« Public Transportation Datasets » : différence entre les versions

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(53 versions intermédiaires par le même utilisateur non affichées)
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See [[PolyDatasets|PolyIT Datasets]], generally not public for privacy reasons.
Another resource: https://sites.google.com/view/transport-research-network/shared-resources/datasets
==Traffic Data==
==Traffic Data==
* highD dataset: new dataset of naturalistic vehicle trajectories recorded on German highways, using a drone https://www.highd-dataset.com/
* DLR Urban Traffic dataset (DLR-UT) https://elib.dlr.de/207287/
* INTERnational, Adversarial and Cooperative moTION Dataset https://interaction-dataset.com/
* NeurIPS 2022 Traffic4cast competition https://github.com/iarai/NeurIPS2022-traffic4cast
* CitySim https://github.com/ozheng1993/UCF-SST-CitySim-Dataset
* PEMS-BAY / METR-LA liyaguang/DCRNN: Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow https://github.com/liyaguang/DCRNN
* NAVER-SEOUL, HyunWookL/PM-MemNet https://github.com/HyunWookL/PM-MemNet
* Uber movement data https://movement.uber.com/?lang=en-CA
* Detector-based traffic data from many countries https://utd19.ethz.ch/index.html
* Trajectories
** BirdsEyeTrajectoryReconstructionSHRP2NDS https://doi.org/10.15787/VTT1/EFYEJR https://github.com/Yiru-Jiao/BirdsEyeTrajectoryReconstructionSHRP2NDS
** TJRD TS: http://tjrdts.linknova.cn (vehicle were tracked through millimeter wave radar sensors installed along the freeways in sequence, and trajectories were spliced)
** Trajnet++ pedestrian trajectory detection benchmark https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge
** pNEUMA https://open-traffic.epfl.ch/
** 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
** Toyota Woven Prediction Dataset https://woven.toyota/en/prediction-dataset/
** 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/
** Trajectories from Shanghai intersections https://www.kaggle.com/datasets/zcyan2/mixed-traffic-trajectory-dataset-in-from-shanghai
** Zen traffic data (2-km Japanese highway) https://zen-traffic-data.net/english/
* Open Data portals
* Open Data portals
** 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)]
** 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)]
** Quebec road accidents https://www.donneesquebec.ca/recherche/fr/dataset/rapports-d-accident
** Quebec: [https://www.donneesquebec.ca/recherche/fr/dataset/rapports-d-accident road accidents], [https://www.donneesquebec.ca/recherche/fr/dataset/debit-de-circulation AADT]
* TRB Traffic flow theory and characteristics committee (AHB45) http://tft.eng.usf.edu/docs.htm (bottom of the page)
* TRB Traffic flow theory and characteristics committee (AHB45) http://tft.eng.usf.edu/docs.htm (bottom of the page)
** includes links to Portland ITS Portal, PeMS in California, etc.
** includes links to Portland ITS Portal, PeMS in California, etc.
** 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
** 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
* Mobile century data https://bayen.berkeley.edu/downloads/mobile-century-data
* Traffic control data from the US
** Nevada http://challenger.nvfast.org/SPM/
** Utah http://udottraffic.utah.gov/ATSPM/


==Our Datasets==
==Automated Vehicles==
* Mohamed Gomaa Mohamed's: Guy / Rene Levesque, St Marc
* Argoverse https://www.argoverse.org/
* Marilyne Brosseau's: Sherbrooke / Amherst, Sherbrooke / Iberville
* Waymo open https://waymo.com/open
* Quebec's roundabouts (to validate) + NYC?
* Nuscenes https://www.nuscenes.org
* Pedestrian data: Polytechnique and NYC
* Volvo https://developer.volvocars.com/open-datasets/cirrus/
* Place Valois
* Worker safety (IRSST)
* Pedestrian - vehicle interactions (Catherine - INRS)


