Effects of prediction feedback in multi-route intelligent traffic systems ☆

We first study the influence of an efficient feedback strategy named the prediction feedback strategy (PFS) based on a multi-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. In this scenario, our model incorporates the effects of adaptability into the cellular automaton models of traffic flow. Simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux. At the end of this paper, we also discuss in what situation PFS will become invalid in multi-route systems.

[1]  André de Palma,et al.  Does providing information to drivers reduce traffic congestion , 1991 .

[2]  Helbing Gas-kinetic derivation of Navier-Stokes-like traffic equations. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[3]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[4]  Terry L. Friesz,et al.  Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control Problem , 1989, Oper. Res..

[5]  Schürmann,et al.  Second-order continuum traffic flow model. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  T. Nagatani The physics of traffic jams , 2002 .

[7]  Moshe Ben-Akiva,et al.  Dynamic network models and driver information systems , 1991 .

[8]  Hani S. Mahmassani,et al.  System performance and user response under real-time information in a congested traffic corridor , 1991 .

[9]  Michael Schreckenberg,et al.  A cellular automaton model for freeway traffic , 1992 .

[10]  Kaan Ozbay,et al.  FUZZY FEEDBACK CONTROL FOR REAL-TIME DYNAMIC TRAFFIC ROUTING: USER EQUILIBRIUM MODEL FORMULATIONS AND CONTROLLER DESIGN , 1996 .

[11]  S. L. Paveri-Fontana,et al.  On Boltzmann-like treatments for traffic flow: A critical review of the basic model and an alternative proposal for dilute traffic analysis , 1975 .

[12]  Lehmann Distribution function properties and the fundamental diagram in kinetic traffic flow theory. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  D. Helbing,et al.  Gas-Kinetic-Based Traffic Model Explaining Observed Hysteretic Phase Transition , 1998, cond-mat/9810277.

[14]  Middleton,et al.  Self-organization and a dynamical transition in traffic-flow models. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[15]  Yasushi Yokoya Dynamics of traffic flow with real-time traffic information. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Xiao-Yan Sun,et al.  Prediction feedback in intelligent traffic systems , 2009 .

[17]  Pak Ming Hui,et al.  Effects of Announcing Global Information in a Two-Route Traffic Flow Model , 2001 .

[18]  I. Prigogine,et al.  A Boltzmann-Like Approach for Traffic Flow , 1960 .

[19]  A. Schadschneider,et al.  Statistical physics of vehicular traffic and some related systems , 2000, cond-mat/0007053.

[20]  H Lieu,et al.  TRAFFIC-FLOW THEORY , 1999 .

[21]  Dirk Helbing Structure and Instability of High-Density Equations for Traffic Flow , 1998 .

[22]  F. Kluegl,et al.  Decision dynamics in a traffic scenario , 2000 .

[23]  Tao Zhou,et al.  Advanced information feedback in intelligent traffic systems. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.