An improved car-following model considering electronic throttle dynamics and delayed velocity difference

Abstract With consideration of the effect of the electronic throttle dynamics, an improved car-following model is constructed in this paper. In the new model, the control signals including the opening angle difference of electronic throttle and the delayed velocity difference are introduced. Moreover, for the sake of confirming the impact of the electronic throttle opening angle difference and the delayed velocity difference, the new model is discussed through theoretical analytical and numerical simulation methods. The conditions of the stability for the proposed model are obtained through control theory method. The results of numerical simulation experiments corroborate that the traffic system has greater stability when the control signals proposed are taken into account and it is benignant to integrate the dynamics of electronic throttle into the traffic flow model to enhance the stability of traffic system.

[1]  S. Ilgin Guler,et al.  Using connected vehicle technology to improve the efficiency of intersections , 2014 .

[2]  Ziyou Gao,et al.  A control method for congested traffic induced by bottlenecks in the coupled map car-following model , 2006 .

[3]  Zhu Wen-xing,et al.  A new car-following model for autonomous vehicles flow with mean expected velocity field , 2018 .

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

[5]  Wang Hao,et al.  A car-following model with the consideration of vehicle-to-vehicle communication technology , 2016 .

[6]  Hongxia Ge,et al.  Two velocity difference model for a car following theory , 2008 .

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

[8]  Ge Hong-Xia,et al.  A modified coupled map car-following model based on application of intelligent transportation system and control of traffic congestion , 2007 .

[9]  H. Ge,et al.  Effects of speed deviation and density difference in traffic lattice hydrodynamic model with interruption , 2018, Physica A: Statistical Mechanics and its Applications.

[10]  Rongjun Cheng,et al.  Nonlinear analysis of an improved continuum model considering mean-field velocity difference , 2019, Physics Letters A.

[11]  Ge Hong-Xia,et al.  The nonlinear analysis for a new continuum model considering anticipation and traffic jerk effect , 2018 .

[12]  张辉,et al.  A control method for congested traffic in the coupled map car-following model , 2009 .

[13]  Tie-Qiao Tang,et al.  Some features of car-following behaviour in the vicinity of signalised intersection and how to model them , 2019 .

[14]  Xue Yu,et al.  The Effect of the Relative Velocity on Traffic Flow , 2002 .

[15]  Xia Wu,et al.  Effects of the prevision relative velocity on traffic dynamics in the ACC strategy , 2019 .

[16]  Rongjun Cheng,et al.  An extended car-following model considering driver’s memory and average speed of preceding vehicles with control strategy , 2019, Physica A: Statistical Mechanics and its Applications.

[17]  H. M. Zhang,et al.  Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model , 2017 .

[18]  Jian Zhang,et al.  Modeling electric bicycle’s lane-changing and retrograde behaviors , 2018 .

[19]  H. Ge,et al.  Analysis of a novel lattice hydrodynamic model considering predictive effect and flow integral , 2019, Physica A: Statistical Mechanics and its Applications.

[20]  Wen-Xing Zhu,et al.  Study on braking process of vehicles at the signalized intersection based on car-following theory , 2019 .

[21]  K Konishi,et al.  Coupled map car-following model and its delayed-feedback control. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[22]  Tie-Qiao Tang,et al.  A speed guidance model accounting for the driver’s bounded rationality at a signalized intersection , 2017 .

[23]  Hai-Jun Huang,et al.  A new macro model with consideration of the traffic interruption probability , 2008 .

[24]  Rongjun-Cheng,et al.  Mean-field flow difference model with consideration of on-ramp and off-ramp , 2019, Physica A: Statistical Mechanics and its Applications.

[25]  G. F. Newell.  Nonlinear Effects in the Dynamics of Car Following , 1961 .

[26]  Du Jun,et al.  A compound compensation method for car-following model , 2016, Commun. Nonlinear Sci. Numer. Simul..

[27]  Jian Zhang,et al.  A speed guidance strategy for multiple signalized intersections based on car-following model , 2018 .

[28]  Rongjun Cheng,et al.  An extended continuum model accounting for the driver's timid and aggressive attributions , 2017 .

[29]  Hongxia Ge,et al.  An improved lattice hydrodynamic model accounting for the effect of “backward looking” and flow integral , 2019, Physica A: Statistical Mechanics and its Applications.

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

[31]  Rongjun Cheng,et al.  KdV–Burgers equation in a new continuum model based on full velocity difference model considering anticipation effect , 2017 .

[32]  Rongjun Cheng,et al.  An extended macro traffic flow model accounting for multiple optimal velocity functions with different probabilities , 2017 .

[33]  Rongjun Cheng,et al.  An extended car-following model under V2V communication environment and its delayed-feedback control , 2018, Physica A: Statistical Mechanics and its Applications.

[34]  Wen-Xing Zhu,et al.  Analysis of feedback control scheme on discrete car-following system , 2018, Physica A: Statistical Mechanics and its Applications.

[35]  Rongjun Cheng,et al.  An extended lattice hydrodynamic model considering the delayed feedback control on a curved road , 2019, Physica A: Statistical Mechanics and its Applications.

[36]  Rongjun Cheng,et al.  An improved lattice hydrodynamic model considering the “backward looking” effect and the traffic interruption probability , 2018 .

[37]  Hao Yang,et al.  A control theoretic formulation of green driving strategies based on inter-vehicle communications , 2014 .

[38]  Arvind Kumar Gupta,et al.  Delayed-feedback control in a Lattice hydrodynamic model , 2015, Commun. Nonlinear Sci. Numer. Simul..

[39]  R. Jiang,et al.  A new continuum model for traffic flow and numerical tests , 2002 .

[40]  Ge Hong-Xia,et al.  An extended continuum model considering optimal velocity change with memory and numerical tests , 2018 .

[41]  Hai-Jun Huang,et al.  Macroscopic modeling of lane‐changing for two‐lane traffic flow , 2009 .

[42]  Rongjun Cheng,et al.  Nonlinear density wave investigation for an extended car-following model considering driver’s memory and jerk , 2018 .

[43]  Hai-Jun Huang,et al.  Influences of the driver’s bounded rationality on micro driving behavior, fuel consumption and emissions , 2015 .

[44]  Taixiong Zheng,et al.  Non-lane-discipline-based car-following model incorporating the electronic throttle dynamics under connected environment , 2017 .

[45]  Gábor Orosz,et al.  Dynamics of connected vehicle systems with delayed acceleration feedback , 2014 .

[46]  Jian Zhang,et al.  A cellular automation model accounting for bicycle’s group behavior , 2018 .

[47]  Tie-Qiao Tang,et al.  An extended two-lane car-following model accounting for inter-vehicle communication , 2018 .

[48]  Xue Yu.  A car-following model with stochastically considering the relative velocity in a traffic flow , 2003 .

[49]  Wen-Xing Zhu,et al.  Analysis of car-following model with cascade compensation strategy , 2016 .