomp.cs.illinois.edu/Data/Car/ and CMU car data http://vasc.ri.cmu.edu/idb/html/car/<br />
* UCSD method for people counting with dataset http://www.svcl.ucsd.edu/projects/peoplecnt/<br />
* Oxford annotated pedestrian dataset (Town Centre) http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html#datasets<br />
* PETS datasets http://www.cvg.rdg.ac.uk/slides/pets.html<br />
** 2009: people tracking with multiple cameras http://www.cvg.rdg.ac.uk/PETS2009/ (http://www.cvg.rdg.ac.uk/PETS2009/a.html)<br />
** 2001: people and cars ftp://ftp.pets.rdg.ac.uk/pub/PETS2001/<br />
* CityCars and CityPedestrians http://www.psi.toronto.edu/index.php?q=flobject%20analysis<br />
* Gavrila http://www.gavrila.net/Research/Pedestrian_Detection/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html<br />
* INRIA dataset used by N. Dalal (HoG classifiers) http://pascal.inrialpes.fr/data/human/<br />
* Multi-View Car Dataset EPFL http://cvlab.epfl.ch/data/pose/<br />
* Multiple object type (including cars) from multiple view http://www.vision.caltech.edu/savarese/3Ddataset.html<br />
* Pascal-type object datasets: http://www.image-net.org/challenges/LSVRC/2012/browse-synsets<br />
* ETH datasets http://www.vision.ee.ethz.ch/datasets/index.en.html<br />
* VIRAT Video Dataset (surveillance, road users, car parks) http://www.viratdata.org/<br />
* NGSIM dataset: highways and urban corridors taken from multiple cameras on high buildings, with the computed results http://ngsim-community.org/<br />
* The PASCAL Visual Object Classes Homepage contains sets of images of objects of various types, including people, bicycles, cars, etc. http://pascallin.ecs.soton.ac.uk/challenges/VOC/ (see also MIT SUN dataset http://groups.csail.mit.edu/vision/SUN/ and Caltech http://www.vision.caltech.edu/Image_Datasets/Caltech256/, MIT CBCL StreetScenes http://cbcl.mit.edu/software-datasets/streetscenes/)<br />
* KITTI vision benchmark suite (images+lidar) http://www.cvlibs.net/datasets/kitti/ (object detection benchmark http://www.cvlibs.net/datasets/kitti/eval_object.php) and Karlsruhe objects http://www.cvlibs.net/datasets/karlsruhe_objects.html<br />
* Longterm Observation of Scenes with Tracks Dataset (LOST) at WUSL http://lost.cse.wustl.edu/browse<br />
* TRaffic ANd COngestionS (TRANCOS) dataset, a novel benchmark for (extremely overlapping) vehicle counting in traffic congestion situation http://agamenon.tsc.uah.es/Personales/rlopez/data/trancos/<br />
* GRAM Road-Traffic Monitoring (GRAM-RTM) dataset, a novel benchmark for multi-vehicle tracking in real-time http://agamenon.tsc.uah.es/Personales/rlopez/data/rtm/<br />
* Amazing online open source tool for annotation (and using Amazon mechanical turk) http://mit.edu/vondrick/vatic/<br />
* The Comprehensive Cars (CompCars) dataset http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html<br />
* Cityscapes Dataset (fine and coarse segmentation) https://www.cityscapes-dataset.com/<br />
** with CityPersons https://arxiv.org/abs/1702.05693<br />
* Traffic sign detection challenge http://benchmark.ini.rub.de/?section=gtsdb&subsection=news<br />
* Common objects in context (Microsoft COCO dataset) http://cocodataset.org<br />
* Miovision Traffic Camera Dataset http://podoce.dinf.usherbrooke.ca/<br />
* Synthia dataset (SYNTHetic collection of Imagery and Annotations) http://synthia-dataset.net/<br />
* [http://wider-challenge.org/ WIDER Face & Pedestrian Challenge] - Track 2: Pedestrian Detection https://competitions.codalab.org/competitions/19118<br />
* BDD100K: A Large-scale Diverse Driving Video Database: http://bair.berkeley.edu/blog/2018/05/30/bdd/<br />
* Stanford Drone Dataset: http://cvgl.stanford.edu/projects/uav_data/<br />
* The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking https://sites.google.com/site/daviddo0323/projects/uavdt<br />
* Collective Activity Dataset: https://vhosts.eecs.umich.edu/vision//activity-dataset.html<br />
* Abnormal Event Detection at 150 FPS: http://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/index.html<br />
* Traffic Research, GRAPH@FIT, Brno University of Technology (camera calibration, car image box, speed measurements): https://medusa.fit.vutbr.cz/traffic/<br />
