RL based Traffic Management System
- 0 Collaborators
In this project, we developed a reinforcement learning model which given any road network and any traffic pattern (traffic densities which exhibit a daily, weekly or special routine) learns to optimize the flow of the traffic such that the clearance time of the traffic is brought down. ...learn more
Project status: Under Development
Intel Technologies
Intel Python
Overview / Usage
This novel idea won us the 3rd prize for Enginx2018 Digital Twin Challenge. Our idea was to use live traffic feed throughout the city and use this to data along with various other features to let an reinforcement based algorithm to optimize the traffic flow in the city. To prove this idea we build a simulation to train the algorithm and a traffic density evaluator.
We even went ahead to build a live model of 6 junction City with LEDs' as cars.
Methodology / Approach
First, sensors at the road intersection determine the number of cars waiting at any red light. All information is stored to come up with a time-series dataset which establishes a pattern in the traffic. A reinforcement learning algorithm runs on the traffic pattern to solve it quicker, i.e. to clear all the vehicles from the random network as quick as possible.
Technologies Used
- SUMO
- OpenCV