|Table of Contents|

Research progress on car-following models(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年05期
Page:
125-138
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Research progress on car-following models
Author(s):
YANG Long-hai1;  ZHANG Chun2;  QIU Xiao-yun1;  LI Shuai1;  WANG Hui1
(1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang,China; 2. Shenzhen Urban Transport Planning Center, Shenzhen 518057, Guangdong, China)
Keywords:
traffic information;  car-following model;  theory-driven model;  data-driven model;  human factor
PACS:
U491.2
DOI:
-
Abstract:
The researches on the car-following models in the past 70 years were reviewed. According to the modeling methods, car-following models were divided into two types: theory-driven model and data-driven model, and the hotspots were summarized. The theory-driven car-following model was reviewed from five aspects: human factor, infrastructure, traffic information, heterogeneous traffic flow, and new modeling theory. According to different machine learning algorithms, the data-driven car-following model was also reviewed from five aspects: fuzzy logic, artificial neural network, instance learning, support vector regression, and deep learning. Analysis result shows that the theory-driven car-following model can theoretically deduces the traffic phenomenon. But it is difficult to comprehensively consider the influencing factors, and some human factors are difficult to quantify, and the explanation of driver decision-making process is not accurate enough. The car-following model of heterogeneous traffic flow lacks effective theoretical basis and formal proof under general traffic conditions. The data-driven car-following models summarize the traffic rules by traffic phenomenon. Due to different of data sources, evaluation indicators and methods, the models based on machine learning algorithms cannot be systematically compared. The data-driven models focuse on micro-angles to study driving behavior characteristics, but are not very explanatory for complex traffic phenomena(such as traffic oscillation, hysteresis, etc.). The research of the car-following models should innovate the data collection method, and capture the drivers' psychological tendencies, perceptual characteristics and cognitive abilities, as well as quantify the influence of human factors and make full use of big data. The data-driven car-following models should provide technical support for the development of driverless technology. Before the automatic driving is fully popularized, the characteristics of drivers' car-following behaviors in the mixed scene of manual driving and automatic driving need to be further studied. 4 figs, 83 refs.

References:

[1] REUSCHEL A. Vehicle movements in the column uniformly accelerated or delayed[J]. Oesterreich IngrArch, 1950, 4: 193-215.
[2] PIPES L A. An operational analysis of traffic dynamics[J]. Journal of Applied Physics, 1953, 24(3): 274-281.
[3] BRACKSTONE M, MCDONALD M. Car-following: a historical review[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 1999, 2(4): 181-196.
[4] NAGEL K, SCHRECKENBERG M. A cellular automaton model for freeway traffic[J]. Journal de Physique I, 1992, 2(12): 2221-2229.
[5] FUKUI M, ISHIBASHI Y. Traffic flow in 1D cellular automaton model including cars moving with high speed[J]. Journal of the Physical Society of Japan, 1996, 65(6): 1868-1870.
[6] KRAUSS S, WAGNER P, GAWRON C. Continuous limit of the Nagel-Schreckenberg model[J]. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 1996, 54(4): 3707-3712.
[7] KRAUSS S, WAGNER P, GAWRON C. Metastable states in a microscopic model of traffic flow[J]. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 1997, 55(5): 5597-5602.
[8] BARLOVIC R, SANTEN L, SCHADSCHNEIDER A, et al. Metastable states in cellular automata for traffic flow[J]. European Physical Journal B, 1998, 5(3): 793-800.
[9] HOOGENDOORN S P, BOVY P H L. State-of-the-art of vehicular traffic flow modelling[J]. Proceedings of the Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 2001, 215(4): 283-303.
[10] OLSTAM J J, TAPANI A. Comparison of car-following
models[R]. Stockholm: Swedish National Road and Transport Research Institute, 2004.
[11] PANWAI S, DIA H. Comparative evaluation of microscopic car-following behavior[J]. IEEE Transactions on Intelligent Transportation Systems, 2005, 6(3): 314-325.
[12] TOLEDO T. Driving behaviour: models and challenges[J]. Transport Reviews, 2007, 27(1): 65-84.
[13] 王殿海,金 盛.车辆跟驰行为建模的回顾与展望[J].中国公路学报,2012,25(1):115-127.
WANG Dian-hai, JIN Sheng. Review and outlook of modeling of car following behavior[J]. China Journal of Highway and Transport, 2012, 25(1): 115-127.(in Chinese)
[14] SAIFUZZAMAN M, ZHENG Zu-duo. Incorporating human-factors in car-following models: a review of recent developments and research needs[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 379-403.