* Vision Meets Drones http://aiskyeye.com/<br />
* MOTChallenge: The Multiple Object Tracking Benchmark https://motchallenge.net/<br />
* AI city challenge: https://www.aicitychallenge.org/ (dataset CityFlow)<br />
* STREETS: A Novel Camera Network Dataset for Traffic Flow https://github.com/corey-snyder/STREETS<br />
* MOT challenge, includes other datasets https://motchallenge.net/<br />
* Event cameras<br />
** MVSEC: The Multi Vehicle Stereo Event Camera Dataset https://daniilidis-group.github.io/mvsec/<br />
** DSEC Dataset: A Stereo Event Camera Dataset for Driving Scenarios https://dsec.ifi.uzh.ch/<br />
* DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios https://drivingstereo-dataset.github.io/<br />
* Sydney group: driving around Sydney campus http://its.acfr.usyd.edu.au/datasets/<br />
* Infrared data: FLIR https://www.flir.quebec/oem/adas/adas-dataset-form/<br />
* Infrared and visual comparison: CAMEL https://camel.ece.gatech.edu/<br />
* MOTSynth (pedestrian videos from GTA V): https://aimagelab.ing.unimore.it/imagelab/page.asp?IdPage=42<br />
* Mobility Aids http://mobility-aids.informatik.uni-freiburg.de/<br />
* X world, EvalAI (CVPR2022 AVA Accessibility Vision and Autonomy Challenge) https://eval.ai/challenge/1690/overview https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_X-World_Accessibility_Vision_and_Autonomy_Meet_ICCV_2021_paper.pdf<br />
* UCLA activity dataset https://vcla.stat.ucla.edu/Projects/Multiscale_Activity_Recognition/<br />
* Multi-Object Multi-Actor, The first benchmark and dataset dedicated to activity parsing https://moma.stanford.edu<br />
* LUMPI: The Leibniz University Multi-Perspective Intersection Dataset https://data.uni-hannover.de/dataset/lumpi<br />
<br />
==Crash datasets==<br />
* WTS: Woven Traffic Safety Dataset https://woven-visionai.github.io/wts-dataset-homepage/<br />
* CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis https://ankitshah009.github.io/accident_forecasting_traffic_camera<br />
* TCP: Traffic Camera Pipeline https://github.com/BerkeleyAutomation/Traffic_Camera_Pipeline<br />
<br />
==LIDAR datasets==<br />
Interesting applications https://scholar.google.com/scholar?&q=lidar%20urban%20environment%20parking<br />
* MulRan: Multimodal Range Dataset for Urban Place Recognition https://sites.google.com/view/mulran-pr<br />
* Complex Urban LiDAR Data Set (more robotics?) http://irap.kaist.ac.kr/dataset<br />
<br />
==Driver Simulator/Naturalistic Driving==<br />
* [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174959 Driver behavior profiling: An investigation with different smartphone sensors and machine learning] https://github.com/jair-jr/driverBehaviorDataset<br />
* Real World Driving to Assess Driver Workload https://www.hcilab.org/research/hcilab-driving-dataset/<br />
* SIMULATOR STUDY I: A Multimodal Dataset for Various Forms of Distracted Driving https://osf.io/c42cn/</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Trajectory_Management_and_Analysis&diff=1006Trajectory Management and Analysis2025-12-18T21:56:54Z<p>NicolasSaunier : </p>
<hr />
<div>* trajectory clustering in 2026<br />
** trajectory datasets: you may start to look at public ones (https://github.com/crowdbotp/OpenTraj), though we may have to ask for ethics approval before starting the project<br />
** trajectory clustering methods: I have worked in that area before, eg with http://n.saunier.free.fr/saunier/stock/saunier06clustering.pdf amd I have found this new work that seems quite interesting: https://github.com/InhwanBae/EigenTrajectory<br />
<br />
* new ideas 2021<br />
** come back to motion prediction: https://arxiv.org/abs/1908.11472<br />
** loop back on work by Laurent Boucaud and Mohsen Rezaie<br />
* Data structure for trajectories?<br />
** dimention: 2D, 3D?<br />
** time dimension: constant sampling rate?<br />
* Application data:<br />
** GPS: potentially different sampling rate<br />
** fixed point detection (eg, BT)<br />
** mobile id detection (BT from mobile sensors such as smartphones (Y. Malinovskiy))<br />
** tracking from video data: potential missing points (occlusion)<br />
** uncertain positions (spatial and time)<br />
* Exceptions ??<br />
