Realtime Autonomous Traffic Signal Switching system
Objective: Traffic signal switching happens to be one the weakest link in the entire traffic flow system. The current traffic switching system relies on preset timing or a manual controller system based mechanism. We planned to optimise the traffic light switching system using Reinforcement learning in a non-intrusive method.
We thought of adapting the autonomous helicopter training mechanism to model the Traffic Switching system.
We have successfully integrated our q-learning algorithm with realtime traffic flow information provided by HERE maps API. We collected the data for 5 days, and then performed a comparative analysis (plots attached) on how much improvement in traffic flow our model can achieve in terms of avg. waiting time.
A suitable extension was to have large negative weights if important vehicles (ambulance, police, fire cars) gets stopped at signals. The algorithm adapts itself to provide a congestion free path for them as much as possible.
How to run the code :
OS requirement: *nix machine
Run this on terminal
To run the live demo :
$ java Main $(echo `node fetch_stats.js`)
To generate graphs :
$node script.js $make $java Main dailytime.csv $sudo pip install -r requirements.txt $python plotting.py
- Values in brackets next to the roads are traffic intensity values fetched via HERE-MAPS api.
- An ambulance is added(location randomly generated - RED/WHITE - MG Road to Kasturba Road) to indicate autonomous Traffic signals switching to provide congestion free path to important vehicles