Public Transportation Datasets

PolyIT Datasets, generally not public for privacy reasons.

Traffic Data

• 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
  • Montreal: counts, Bluetooth travel times (continuous, 2016-2017), MTL trajet travel survey with trajectories (2016, 2017), cyclist trajectories (2014)
  • Quebec: road accidents, AADT
• 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.
  • 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/

Automated Vehicles

• Argoverse https://www.argoverse.org/
• Waymo open https://waymo.com/open
• Nuscenes https://www.nuscenes.org
• Volvo https://developer.volvocars.com/open-datasets/cirrus/

Video-related Datasets

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

• Public video data set for road transportation applications (PDTV) http://www.tft.lth.se/video/co-operation/data-exchange/ ftp://barbapappa.tft.lth.se/
• Old synthetic and traffic video data http://i21www.ira.uka.de/image_sequences/
• Comprehensive cars dataset: http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
• MIT car data http://cbcl.mit.edu/software-datasets/CarData.html and person data http://cbcl.mit.edu/software-datasets/PedestrianData.html
• MIT Traffic Dataset http://www.ee.cuhk.edu.hk/~xgwang/MITtraffic.html
• UIUC car detection http://cogcomp.cs.illinois.edu/Data/Car/ and CMU car data http://vasc.ri.cmu.edu/idb/html/car/
• UCSD method for people counting with dataset http://www.svcl.ucsd.edu/projects/peoplecnt/
• Oxford annotated pedestrian dataset (Town Centre) http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html#datasets
• PETS datasets http://www.cvg.rdg.ac.uk/slides/pets.html
  • 2009: people tracking with multiple cameras http://www.cvg.rdg.ac.uk/PETS2009/ (http://www.cvg.rdg.ac.uk/PETS2009/a.html)
  • 2001: people and cars ftp://ftp.pets.rdg.ac.uk/pub/PETS2001/
• CityCars and CityPedestrians http://www.psi.toronto.edu/index.php?q=flobject%20analysis
• Gavrila http://www.gavrila.net/Research/Pedestrian_Detection/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html
• INRIA dataset used by N. Dalal (HoG classifiers) http://pascal.inrialpes.fr/data/human/
• Multi-View Car Dataset EPFL http://cvlab.epfl.ch/data/pose/
• Multiple object type (including cars) from multiple view http://www.vision.caltech.edu/savarese/3Ddataset.html
• Pascal-type object datasets: http://www.image-net.org/challenges/LSVRC/2012/browse-synsets
• ETH datasets http://www.vision.ee.ethz.ch/datasets/index.en.html
• VIRAT Video Dataset (surveillance, road users, car parks) http://www.viratdata.org/
• NGSIM dataset: highways and urban corridors taken from multiple cameras on high buildings, with the computed results http://ngsim-community.org/
• 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/)
• 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
• Longterm Observation of Scenes with Tracks Dataset (LOST) at WUSL http://lost.cse.wustl.edu/browse
• 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/
• 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/
• Amazing online open source tool for annotation (and using Amazon mechanical turk) http://mit.edu/vondrick/vatic/
• The Comprehensive Cars (CompCars) dataset http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
• Cityscapes Dataset (fine and coarse segmentation) https://www.cityscapes-dataset.com/
  • with CityPersons https://arxiv.org/abs/1702.05693
• Traffic sign detection challenge http://benchmark.ini.rub.de/?section=gtsdb&subsection=news
• Common objects in context (Microsoft COCO dataset) http://cocodataset.org
• Miovision Traffic Camera Dataset http://podoce.dinf.usherbrooke.ca/
• Synthia dataset (SYNTHetic collection of Imagery and Annotations) http://synthia-dataset.net/
• 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/
• 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 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/
• SIMULATOR STUDY I: A Multimodal Dataset for Various Forms of Distracted Driving https://osf.io/c42cn/