* Python bindings: inspiration from OpenCV?<br />
* Generic interface to different database engines, possibly with spatial extensions (eg PostgreSQL with PostGIS, SQLite with SpatiaLite)<br />
* Representation of clusters, motion patterns: prototypes, paths, Gaussian processes<br />
** motion prediction from historical data / large datasets<br />
* Use of Catch for tests (https://github.com/philsquared/Catch): [https://raw.github.com/philsquared/Catch/master/single_include/catch.hpp header] only<br />
<br />
==Trajectory Filtering==<br />
* Wikipedia https://en.wikipedia.org/wiki/Numerical_differentiation https://en.wikipedia.org/wiki/Numerical_smoothing_and_differentiation https://en.wikipedia.org/wiki/Finite_difference_coefficients<br />
* papers: (from https://encrypted.google.com/search?hl=en&q=Savitzky-Golay%20vehicle%20trajectories)<br />
http://www.sciencedirect.com/science/article/pii/S0968090X1100091X https://encrypted.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0CCcQFjAB&url=http%3A%2F%2Fentpe.fr%2Ffr%2Fcontent%2Fdownload%2F3451%2F21866%2Ffile%2F09-3831.pdf&ei=FtnrU6uFN5D2yQSKu4DoCw&usg=AFQjCNGdla4RfBqPPmGR_KC8_lAUH2eE0A&sig2=PKT_UdGYDQYQfyekvvKerQ&bvm=bv.72938740,d.aWw http://www.academia.edu/6190206/Using_Drivers_Jerks_Profile_in_Computer_Vision-Based_Traffic_Safety_Evaluations._In_Transportation_Research_Board_93rd_Annual_Meeting_January_2014<br />
<br />
<br />
<br />
==Data Sources==<br />
* http://www.rtreeportal.org/<br />
* http://crawdad.cs.dartmouth.edu/data.php, eg http://crawdad.cs.dartmouth.edu/epfl/mobility<br />
* http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/default.aspx<br />
<br />
==Resources==<br />
* Microsoft Research http://research.microsoft.com/pubs/164590/KDD12-PopularRoutes.pdf<br />
* Vlachos (LCSS) http://alumni.cs.ucr.edu/~mvlachos/publications.html<br />
* http://www-ctp.di.fct.unl.pt/~fb/gisruk2011_final.pdf<br />
* http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5337231<br />
* http://isl.cs.unipi.gr/pubs/theses/Frentzos_PhD_Thesis_EN.pdf</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=GestionEquipe&diff=1005GestionEquipe2025-12-15T20:19:06Z<p>NicolasSaunier : </p>
<hr />
<div>==Formation éthique==<br />
les étudiants doivent connaître les règles d'intégrité en recherche<br />
<br />
https://www.polymtl.ca/renseignements-generaux/documents-officiels/6-recherche-et-innovation<br />
<br />
==Bonnes pratiques==<br />
* Assurer la transparence et l’équité des pratiques<br />
* Faire connaitre les opportunités et ressources sur le campus<br />
* Soutenir l’intégration des nouvelles recrues<br />
* Encourager les échanges et la convivialité<br />
* Renforcer la cohésion et l’esprit d’équipe<br />
* Réduire la pression de performance et ses effets contreproductifs<br />
* Favoriser la santé globale, le bien-être et l’équilibre de vie<br />
* Aménager des espaces agréables et confortables<br />
* Rester à l’écoute des besoins<br />
<br />
Tiré de [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Labos-inclusifs_Recommandations.pdf?guest=true Document synthèse des bonnes pratiques]<br />
<br />
==Ressources==<br />
* https://www.polymtl.ca/edi/labos-inclusifs<br />
* [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-sante-mentale.pdf?guest=true Continuum en santé mentale] [https://share.polymtl.ca/alfresco/service/api/path/content;cm:content/workspace/SpacesStore/Company%20Home/Sites/edi-web/documentLibrary/Labos-inclusifs/Continuum-mental-health.pdf?guest=true Mental Health Continuum]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Data_Science_Resources&diff=1004Data Science Resources2025-11-27T03:49:45Z<p>NicolasSaunier : /* Spatial Analysis */</p>
<hr />
<div>=General Books=<br />
* Free books on data science http://www.learndatasci.com/free-data-science-books<br />
* Many online books on various data science topics on https://bookdown.org/<br />
<br />
=Code=<br />
