Running

In order to build the container from the Dockerfile, we first need to build it. We associate it the flow tag:

sudo docker build -t flow .

In order to run the container, invoque the following instructions. The current directory ($(pwd) on Linux) will be bind mounted to /host allowing the user to modify the project from the host machine and run it from the docker. We also bind mount ./ray_results (host) to /root/ray_results (container) which allow to interact with the results (e.g. running Tensorboard) from the host. There is also some Linux display wizardry going on with X11 allowing to set the render flag to True in IssyExperimentParams and have the Sumo GUI appear on the host machine:

# allow connection to x11 server from docker
xhost +

# run sumo.py in dockerised sumo-gui
mkdir -p ray_results && sudo docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --privileged -ti --mount src="$PWD/ray_results",target=/root/ray_results,type=bind --mount src="$(pwd)",target=/host,type=bind flow /bin/bash -c "source /root/.bashrc && python /host/src/flow/issy.py"

In order to open a shell inside the docker image with graphic support, run the following command.

xhost +
mkdir -p ray_results && sudo docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --privileged -ti --mount src="$PWD/ray_results",target=/root/ray_results,type=bind --mount src="$(pwd)",target=/host,type=bind flow /bin/bash

Tensorboard

To see training results in Tensorboard, call it from the host after having ensured you have the appropriate permissions as follows:

sudo chmod -R +777 ray_results
tensorboard --logdir ray_results

État de l’art

Acquisition de données

• https://opentraffic.io
• https://github.com/graphhopper/open-traffic-collection
• https://www.stadt-koeln.de/externe-dienste/open-data/traffic.php
• https://developer.here.com/documentation/traffic/topics_v6.1/resource-parameters-flow.html (owned my Nokia Maps)
• http://www.pressreleasepoint.com/machine-learning-help-optimize-traffic-and-reduce-pollution
• http://cs229.stanford.edu/proj2005/RobinsonMosherEgner-LearningTrafficLightControlPolicies.pdf
• https://www.vegvesen.no/_attachment/336339/binary/585485
• https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
• https://archive.ics.uci.edu/ml/datasets/Taxi+Service+Trajectory+-+Prediction+Challenge,+ECML+PKDD+2015
• https://catalog.data.gov/dataset?q=traffic

Simulation de données

• http://www.traffic-simulation.de/
  • code source: https://github.com/movsim/traffic-simulation-de
• http://sumo.dlr.de/index.html
  • Sumo interaction API in python: http://www.sumo.dlr.de/daily/pydoc/traci.html

Traffic flow prediction

• https://arxiv.org/pdf/1710.01695.pdf
• https://www.sciencedirect.com/science/article/pii/S0952197611000777#f0005
• https://www.sciencedirect.com/science/article/pii/S0952197617302658?via%3Dihub
• https://www.sciencedirect.com/science/article/pii/S0968090X00000474
• https://hal.archives-ouvertes.fr/hal-01163018/document

Keywords

Useful for google searches:

• Traffic smoothing