%0 Electronic Article
%A Biao Yin
%+ Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France
%A Mahjoub Dridi
%+ Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France
%A Abdellah El Moudni
%+ Laboratoire Systèmes et Transports, Université de Technologie de Belfort-Montbéliard, 90000 Belfort, France
%K vehicle-following model
%K reinforcement learning method
%K microscopic traffic dynamic model
%K distributed traffic network system
%K traffic network loading environment
%K cellular automata theory
%K traffic network microsimulation model
%K ADP method
%K approximate dynamic programming
%K adaptive traffic signal control algorithm
%X This study presents the adaptive traffic signal control algorithm in a distributed traffic network system. The proposed algorithm is based on a micro-simulation model and a reinforcement learning method, namely approximate dynamic programming (ADP). By considering traffic environment in discrete time, the microscopic traffic dynamic model is built. In particular, the authors explore a vehicle-following model using cellular automata theory. This vehicle-following model theoretically contributes to traffic network loading environment in an accessible way. To make the network coordinated, tunable state with weights of queue length and vehicles on lane is considered. The intersection can share information with each other in this state representation and make a joint action for intersection coordination. Moreover, the traffic signal control algorithm based on ADP method performs quite well in different performance measures witnessed by simulations. By comparing with other control methods, experimental results present that the proposed algorithm could be a potential candidate in an application of traffic network control system.
%@ 1751-956X
%T Traffic network micro-simulation model and control algorithm based on approximate dynamic programming
%B IET Intelligent Transport Systems
%D April 2016
%V 10
%N 3
%P 186-196
%I Institution of Engineering and Technology
%U https://digital-library.theiet.org/;jsessionid=7ra44d3tsirce.x-iet-live-01content/journals/10.1049/iet-its.2015.0108
%G EN