* Data science using Python https://github.com/jakevdp/PythonDataScienceHandbook (see [[Programming_Resources|Programming resources]] for Python and other languages)<br />
* Python data science handbook https://jakevdp.github.io/PythonDataScienceHandbook/ <br />
** [https://github.com/jupyter/jupyter/wiki Gallery of Jupyter Notebooks on programming, data science, etc.]<br />
* Examples and tutorials (Jupyter notebooks) for the transportation data management course CIV8760 (in French) https://github.com/nsaunier/CIV8760/<br />
* PolyIT GitHub https://github.com/nsaunier/TransportDataEngineering<br />
<br />
=Data Management=<br />
* [http://www.datacarpentry.org/lessons/ Data carpentry]<br />
* A Quick Guide to Organizing Computational Biology Projects https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424 <br />
* Données de recherche https://guides.biblio.polymtl.ca/donneesrecherche [[OpenScience|open science ou science ouverte]], incluant les politiques de gestion des données de recherche<br />
<br />
=Numerical Methods=<br />
* Python Programming And Numerical Methods: A Guide For Engineers And Scientists¶ https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html<br />
<br />
=Statistics=<br />
* Statistical Thinking for the 21st Century https://statsthinking21.org/<br />
* Learning Statistics with R https://learningstatisticswithr.com/<br />
* Answering questions with data https://crumplab.com/statistics/<br />
* Carnegie Mellon University free online courses: [https://oli.cmu.edu/courses/probability-statistics-open-free/ Probability & Statistics] [https://oli.cmu.edu/courses/statistical-reasoning-copy/ Statistical Reasoning]<br />
* Scientific Approaches to Transportation Research http://onlinepubs.trb.org/Onlinepubs/nchrp/cd-22/start.htm<br />
* Understanding and Communicating Multimodal Transportation Data http://web.cecs.pdx.edu/~monserec/t.data/<br />
* (French) resources<br />
** Cours MTH2302C: Probabilités et statistique, Denis Marcotte http://cours.polymtl.ca/geo/marcotte/mth2302c.html<br />
** Notes et ebooks de Ricco Rakotomalala https://cours-machine-learning.blogspot.com/<br />
** Cours du master économétrie et statistique appliquée de l'Université d’Orléans https://www.univ-orleans.fr/deg/masters/ESA/CH/churlin_E.htm#_Universit%C3%A9_d%27Orl%C3%A9ans,_Master_Econom<br />
** Explication et interprétation des modèles de choix discrets https://mate-shs.cnrs.fr/actions/tutomate/tuto35-regression-logistique-deauvieau/<br />
* Software<br />
** R, Python (scipy, statsmodels)<br />
** [http://gretl.sourceforge.net/ Gretl (econometrics)]<br />
<br />
=Artificial Intelligence=<br />
* CS188 Intro to AI http://ai.berkeley.edu<br />
* Finnish MOOC https://buildingai.elementsofai.com/<br />
<br />
=Machine Learning=<br />
* List of machine learning books http://matpalm.com/blog/cool_machine_learning_books/<br />
** Pattern Recognition and Machine Learning by Christopher Bishop free at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf<br />
* Neural Networks: Zero to Hero https://karpathy.ai/zero-to-hero.html https://github.com/karpathy/nn-zero-to-hero (NanoGPT https://github.com/karpathy/nanoGPT)<br />
* [http://profs.polymtl.ca/jagoulet/Site/Goulet_web_page_BOOK.html Probabilistic Machine Learning for Civil Engineers, James Goulet]<br />
* [https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/ Hey, T. Tansley, S. & Tolle, K. (Eds.) The Fourth Paradigm: Data-Intensive Scientific Discovery Microsoft Research, 2009]<br />
* [http://www.kdnuggets.com/ Site KDnuggets]<br />
* (French) reference book: Cornuéjols, A.; Miclet, L. & Kodratoff, Y. Apprentissage Artificiel Eyrolles, 2002<br />
* (French) [https://cours-machine-learning.blogspot.com/ Notes pour les cours de data mining de Ricco Rakotomalala]<br />
** [https://tanagra-machine-learning.blogspot.com Tanagra by the same author]<br />
* MOOC by IVADO on deep learning https://cours.edulib.org/courses/course-v1:IVADO+IA-101+P2018/<br />
* Software<br />
** [http://www.cs.waikato.ac.nz/ml/weka/ Weka]<br />
<br />
=Data Visualization=<br />
* Tufte, E. R. The Visual Display of Quantitative Information Graphics Press, 1983<br />
