Modeling and analyses for an extended car-following model accounting for drivers' situation awareness from cyber physical perspective

被引:14
作者
Chen, Dong [1 ,2 ]
Sun, Dihua [1 ,2 ]
Zhao, Min [1 ,2 ]
Zhou, Tong [3 ]
Cheng, Senlin [2 ]
机构
[1] Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China
[2] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China
[3] Chongqing Vocat Inst Engn, Coll Informat Engn, Chongqing 402260, Peoples R China
基金
中国国家自然科学基金;
关键词
Car-following model; Situation awareness; Cyber physical process; Stability; mKdV equation; Traffic jams; LATTICE HYDRODYNAMIC MODEL; VELOCITY DIFFERENCE MODEL; TRAFFIC FLOW MODEL; STABILITY ANALYSIS; DRIVING BEHAVIOR; CONTINUUM MODEL; VEHICLE SYSTEMS; DYNAMICS; EQUATION; FEEDBACK;
D O I
10.1016/j.physa.2018.02.125
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In fact, driving process is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. Meanwhile, the drivers have situation awareness in driving process, which is not only ascribed to the current traffic states, but also extrapolates the changing trend. In this paper, an extended car-following model is proposed to account for drivers' situation awareness. The stability criterion of the proposed model is derived via linear stability analysis. The results show that the stable region of proposed model will be enlarged on the phase diagram compared with previous models. By employing the reductive perturbation method, the modified Korteweg de Vries (mKdV) equation is obtained. The kink-antikink soliton of mKdV equation reveals theoretically the evolution of traffic jams. Numerical simulations are conducted to verify the analytical results. Two typical traffic Scenarios are investigated. The simulation results demonstrate that drivers' situation awareness plays a key role in traffic flow oscillations and the congestion transition. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:52 / 68
页数:17
相关论文
共 46 条
[1]   Bifurcation analysis of a speed gradient continuum traffic flow model [J].
Ai, Wen-Huan ;
Shi, Zhong-Ke ;
Liu, Da-Wei .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2015, 437 :418-429
[2]   DYNAMICAL MODEL OF TRAFFIC CONGESTION AND NUMERICAL-SIMULATION [J].
BANDO, M ;
HASEBE, K ;
NAKAYAMA, A ;
SHIBATA, A ;
SUGIYAMA, Y .
PHYSICAL REVIEW E, 1995, 51 (02) :1035-1042
[3]   Optimization and Control of Cyber-Physical Vehicle Systems [J].
Bradley, Justin M. ;
Atkins, Ella M. .
SENSORS, 2015, 15 (09) :23020-23049
[4]   An extended macro traffic flow model accounting for multiple optimal velocity functions with different probabilities [J].
Cheng, Rongjun ;
Ge, Hongxia ;
Wang, Jufeng .
PHYSICS LETTERS A, 2017, 381 (32) :2608-2620
[5]   KdV-Burgers equation in a new continuum model based on full velocity difference model considering anticipation effect [J].
Cheng Rongjun ;
Ge Hongxia ;
Wang Jufeng .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 481 :52-59
[6]   Requiem for second-order fluid approximations of traffic flow [J].
Daganzo, CF .
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 1995, 29 (04) :277-286
[7]   An Observer/Predictor-Based Model of the User for Attaining Situation Awareness [J].
Eskandari, Neda ;
Dumont, Guy A. ;
Wang, Z. Jane .
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 2016, 46 (02) :279-290
[8]  
Gang Xiong, 2015, IEEE/CAA Journal of Automatica Sinica, V2, P320
[9]   The time-dependent Ginzburg-Landau equation for car-following model considering anticipation-driving behavior [J].
Ge, Hong-Xia ;
Lv, Feng ;
Zheng, Peng-Jun ;
Cheng, Rong-Jun .
NONLINEAR DYNAMICS, 2014, 76 (02) :1497-1501
[10]   Dynamics of connected vehicle systems with delayed acceleration feedback [J].
Ge, Jin I. ;
Orosz, Gabor .
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2014, 46 :46-64