REALLIGHT: DRL based Intersection Control in Developing Countries without Traffic Simulators

Effective traffic intersection control is a crucial problem for urban sustainability. State of the art research seeking Artificial Intelligence (AI), for example Deep Reinforcement Learning (DRL) based traffic control are using traffic simulators, ignoring the shortcomings of traffic simulators used to train the DRL control algorithms. These simulators are limited in capturing fine nuances in traffic flow changes, which can make the trained models unrealistic. This is especially true in developing countries, where traffic flow is non-laned and chaotic, and extremely hard to simulate based on standard microscopic-model based traffic simulation rules. In the given paper, we seek to do away with traffic simulators, and try to train DRL systems with 40 hours of real traffic data deploying cameras at a New Delhi busy traffic intersection, making intelligent traffic intersection control more realistic for developing countries, and hence has been termed as REALLIGHT.