Towards the bi-directional cellular automaton model with perception ranges

The traditional cellular automaton (CA) model assumes that drivers only receive information from the preceding vehicles, e.g. the brake light information. However, in reality, drivers not only perceive information from downstream but can also get upstream information, e.g. the honk stimulation. The CA model involving traffic information from downstream and upstream is called the bi-directional CA model here. Meanwhile, with the introduction of Connected Vehicle Technologies, the perception range of drivers is expected to significantly increase which can lead to more informed driving behavior. Such an impact cannot be easily modeled by traditional one-directional CA models. In this study, the perception ranges of both the brake light effect and honk stimulation are introduced into the bi-directional CA model. Fundamental diagrams and spatial–temporal diagrams are then analyzed and two methods, i.e. the traffic flow interruption effect and microscopic analysis of time series data, are utilized to distinguish the synchronized traffic flow. Further numerical results illustrate that the perception range and slow-to-start sensitivity threshold are two important factors to reproduce the synchronized flow, and consideration of the honk information and the larger perception range both benefit the stability of traffic flow, which implies the potential significance of the application of Connected Vehicle Technologies.

[1]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  Dihua Sun,et al.  Effect of looking backward on traffic flow in an extended multiple car-following model , 2011 .

[3]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[4]  Mu-Ren Liu,et al.  The Effect of Noise-First and Anticipation Headway on Traffic Flow , 2007 .

[5]  L. A. Pipes.  An Operational Analysis of Traffic Dynamics , 1953 .

[6]  B. Kerner.  EXPERIMENTAL FEATURES OF SELF-ORGANIZATION IN TRAFFIC FLOW , 1998 .

[7]  Robert Herman,et al.  Traffic Dynamics: Analysis of Stability in Car Following , 1959 .

[8]  Liang Zheng,et al.  Analysis of honk effect on the traffic flow in a cellular automaton model , 2011 .

[9]  Boris S. Kerner,et al.  Criterion for traffic phases in single vehicle data and empirical test of a microscopic three-phase traffic theory , 2006 .

[10]  Serge P. Hoogendoorn,et al.  New Estimation Technique for Vehicle-Type-Specific Headway Distributions , 1998 .

[11]  A. Schadschneider,et al.  Single-vehicle data of highway traffic: a statistical analysis. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Nagel.  Particle hopping models and traffic flow theory. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

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

[14]  Akihiro Nakayama,et al.  Dynamical model of a cooperative driving system for freeway traffic. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  R. Jiang,et al.  Full velocity difference model for a car-following theory. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  B. Kerner,et al.  EXPERIMENTAL PROPERTIES OF PHASE TRANSITIONS IN TRAFFIC FLOW , 1997 .

[17]  R. Sollacher,et al.  Multi-anticipative car-following model , 1999 .

[18]  C. Daganzo.  THE CELL TRANSMISSION MODEL.. , 1994 .

[19]  Dirk Helbing.  Modeling multi-lane traffic flow with queuing effects , 1997 .

[20]  M. E. Lárraga,et al.  Cellular automata for one-lane traffic flow modeling , 2005 .

[21]  Rui Jiang,et al.  Cellular automata models for synchronized traffic flow , 2003 .

[22]  H. X. Ge,et al.  Effect of looking backward on traffic flow in a cooperative driving car following model , 2006 .

[23]  Kai Nagel,et al.  From Particle Hopping Models to Traffic Flow Theory , 1998 .

[24]  Akihiro Nakayama,et al.  Effect of looking at the car that follows in an optimal velocity model of traffic flow. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Neil F. Johnson,et al.  Cellular automata models of traffic flow along a highway containing a junction , 1996 .

[26]  X Li,et al.  Cellular automaton model considering the velocity effect of a car on the successive car. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.