Intelligent traffic light systems using edge flow predictions.

In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations. • With this work we aimed to optimize traffic signal systems by dynamically setting the system to behave as per predicted traffic flow. • Vehicle flow is predicted using Graph-based semi-supervised and active learning on edge flows. • The systems were created and tested using Simulation Of Urban Mobility (SUMO). • Vehicle data and routes were also generated using this application. • The proposed approach was compared against other methods, such as an occupancy based method, a scoring-based method and reinforcement learning. [ABSTRACT FROM AUTHOR]