* Blog: [https://flowingdata.com/ Flowing data], [https://www.reddit.com/r/dataisbeautiful/ Data Is Beautiful (Reddit)]<br />
* https://datavizcatalogue.com<br />
* Articles: Wikipedia [https://en.wikipedia.org/wiki/Diagram diagrams] and [https://en.wikipedia.org/wiki/Chart Charts], [http://queue.acm.org/detail.cfm?id=1805128 ACM paper], [https://www.economist.com/node/15557455 The Economist]<br />
* Labs:<br />
** [http://hcil.umd.edu/ Human-Computer Interaction Lab] and [https://www.cattlab.umd.edu/ Center for Advanced Transportation Technology Laboratory], University of Maryland<br />
** TRB [https://www.teachamerica.com/viz/viz2006.html 5th], [http://teachamerica.com/VIZ11/index.html 6th], [https://teachamerica.com/VIZ13/index.html 7th], [http://viz17.businesscatalyst.com/videos.html 8th] and [http://www.cvent.com/events/9th-international-visualization-in-transportation-symposium-a-better-view/event-summary-aa788f12e69f4c5c83936e5c800b1152.aspx 9th] International Visualization in Transportation Symposium and Workshop<br />
** Professors [http://www.professeurs.polymtl.ca/thomas.hurtut/\#dataviz Thomas Hurtut (Polytechnique)], [http://perso.telecom-paristech.fr/~elc/ Éric Lecolinet (Télécom ParisTech)]<br />
* Courses <br />
** [https://www.graphics.stanford.edu/courses/cs448b-04-winter/ Data Visualization (CS448b)], Stanford<br />
** [https://www.cs171.org/ CS117], Hanspeter Pfister, Harvard<br />
* Videos: [https://www.youtube.com/watch?v=AdSZJzb-aX8 The Art of Data Visualization | PBS Digital Studios], [https://www.youtube.com/watch?v=-xS7QJhVbcM Harvard i-lab | Data Visualization for Non-Programmers], [https://www.youtube.com/watch?v=aT4JvF7sglg Mike Bostock (D3js) - Keynote], [https://www.youtube.com/watch?v=R-oiKt7bUU8 Designing Data Visualizations with Noah Iliinsky]<br />
* Libraries / tools<br />
** Python: [https://matplotlib.org/ matplotlib], [https://seaborn.pydata.org/ seaborn]<br />
** R: [https://ggplot2.tidyverse.org/ ggplot2]<br />
** Javascript: [https://d3js.org/ D3.js]<br />
** Old: [http://www.gnuplot.info gnuplot]<br />
<br />
=Time Series=<br />
* Forecasting: Principles and Practice (2nd ed) https://otexts.com/fpp2/<br />
<br />
=Spatial Data=<br />
* Introduction to Geospatial Concepts https://datacarpentry.org/organization-geospatial/<br />
* QGIS documentation: [https://docs.qgis.org/latest/fr/docs/index.html français], [https://docs.qgis.org/latest/en/docs/index.html english]<br />
** A Gentle Introduction to GIS https://docs.qgis.org/latest/en/docs/gentle_gis_introduction/index.html<br />
** QGIS how-to: [https://www.qgistutorials.com/en/docs/3/creating_heatmaps.html heatmaps], [https://www.giscourse.com/how-to-add-openstreetmap-basemaps-in-qgis-3-0/|add OSM layer]<br />
* SpatiaLite cookbook http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/index.html<br />
* Introduction to Python for Geographic Data Analysis https://python-gis-book.readthedocs.io<br />
* Introduction to GIS Programming https://geog-312.gishub.org<br />
* (French) books from EPFL: Systèmes d'Information Géographique [https://www.researchgate.net/publication/320979981_Systemes_d%27Information_Geographique_1 Partie 1] et [https://www.researchgate.net/publication/320980079_Systemes_d%27Information_Geographique_2 Partie 2]<br />
* (French) resources, including online courses in https://claroline-connect.univ-st-etienne.fr/web/app.php/resource/open/icap_wiki/224152#/<br />
<br />
=Spatial Analysis=<br />
* [https://ipeagit.github.io/intro_access_book/ Introduction to urban accessibility, a practical guide with R]<br />
* [https://paezha.github.io/spatial-analysis-r/ An Introduction to Spatial Data Analysis and Statistics: A Course in R], Prof. Antonio Paez<br />
* [https://mgimond.github.io/Spatial/index.html Intro to GIS and Spatial Analysis]<br />
* [https://pysal.org Python Spatial Analysis Library (PySAL)]<br />
* [https://geodacenter.github.io/documentation.html Documentation de GeoDa]<br />
* [https://www.spatialanalysisonline.com/ Geospatial Analysis - A comprehensive guide]<br />
* [https://spacetimewithr.org Spatio-Temporal Statistics with R]<br />
