TDGL and mKdV equations for an extended car-following model with the consideration of driver's memory

被引：16
|
作者：
Yongjiang-Wang [1 ,2 ,3 ]
Han-Song [1 ,2 ,3 ]
Rongjun-Cheng [1 ]
机构：
[1] Ningbo Univ, Fac Maritime & Transportat, Ningbo 315211, Zhejiang, Peoples R China
[2] Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Nanjing 210096, Jiangsu, Peoples R China
[3] Ningbo Univ, Natl Traff Management Engn & Technol Res Ctr, Subctr, Ningbo 315211, Zhejiang, Peoples R China
来源：
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2019年 / 515卷
基金：
中国国家自然科学基金;
关键词：
Car-following model; Traffic flow; Energy consumption; Memory effect; VELOCITY DIFFERENCE MODEL; TRAFFIC FLOW; BOUNDED RATIONALITY; JAMMING TRANSITION; FUEL CONSUMPTION; CONTINUUM MODEL; LATTICE MODEL; VEHICLES; CONGESTION; STABILITY;
D O I：
10.1016/j.physa.2018.09.171
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This paper presents an extended car-following model by taking driver's memory and the full velocity difference into the original optimal velocity model (OVM). The stability condition of this model is obtained by using linear stability theory. The TDGL equation and the mKdV equation are derived from nonlinear analysis. The energy consumption of new car-following models considering the driver's memory is discussed. Furthermore, the new car-following model is investigated in detail by numerical methods. Both analytical and simulation results show that the extended following car-following model will not only suppress the traffic congestion but also reduce energy consumption. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：440 / 449
页数：10
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