[15] WAHLE J, NEUBERT L, SCHRECKENBERG M. Modeling and simulation of traffic flow[J]. Computer Physics Communications, 1999, 121/122: 402-405.
[16] FRITZSCHE H T. A model for traffic simulation[J]. Traffic Engineering and Control, 1994, 35(5): 317-321.
[17] FANCHER P S, BAREKET Z. Evolving model for studying driver-vehicle system performance in longitudinal control of headway[J]. Transportation Research Record, 1998, 1631: 13-19.
[18] MICHAELS R M, COZAN L W. Perceptual and field factors causing lateral displacement[J]. Highway Research Record, 1963, 25: 1-13.
[19] GRAY R, REGAN D. Accuracy of estimating time to collision using binocular and monocular information[J]. Vision Research, 1998, 38(4): 499-512.
[20] ANDERSEN G J, SAUER C W. Optical information for car following: the driving by visual angle(DVA)model[J]. Human Factors, 2007, 49(5): 878-896.
[21] HAMDAR S H, TREIBER M, MAHMASSANI H S, et al. Modeling driver behavior as sequential risk-taking task[J]. Transportation Research Record, 2008(2088): 208-217.
[22] HAMDAR S H, MAHMASSANI H S, TREIBER M. From behavioral psychology to acceleration modeling: calibration, validation, and exploration of drivers' cognitive and safety parameters in a risk-taking environment[J]. Transportation Research Part B: Methodological, 2015, 78: 32-53.
[23] TALEBPOUR A, MAHMASSANI H S, HAMDAR S H.
Multiregime sequential risk-taking model of car-following behavior: specification, calibration, and sensitivity analysis[J]. Transportation Research Record, 2016(2260): 60-66.
[24] STANTON N A, SALMON P M. Human error taxonomies applied to driving: a generic driver error taxonomy and its implications for intelligent transport systems[J]. Safety Science, 2009, 47(2): 227-237.
[25] YOUNG K L, SALMON P M. Examining the relationship
between driver distraction and driving errors: a discussion of theory, studies and methods[J]. Safety Science, 2012, 50(2): 165-174.
[26] PRZYBYLA J, TAYLOR J, JUPE J, et al. Simplified, data-driven, errorable car-following model to predict the safety effects of distracted driving[C]∥IEEE. 2012 15th International IEEE Conference on Intelligent Transportation Systems. New York: IEEE, 2012: 1149-1154.
[27] CHEN Dan-jue, LAVAL J, ZHENG Zu-duo, et al. A behavioral car-following model that captures traffic oscillations[J]. Transportation Research Part B: Methodological, 2012, 46(6): 744-761.
[28] YANG H H, PENG H. Development of an errorable car-following driver model[J]. Vehicle System Dynamics, 2010, 48(6): 751-773.
[29] FULLER R. The task-capability interface model of the driving
process[J]. Recherche Transports Sécurité, 2000, 66: 47-57.
[30] FULLER R. Towards a general theory of driver behaviour[J]. Accident Analysis and Prevention, 2005, 37(3): 461-472.
[31] SAIFUZZAMAN M, ZHENG Zu-duo, MAZHARUL HAQUE M, et al. Revisiting the task-capability interface model for incorporating human factors into car-following models[J]. Transportation Research Part B: Methodological, 2015, 82: 1-19.
[32] TANG Tie-qiao, WANG Yun-peng, YANG Xiao-bao, et al. A new car-following model accounting for varying road condition[J]. Nonlinear Dynamics, 2012, 70(2): 1397-1405.
[33] TANG T Q, LI J G, HUANG H J, et al. A car-following model with real-time road conditions and numerical tests[J]. Measurement: Journal of the International Measurement Confederation, 2014, 48: 63-76.
[34] YANG Long-hai, ZHANG Xi-qiao, GONG Jie-kun, et al. The research of car- following model based on real-time maximum deceleration[J]. Mathematical Problems in Engineering, 2015, 2015: 1-9.
[35] LI Chuan-yao, TANG Tie-qiao, HUANG Hai-jun, et al. A new car-following model with consideration of driving resistance[J]. Chinese Physics Letters, 2011, 28(3): 38902-1-4.
[36] JIN Sheng, WANG Dian-hai, TAO Peng-fei, et al. Non-
lane-based full velocity difference car following model[J]. Physica A: Statistical Mechanics and its Applications, 2010, 389(21): 4654-4662.
