The effects of the control and prediction horizons on the urban traffic regulation

The paper studies the effects of control and prediction horizons on a multimodal traffic regulation strategy. The strategy acts on traffic lights in order to regulate Private Vehicle (PV) traffic by minimizing their number in the network and to regulate the Public Transport (PT) traffic by maximizing their schedule adherence. The strategy architecture is based on an automatic closed loop and uses the principles of the General Model predictive Control (GMPC). The GMPC defines two blocs : the prediction bloc and the optimizer. The predictor is based on two models belonging to PV and PT traffic. These two models balance simplicity and accuracy. The optimizer is based on the Particle Swarm Optimization, a powerful metaheuristic, that resolves a bi-objective optimization problem. The problem, as it is formulated within GMPC architecture, evolves control and prediction horizons. The simulation study focuses on these two important parameters and their effects on the traffic regulation efficiency.

[1]  N. Bhouri,et al.  Régulation multimodale du trafic routier et des transports en commun de surface : Une classification des méthodes , 2008 .

[2]  Frans van den Bergh,et al.  An analysis of particle swarm optimizers , 2002 .

[3]  Markos Papageorgiou,et al.  A Multivariable Regulator Approach to Traffic-Responsive Network-Wide Signal Control , 2000 .

[4]  Philip J Tarnoff,et al.  EVALUATION OF OPTIMIZED POLICIES FOR ADAPTIVE CONTROL STRATEGY , 1991 .

[5]  J J Henry,et al.  P.T. PRIORITY AND PRODYN , 1994 .

[6]  M. Papageorgiou,et al.  Control and Optimization Methods for Traffic Signal Control in Large-scale Congested Urban Road Networks , 2007, 2007 American Control Conference.

[7]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[8]  Neila Bhouri,et al.  A multimodal traffic responsive strategy using particle swarm optimization , 2009, CTS 2009.

[9]  H. Michalska,et al.  Receding horizon control of nonlinear systems , 1988, Proceedings of the 28th IEEE Conference on Decision and Control,.

[10]  Markos Papageorgiou,et al.  Store-and-forward based methods for the signal control problem in large-scale congested urban road networks , 2009 .

[11]  R D Bretherton,et al.  THE SCOOT ON-LINE TRAFFIC SIGNAL OPTIMISATION TECHNIQUE , 1982 .

[12]  Alexander Skabardonis,et al.  Control Strategies for Transit Priority , 1998 .

[13]  W Chen,et al.  A NEW APPROACH FOR BUS PRIORITY AT SIGNALISED INTERSECTIONS , 1998 .

[14]  D. Gazis.  Optimum Control of a System of Oversaturated Intersections , 1964 .

[15]  Neila Bhouri,et al.  A multimodal model for an urban traffic control policy , 2008 .

[16]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[17]  Sophie Midenet,et al.  The real-time urban traffic control system CRONOS: Algorithm and experiments , 2006 .

[18]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[19]  R B Potts,et al.  THE OVERSATURATED INTERSECTION , 1963 .