Reinforcement Learning forTrueAdaptive Traffic Signal Control

Baher Abdulhai, Rob Pringle, Grigoris J. Karakoulas
2003 Journal of transportation engineering  
The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificial intelligence approach undergoing development in the machinelearning community, offers key advantages in this regard. The ability of a control agent to learn relationships between control actions and their effect on the environment while pursuing a goal is a distinct improvement over prespecified models
more » ... especified models of the environment. Prespecified models are a prerequisite of conventional control methods and their accuracy limits the performance of control agents. This paper contains an introduction to Q-learning, a simple yet powerful reinforcement learning algorithm, and presents a case study involving application to traffic signal control. Encouraging results of the application to an isolated traffic signal, particularly under variable traffic conditions, are presented. A broader research effort is outlined, including extension to linear and networked signal systems and integration with dynamic route guidance. The research objective involves optimal control of heavily congested traffic across a two-dimensional road network-a challenging task for conventional traffic signal control methodologies.
doi:10.1061/(asce)0733-947x(2003)129:3(278) fatcat:ocpavsaih5h57l2hqz2kbjslpy