* [https://introsda.readthedocs.io/en/latest/index.html Introduction to Spatial Data Analysis], Aalto University</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=Data_Science_Resources&diff=1003Data Science Resources2025-11-27T03:49:09Z<p>NicolasSaunier : /* Machine Learning */</p>
<hr />
<div>=General Books=<br />
* Free books on data science http://www.learndatasci.com/free-data-science-books<br />
* Many online books on various data science topics on https://bookdown.org/<br />
<br />
=Code=<br />
* Data science using Python https://github.com/jakevdp/PythonDataScienceHandbook (see [[Programming_Resources|Programming resources]] for Python and other languages)<br />
* Python data science handbook https://jakevdp.github.io/PythonDataScienceHandbook/ <br />
** [https://github.com/jupyter/jupyter/wiki Gallery of Jupyter Notebooks on programming, data science, etc.]<br />
* Examples and tutorials (Jupyter notebooks) for the transportation data management course CIV8760 (in French) https://github.com/nsaunier/CIV8760/<br />
* PolyIT GitHub https://github.com/nsaunier/TransportDataEngineering<br />
<br />
=Data Management=<br />
* [http://www.datacarpentry.org/lessons/ Data carpentry]<br />
* A Quick Guide to Organizing Computational Biology Projects https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424 <br />
* Données de recherche https://guides.biblio.polymtl.ca/donneesrecherche [[OpenScience|open science ou science ouverte]], incluant les politiques de gestion des données de recherche<br />
<br />
=Numerical Methods=<br />
* Python Programming And Numerical Methods: A Guide For Engineers And Scientists¶ https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html<br />
<br />
=Statistics=<br />
* Statistical Thinking for the 21st Century https://statsthinking21.org/<br />
* Learning Statistics with R https://learningstatisticswithr.com/<br />
* Answering questions with data https://crumplab.com/statistics/<br />
* Carnegie Mellon University free online courses: [https://oli.cmu.edu/courses/probability-statistics-open-free/ Probability & Statistics] [https://oli.cmu.edu/courses/statistical-reasoning-copy/ Statistical Reasoning]<br />
* Scientific Approaches to Transportation Research http://onlinepubs.trb.org/Onlinepubs/nchrp/cd-22/start.htm<br />
* Understanding and Communicating Multimodal Transportation Data http://web.cecs.pdx.edu/~monserec/t.data/<br />
* (French) resources<br />
** Cours MTH2302C: Probabilités et statistique, Denis Marcotte http://cours.polymtl.ca/geo/marcotte/mth2302c.html<br />
** Notes et ebooks de Ricco Rakotomalala https://cours-machine-learning.blogspot.com/<br />
** Cours du master économétrie et statistique appliquée de l'Université d’Orléans https://www.univ-orleans.fr/deg/masters/ESA/CH/churlin_E.htm#_Universit%C3%A9_d%27Orl%C3%A9ans,_Master_Econom<br />
** Explication et interprétation des modèles de choix discrets https://mate-shs.cnrs.fr/actions/tutomate/tuto35-regression-logistique-deauvieau/<br />
* Software<br />
** R, Python (scipy, statsmodels)<br />
** [http://gretl.sourceforge.net/ Gretl (econometrics)]<br />
<br />
=Artificial Intelligence=<br />
* CS188 Intro to AI http://ai.berkeley.edu<br />
* Finnish MOOC https://buildingai.elementsofai.com/<br />
<br />
=Machine Learning=<br />
* List of machine learning books http://matpalm.com/blog/cool_machine_learning_books/<br />
** Pattern Recognition and Machine Learning by Christopher Bishop free at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf<br />
* Neural Networks: Zero to Hero https://karpathy.ai/zero-to-hero.html https://github.com/karpathy/nn-zero-to-hero (NanoGPT https://github.com/karpathy/nanoGPT)<br />
* [http://profs.polymtl.ca/jagoulet/Site/Goulet_web_page_BOOK.html Probabilistic Machine Learning for Civil Engineers, James Goulet]<br />
* [https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discovery/ Hey, T. Tansley, S. & Tolle, K. (Eds.) The Fourth Paradigm: Data-Intensive Scientific Discovery Microsoft Research, 2009]<br />
* [http://www.kdnuggets.com/ Site KDnuggets]<br />
* (French) reference book: Cornuéjols, A.; Miclet, L. & Kodratoff, Y. Apprentissage Artificiel Eyrolles, 2002<br />
