varun-projects / traffic-optimisation

Traffic Signal timings using Deep Q-Learning

Traffic Optimisation

Traffic Signals optimisation for better road traffic handling using Deep Reinforcement Learning. The software SUMO ( Simulation of Urban MObility ) has been used to simulate traffic. To control the traffic signals system, the Traci, or Traffic Control Interface has been used which allows to retrieve values of simulated objects and to manipulate their behaviour "on-line".

Setup Instructions

Install SUMO 0.28.0 using the command -

sudo apt-get install sumo sumo-tools sumo-doc

Create a virtual environment(install if not installed) using the command -

virtualenv venv

Activate the virtual environment -

source venv/bin/activate

Install requirements using pip install -r setup/requirements.txt

Set the SUMO_HOME environment variable equal to the SUMO directory installed in your system ( /usr/share/sumo ).

Clone this repository using

git clone https://github.com/Wert1996/Traffic-Optimisation.git

To see the simulation in action, run

python singleSimulation.py

To see the training of the Deep Q Learner in action, run

python control.py

The training can be run in gui mode by changing sumo to sumo-gui in the sumo binary path in control.py. The training results are stored in ResultsOfSimulations.txt