Reinforcement Learning Algorithm for Traffic Signal Control To Reduce GHG Emissions

Persistent traffic congestions which are varying in volume and duration in the cities are not just the source of frustration for drivers, but also one of the biggest sources of greenhouse gas emissions in the world. Those types of congestions cannot be adequately resolved by the traditional traffic signal controllers.
Breeze Labs Inc. in cooperation with Simon Fraser University aims to develop a system that provides optimal traffic signal control based on road user information received from existing CCTV cameras and other traffic sensors on the intersections. Optimizing all road users’ wait times and as the result reducing an environmental footprint of traffic at intersections can be achieved by using latest advancements in Artificial Intelligence algorithms. The goal of this project is to develop such algorithms that can be used by Breeze Labs in real-time on the intersections.

Faculty Supervisor:

Mo Chen


Pedram Agand


Breeze Traffic


Computer science


Transportation and warehousing


Simon Fraser University



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