==Video-related Datasets==
==Video-related Datasets==
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* Miovision Traffic Camera Dataset http://podoce.dinf.usherbrooke.ca/
* Miovision Traffic Camera Dataset http://podoce.dinf.usherbrooke.ca/
* Synthia dataset (SYNTHetic collection of Imagery and Annotations) http://synthia-dataset.net/
* Synthia dataset (SYNTHetic collection of Imagery and Annotations) http://synthia-dataset.net/
* WIDER Face & Pedestrain Challenge - Track 2: Pedestrian Detection https://competitions.codalab.org/competitions/19068
* [http://wider-challenge.org/ WIDER Face & Pedestrian Challenge] - Track 2: Pedestrian Detection https://competitions.codalab.org/competitions/19118
* BDD100K: A Large-scale Diverse Driving Video Database: http://bair.berkeley.edu/blog/2018/05/30/bdd/
* BDD100K: A Large-scale Diverse Driving Video Database: http://bair.berkeley.edu/blog/2018/05/30/bdd/
* Stanford Drone Dataset: http://cvgl.stanford.edu/projects/uav_data/
* The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking https://sites.google.com/site/daviddo0323/projects/uavdt
* Collective Activity Dataset: https://vhosts.eecs.umich.edu/vision//activity-dataset.html
* Abnormal Event Detection at 150 FPS: http://www.cse.cuhk.edu.hk/leojia/projects/detectabnormal/index.html
* Traffic Research, GRAPH@FIT, Brno University of Technology (camera calibration, car image box, speed measurements): https://medusa.fit.vutbr.cz/traffic/
* Vision Meets Drones http://aiskyeye.com/
* MOTChallenge: The Multiple Object Tracking Benchmark https://motchallenge.net/
* AI city challenge: https://www.aicitychallenge.org/ (dataset CityFlow)
* STREETS: A Novel Camera Network Dataset for Traffic Flow https://github.com/corey-snyder/STREETS
* MOT challenge, includes other datasets https://motchallenge.net/
* Event cameras
** MVSEC: The Multi Vehicle Stereo Event Camera Dataset https://daniilidis-group.github.io/mvsec/
** DSEC Dataset: A Stereo Event Camera Dataset for Driving Scenarios https://dsec.ifi.uzh.ch/
* DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios https://drivingstereo-dataset.github.io/
* Sydney group: driving around Sydney campus http://its.acfr.usyd.edu.au/datasets/
* Infrared data: FLIR https://www.flir.quebec/oem/adas/adas-dataset-form/
* Infrared and visual comparison: CAMEL https://camel.ece.gatech.edu/
* MOTSynth (pedestrian videos from GTA V): https://aimagelab.ing.unimore.it/imagelab/page.asp?IdPage=42
* Mobility Aids http://mobility-aids.informatik.uni-freiburg.de/
* 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
* UCLA activity dataset https://vcla.stat.ucla.edu/Projects/Multiscale_Activity_Recognition/
* Multi-Object Multi-Actor, The first benchmark and dataset dedicated to activity parsing https://moma.stanford.edu
* LUMPI: The Leibniz University Multi-Perspective Intersection Dataset https://data.uni-hannover.de/dataset/lumpi
==Crash datasets==
* WTS: Woven Traffic Safety Dataset https://woven-visionai.github.io/wts-dataset-homepage/
* CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis https://ankitshah009.github.io/accident_forecasting_traffic_camera
* TCP: Traffic Camera Pipeline https://github.com/BerkeleyAutomation/Traffic_Camera_Pipeline
==LIDAR datasets==
Interesting applications https://scholar.google.com/scholar?&q=lidar%20urban%20environment%20parking
* MulRan: Multimodal Range Dataset for Urban Place Recognition https://sites.google.com/view/mulran-pr
* Complex Urban LiDAR Data Set (more robotics?) http://irap.kaist.ac.kr/dataset


==Driver Simulator/Naturalistic Driving==
==Driver Simulator/Naturalistic Driving==
* [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
* Real World Driving to Assess Driver Workload https://www.hcilab.org/research/hcilab-driving-dataset/
* Real World Driving to Assess Driver Workload https://www.hcilab.org/research/hcilab-driving-dataset/
* SIMULATOR STUDY I: A Multimodal Dataset for Various Forms of Distracted Driving https://osf.io/c42cn/
* SIMULATOR STUDY I: A Multimodal Dataset for Various Forms of Distracted Driving https://osf.io/c42cn/

Dernière version du 2 janvier 2026 à 13:22

See PolyIT Datasets, generally not public for privacy reasons.

Another resource: https://sites.google.com/view/transport-research-network/shared-resources/datasets

Traffic Data

Automated Vehicles

Video-related Datasets

Image datasets of known objects are useful to train and test object classifiers

Crash datasets

LIDAR datasets

Interesting applications https://scholar.google.com/scholar?&q=lidar%20urban%20environment%20parking

Driver Simulator/Naturalistic Driving