* (French) [https://cours-machine-learning.blogspot.com/ Notes pour les cours de data mining de Ricco Rakotomalala]<br />
** [https://tanagra-machine-learning.blogspot.com Tanagra by the same author]<br />
* MOOC by IVADO on deep learning https://cours.edulib.org/courses/course-v1:IVADO+IA-101+P2018/<br />
* Software<br />
** [http://www.cs.waikato.ac.nz/ml/weka/ Weka]<br />
<br />
=Data Visualization=<br />
* Tufte, E. R. The Visual Display of Quantitative Information Graphics Press, 1983<br />
* Blog: [https://flowingdata.com/ Flowing data], [https://www.reddit.com/r/dataisbeautiful/ Data Is Beautiful (Reddit)]<br />
* https://datavizcatalogue.com<br />
* Articles: Wikipedia [https://en.wikipedia.org/wiki/Diagram diagrams] and [https://en.wikipedia.org/wiki/Chart Charts], [http://queue.acm.org/detail.cfm?id=1805128 ACM paper], [https://www.economist.com/node/15557455 The Economist]<br />
* Labs:<br />
** [http://hcil.umd.edu/ Human-Computer Interaction Lab] and [https://www.cattlab.umd.edu/ Center for Advanced Transportation Technology Laboratory], University of Maryland<br />
** TRB [https://www.teachamerica.com/viz/viz2006.html 5th], [http://teachamerica.com/VIZ11/index.html 6th], [https://teachamerica.com/VIZ13/index.html 7th], [http://viz17.businesscatalyst.com/videos.html 8th] and [http://www.cvent.com/events/9th-international-visualization-in-transportation-symposium-a-better-view/event-summary-aa788f12e69f4c5c83936e5c800b1152.aspx 9th] International Visualization in Transportation Symposium and Workshop<br />
** Professors [http://www.professeurs.polymtl.ca/thomas.hurtut/\#dataviz Thomas Hurtut (Polytechnique)], [http://perso.telecom-paristech.fr/~elc/ Éric Lecolinet (Télécom ParisTech)]<br />
* Courses <br />
** [https://www.graphics.stanford.edu/courses/cs448b-04-winter/ Data Visualization (CS448b)], Stanford<br />
** [https://www.cs171.org/ CS117], Hanspeter Pfister, Harvard<br />
* Videos: [https://www.youtube.com/watch?v=AdSZJzb-aX8 The Art of Data Visualization | PBS Digital Studios], [https://www.youtube.com/watch?v=-xS7QJhVbcM Harvard i-lab | Data Visualization for Non-Programmers], [https://www.youtube.com/watch?v=aT4JvF7sglg Mike Bostock (D3js) - Keynote], [https://www.youtube.com/watch?v=R-oiKt7bUU8 Designing Data Visualizations with Noah Iliinsky]<br />
* Libraries / tools<br />
** Python: [https://matplotlib.org/ matplotlib], [https://seaborn.pydata.org/ seaborn]<br />
** R: [https://ggplot2.tidyverse.org/ ggplot2]<br />
** Javascript: [https://d3js.org/ D3.js]<br />
** Old: [http://www.gnuplot.info gnuplot]<br />
<br />
=Time Series=<br />
* Forecasting: Principles and Practice (2nd ed) https://otexts.com/fpp2/<br />
<br />
=Spatial Data=<br />
* Introduction to Geospatial Concepts https://datacarpentry.org/organization-geospatial/<br />
* QGIS documentation: [https://docs.qgis.org/latest/fr/docs/index.html français], [https://docs.qgis.org/latest/en/docs/index.html english]<br />
** A Gentle Introduction to GIS https://docs.qgis.org/latest/en/docs/gentle_gis_introduction/index.html<br />
** QGIS how-to: [https://www.qgistutorials.com/en/docs/3/creating_heatmaps.html heatmaps], [https://www.giscourse.com/how-to-add-openstreetmap-basemaps-in-qgis-3-0/|add OSM layer]<br />
* SpatiaLite cookbook http://www.gaia-gis.it/gaia-sins/spatialite-cookbook-5/index.html<br />
* Introduction to Python for Geographic Data Analysis https://python-gis-book.readthedocs.io<br />
* Introduction to GIS Programming https://geog-312.gishub.org<br />
* (French) books from EPFL: Systèmes d'Information Géographique [https://www.researchgate.net/publication/320979981_Systemes_d%27Information_Geographique_1 Partie 1] et [https://www.researchgate.net/publication/320980079_Systemes_d%27Information_Geographique_2 Partie 2]<br />
* (French) resources, including online courses in https://claroline-connect.univ-st-etienne.fr/web/app.php/resource/open/icap_wiki/224152#/<br />
<br />
=Spatial Analysis=<br />
* [https://ipeagit.github.io/intro_access_book/ Introduction to urban accessibility, a practical guide with R]<br />
