Optimal fuzzy traffic signal controller for an isolated intersection

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Fuzzy logic system (FLS) is the method applied to develop the intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The FLS controller (FLC) uses genetic algorithm to tune its parameters during learning phase. Finally, The performance of the intelligent FLC is compared with the performance of a FLC with predefined parameters and three simple fixed-time controller. Simulation results indicate that intelligent FLC significantly reduces the total delay in the network compared to the fixed-time method and FLC with manual parameter setting.

[1]  Monireh Abdoos,et al.  Traffic light control in non-stationary environments based on multi agent Q-learning , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[2]  Gang Li,et al.  Adaptive Traffic Signals Control by Using Fuzzy Logic , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[3]  Dipti Srinivasan,et al.  Cooperative, hybrid agent architecture for real-time traffic signal control , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[4]  J. Y. K. Luk,et al.  Two traffic responsive area traffic control methods: SCAT and SCOOT , 1983 .

[5]  Emile H. L. Aarts,et al.  Global optimization and simulated annealing , 1991, Math. Program..

[6]  J. Spall.  Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .

[7]  Pitu B. Mirchandani,et al.  A REAL-TIME TRAFFIC SIGNAL CONTROL SYSTEM: ARCHITECTURE, ALGORITHMS, AND ANALYSIS , 2001 .

[8]  Ana L. C. Bazzan,et al.  Opportunities for multiagent systems and multiagent reinforcement learning in traffic control , 2009, Autonomous Agents and Multi-Agent Systems.

[9]  D I Robertson,et al.  User guide to TRANSYT version 6 , 1976 .

[10]  E. Mamdani,et al.  FUZZY LOGIC CONTROLLER FOR A TRAFFIC JUNCTION , 1977 .

[11]  Dipti Srinivasan,et al.  Neural Networks for Real-Time Traffic Signal Control , 2006, IEEE Transactions on Intelligent Transportation Systems.

[12]  Yetis Sazi Murat,et al.  A FUZZY LOGIC MULTI-PHASED SIGNAL CONTROL MODEL FOR ISOLATED JUNCTIONS , 2005 .

[13]  D I Robertson,et al.  TRANSYT: A TRAFFIC NETWORK STUDY TOOL , 1969 .

[14]  Fernando Gomide,et al.  Fuzzy traffic control: adaptive strategies , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[15]  Baher Abdulhai,et al.  Multiagent Reinforcement Learning for Integrated Network of Adaptive Traffic Signal Controllers (MARLIN-ATSC): Methodology and Large-Scale Application on Downtown Toronto , 2013, IEEE Transactions on Intelligent Transportation Systems.

[16]  C. P. Pappis,et al.  A Fuzzy Logic Controller for a Trafc Junction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Nathan H Gartner.  Demand-Responsive Decentralized Urban Traffic Control. Part I: Single-Intersection Policies , 1982 .

[18]  Monireh Abdoos,et al.  Holonic multi-agent system for traffic signals control , 2013, Eng. Appl. Artif. Intell..

[19]  N H Gartner,et al.  DEMAND-RESPONSIVE DECENTRALIZED URBAN TRAFFIC CONTROL. PART 2. NETWORK EXTENSIONS , 1983 .

[20]  Faridoon Shabaninia,et al.  Type-2 fuzzy multiagent traffic signal control , 2012, 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI).

[21]  Peter Thomas,et al.  Traffic signal control using fuzzy logic and evolutionary algorithms , 2007, 2007 IEEE Congress on Evolutionary Computation.

[22]  D. C. Chin,et al.  Traffic-responsive signal timing for system-wide traffic control , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[23]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[24]  Chen Cai,et al.  Adaptive traffic signal control using approximate dynamic programming , 2009 .

[25]  S. Chand,et al.  Self-organizing traffic control via fuzzy logic , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[26]  Wu Wei,et al.  Traffic signal control using fuzzy logic and MOGA , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[27]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[28]  B.M. Nair,et al.  A fuzzy Logic Controller for Isolated Signalized Intersection with Traffic Abnormality Considered , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[29]  M. Steingröver.  Reinforcement Learning of Traffic Light Controllers under Partial Observability , 2007 .

[30]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..