Analysis of the predictive effect and feedback control in an extended lattice hydrodynamic model

This paper aims to put forward an extended lattice hydrodynamic model, explore its effects on alleviating traffic congestion and provide theoretical basis for traffic management departments and traffic engineering implementation departments.,The control method is applied to study the stability of the new model. Through nonlinear analysis, the mKdV equation representing kink-antikink soliton is acquired.,The predictive effect and the control signal can enhance the traffic flow stability and reduce the energy consumption.,The predictive effect and feedback control are first considered in lattice hydrodynamic model simultaneously. Numerical simulations demonstrate that these two factors can enhance the traffic flow stability.

[1]  Takashi Nagatani,et al.  TDGL and MKdV equations for jamming transition in the lattice models of traffic , 1999 .

[2]  G. Peng,et al.  Research on the stabilization effect of continuous self-delayed traffic flux in macro traffic modeling , 2019, Physica A: Statistical Mechanics and its Applications.

[3]  Yu Cui,et al.  The control method for the lattice hydrodynamic model , 2015, Commun. Nonlinear Sci. Numer. Simul..

[4]  Rongjun Cheng,et al.  An extended lattice hydrodynamic model considering the driver’s sensory memory and delayed-feedback control , 2019, Physica A: Statistical Mechanics and its Applications.

[5]  Wei Zhang,et al.  Path optimization of taxi carpooling , 2018, PloS one.

[6]  Jufeng Wang,et al.  An improved lattice hydrodynamic model considering the driver’s desire of driving smoothly , 2019 .

[7]  Alfred Ramani,et al.  Epidemic dynamics: discrete-time and cellular automaton models , 2003 .

[8]  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.

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

[10]  Guanghan Peng,et al.  A novel lattice hydrodynamic model considering the optimal estimation of flux difference effect on two-lane highway , 2018, Physica A: Statistical Mechanics and its Applications.

[11]  Qi Wei,et al.  An extended car-following model considering random safety distance with different probabilities , 2018 .

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

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

[14]  Ramanpreet Kaur,et al.  Analyses of a heterogeneous lattice hydrodynamic model with low and high-sensitivity vehicles , 2018, Physics Letters A.

[15]  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.

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

[17]  Geng Zhang,et al.  Analysis of two-lane lattice hydrodynamic model with consideration of drivers’ characteristics , 2015 .

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

[19]  Ge Hong-Xia,et al.  A Lattice Model for Bidirectional Pedestrian Flow on Gradient Road , 2014 .

[20]  Siuming Lo,et al.  An extended car-following model accounting for the average headway effect in intelligent transportation system , 2017 .

[21]  Changxi Ma,et al.  Distribution path robust optimization of electric vehicle with multiple distribution centers , 2018, PloS one.

[22]  Yu Zhang,et al.  Study on the continuous delayed optimal flow on traffic stability in a new macro traffic model , 2019 .

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

[24]  Liang Chen,et al.  Analysis of the trip costs of a traffic corridor with two entrances and one exit under car-following model , 2017 .

[25]  Guanghan Peng,et al.  A new lattice model of traffic flow with the consideration of individual difference of anticipation driving behavior , 2013, Commun. Nonlinear Sci. Numer. Simul..

[26]  Guanghan Peng,et al.  A new lattice model of traffic flow considering driver's anticipation effect of the traffic interruption probability , 2018 .

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

[28]  Jufeng Wang,et al.  Effect of the driver’s desire for smooth driving on the car-following model , 2018, Physica A: Statistical Mechanics and its Applications.

[29]  Takashi Nagatani,et al.  Modified KdV equation for jamming transition in the continuum models of traffic , 1998 .

[30]  A. Gupta,et al.  Analyses of driver’s anticipation effect in sensing relative flux in a new lattice model for two-lane traffic system , 2013 .

[31]  Changxi Ma,et al.  Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment , 2018, IEEE Access.

[32]  Dong Chen,et al.  Stability analysis of a new lattice hydrodynamic model by considering lattice’s self-anticipative density effect , 2017 .

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

[34]  Zhu Hui-bing,et al.  Lattice models of traffic flow considering drivers' delay in response , 2009 .

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

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

[37]  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.

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

[39]  Arvind Kumar Gupta,et al.  Analysis of a modified two-lane lattice model by considering the density difference effect , 2014, Commun. Nonlinear Sci. Numer. Simul..

[40]  Rongjun Cheng,et al.  A new lattice hydrodynamic model based on control method considering the flux change rate and delay feedback signal , 2018 .

[41]  Hai-Jun Huang,et al.  A route-based traffic flow model accounting for interruption factors , 2019, Physica A: Statistical Mechanics and its Applications.

[42]  Tie-Qiao Tang,et al.  Effects of signal light on the fuel consumption and emissions under car-following model , 2017 .

[43]  Sapna Sharma,et al.  Lattice hydrodynamic modeling of two-lane traffic flow with timid and aggressive driving behavior , 2015 .

[44]  Tianlong Gu,et al.  Lattice hydrodynamic modeling of traffic flow with consideration of historical current integration effect , 2018, Physica A: Statistical Mechanics and its Applications.

[45]  Liang Chen,et al.  Analysis of trip cost allowing late arrival in a traffic corridor with one entry and one exit under car-following model , 2019 .

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

[47]  Changxi Ma,et al.  Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm , 2018, PloS one.