* [https://paezha.github.io/spatial-analysis-r/ An Introduction to Spatial Data Analysis and Statistics: A Course in R], Prof. Antonio Paez<br />
* [https://mgimond.github.io/Spatial/index.html Intro to GIS and Spatial Analysis]<br />
* [https://pysal.org Python Spatial Analysis Library (PySAL)]<br />
* [https://geodacenter.github.io/documentation.html Documentation de GeoDa]<br />
* [https://www.spatialanalysisonline.com/ Geospatial Analysis - A comprehensive guide]<br />
* [https://spacetimewithr.org Spatio-Temporal Statistics with R]</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1002SeminaireGroupe2025-11-21T19:05:19Z<p>NicolasSaunier : </p>
<hr />
<div>* 2025<br />
** 21/11/2025: group meeting and discussion of data organization (see [[OpenScience]])<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunierhttps://www.polymtl.ca/wikitransport/index.php?title=SeminaireGroupe&diff=1001SeminaireGroupe2025-11-21T19:04:38Z<p>NicolasSaunier : </p>
<hr />
<div>* 2025<br />
** 21/11/2025: group meeting and discussion of data organization<br />
** 31/10/2025: group meeting<br />
* 2024<br />
** research data planning and management<br />
** 10 or 12/07/2024: discussion of paper in https://tsr.international/TSR/issue/view/3297<br />
** 21/06/2024: Tristan Fortin, Study of road user understanding, behavior and safety on bicycle boulevards<br />
** 07/06/2024: Frédérick Chabot, Characterization framework to assess the performance of a tool for the observation of life in public spaces<br />
** 31/05/2024: Qingwu Liu, A comprehensive case study on deep learning-based object recognition for safety analysis on roadside and vehicle-mounted dataset<br />
** 17/05/2024: Guillaume Néven, Probabilistic approach to path travel time estimation<br />
* 2023<br />
** Summer seminar<br />
*** Tristan Fortin, Collecte de données sur les vélorues : analyse vidéo et questionnaire<br />
*** Yash Pratap Singh, Benchmarking Computer Vision Surveillance Algorithms for Practical Traffic Applications<br />
*** Qingwu Liu, How good are deep learning methods for automated road safety analysis using video data? An experimental study<br />
*** Zhangcun Yan, Investigating and Modeling Motorized and Non-Motorized Interaction Behavior in Shared Spaces of Intersections<br />
*** Tarcisio Costa de Souza Neto, Automatisation d'extraction et d'analyse des données ouvertes de transports<br />
*** Reza Zarei, “Questionnaire Design” for surveying drivers' attitudes towards road safety. <br />
*** Edem Houndjafo, Étude pour l’installation de feux cyclistes aux intersections du chemin de la Côte Sainte Catherine<br />
*** Xinyu Chen, Matrix and Tensor Models for Spatiotemporal Traffic Data Modeling<br />
*** Mohammad Ghavidel, Smartphone Accelerometer Sensor Orientation: best Techniques and its applications<br />
*** Frédérick Chabot, Develop a performance characterization framework for a public space - public life data collection tool<br />
** group meetings open to everyone starting February 23rd: discussions and exchanges of ideas, on research projects and technical discussions, sometimes based on published papers (reading club). <br />
*** February 23rd: data management plan ("plan de gestion des données") https://guides.biblio.polymtl.ca/donneesrecherche<br />
*** other ideas: open access publication, peer review is largely a failure... https://experimentalhistory.substack.com/p/the-rise-and-fall-of-peer-review So we could write papers differently: https://psyarxiv.com/2uxwk/<br />
<br />
* 2012<br />
** 26 juillet<br />
** 19 juillet<br />
** 12 juillet<br />
** 5 juillet: application du Canadian Capacity Guide (François), http://www.runmycode.org<br />
** 29 juin: projet de collecte de données sur les carrefours giratoires (Arthur)<br />
** 22 juin: projet marquage au sol (Caio)<br />
** 15 juin: discussion des paramètres de description des carrefours giratoires (Paul)<br />
** 8 juin: demo background subtraction (Anurag, Jean-Philippe), de traffic intelligence<br />
** 1 juin: presentation de Mohamed sur les méthodes de prédictions des mouvement (en particulier en robotique), résultats graphiques de François<br />
** 18 mai: première réunion</div>NicolasSaunier