The Power of Adaptability Applied to Vehicular Traffic Management

Abstract

Nobody likes sitting in traffic, at least the author of this work does not. In this thesis, we take the question of how can we improve traffic? We break it down and iterate on it, finally arriving at the problem of traffic optimization, via the optimization of traffic lights. This is the main driver of this research work. Traffic light control strategies have been previously classified as static and dynamic. We typically see static controllers in the fixed-cycle or actuated controllers that are widely deployed in the traffic lights of our cities; however, truly smart traffic lights are still not a widespread technology. This work tries to ground some of the research in dynamic traffic light controllers, so that smarter traffic lights become practical and ready for roll-out. We describe a Reinforcement Learning algorithm which we designed, and which only uses information that is local to the traffic light. A key element of this algorithm is the fact that we believe the inputs to it can be obtained by existing traffic intersection technologies, making its deployment feasible. We compare this algorithm against an optimal static traffic control policy, in the pursuit of understanding the strengths of both controller types. Finally, we analyze and present the results of this comparison, and illustrate a set of opportunities that may improve traffic management through different approaches, such as optimal traffic management, following the footsteps of this work or by having drivers collaborate with load balancing.