[37] 梁玉娟,薛 郁.道路弯道对交通流影响的研究[J].物理学报,2010,59(8):5325-5331.
LIANG Yu-juan, XUE Yu. Study on traffic flow affected by the road turning[J]. Acta Physica Sinica, 2010, 59(8): 5325-5331.(in Chinese)
[38] ZHANG H M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model[J]. Transportation Research Part B: Methodological, 2003, 37(1): 27-41.
[39] TANG T Q, HUANG H J, ZHAO S G, et al. An extended OV model with consideration of driver's memory[J]. International Journal of Modern Physics B, 2009, 23(5): 743-752.
[40] TANG T Q, LI C Y, HUANG H J. A new car-following model with the consideration of the driver's forecast effect[J]. Physics Letters A, 2010, 374(38): 3951-3956.
[41] 秦严严,王 昊,王 炜,等.自适应巡航控制车辆跟驰模型综述[J].交通运输工程学报,2017,17(3):121-130.
QIN Yan-yan, WANG Hao, WANG Wei, et al. Review of car-following models of adaptive cruise control[J]. Journal of Traffic and Transportation Engineering, 2017, 17(3): 121-130.(in Chinese)
[42] 华雪东,王 炜,王 昊.考虑车与车互联通讯技术的交通流跟驰模型[J].物理学报,2016,65(1):44-55.
HUA Xue-dong, WANG Wei, WANG Hao. A car-following model with the consideration of vehicle-to-vehicle communication technology[J]. Acta Physica Sinica, 2016, 65(1): 44-55.(in Chinese)
[43] WANG Tao, ZHAO Jing, LI Peng. An extended car-following model at un-signalized intersections under V2V communication environment[J]. Plos One, 2018, 13(2): 1-13.
[44] TANG Tie-qiao, SHI Wei-feng, SHANG Hua-yan, et al. A new car-following model with consideration of inter-vehicle communication[J]. Nonlinear Dynamics, 2014, 76(4): 2017-2023.
[45] TANG Tie-qiao, SHI Wei-feng, SHANG Hua-yan, et al.
An extended car-following model with consideration of the reliability of inter-vehicle communication[J]. Measurement: Journal of the International Measurement Confederation, 2014, 58: 286-293.
[46] OU Hui, TANG Tie-qiao. An extended two-lane car-following model accounting for inter-vehicle communication[J]. Physica A: Statistical Mechanics and its Applications, 2018, 495: 260-268.
[47] 沈逢春.基于广义元胞自动机的混合交通流建模研究[D].杭州:浙江大学,2012.
SHEN Feng-chun. Studies on mixed traffic flow modeling based on extended cellular automaton[D]. Hangzhou: Zhejiang University, 2012.(in Chinese)
[48] 于 丹.基于元胞自动机的小汽车-卡车异质交通流特性研究[D].成都:西南交通大学,2016.
YU Dan. Research on characteristics analysis of car-truck heterogeneous traffic based on the cellular automata model[D]. Chengdu: Southwest Jiaotong University, 2016.(in Chinese)
[49] 秦严严,王 昊,王 炜,等.混有CACC车辆和ACC车辆的异质交通流基本图模型[J].中国公路学报,2017,30(10):127-136.
QIN Yan-yan, WANG Hao, WANG Wei, et al. Fundamental diagram model of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles and adaptive cruise control vehicles[J]. China Journal of Highway and Transport, 2017, 30(10): 127-136.(in Chinese)
[50] KNORR F, SCHRECKENBERG M. Influence of inter-vehicle communication on peak hour traffic flow[J]. Physica A: Statistical Mechanics and its Applications, 2012, 391(6): 2225-2231.
[51] BOSE A, IOANNOU P. Analysis of traffic flow with mixed manual and semi-automated vehicles[C]∥IEEE. Proceedings of the 1999 American Control Conference. New York: IEEE, 1999: 2173-2177.
[52] BOSE A, IOANNOU P. Mixed manual/semi-automated traffic: a macroscopic analysis[J]. Transportation Research Part C: Emerging Technologies, 2003, 11(6): 439-462.
[53] 邱小平,马丽娜,周小霞,等.基于安全距离的手动-自动驾驶混合交通流研究[J].交通运输系统工程与信息,2016,16(4):101-108,124.
QIU Xiao-ping, MA Li-na, ZHOU Xiao-xia, et al. The mixed traffic flow of manual-automated driving based on safety distance[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4): 101-108, 124.(in Chinese)
[54] ZHU W X, ZHANG H M. Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model[J]. Physica A: Statistical Mechanics and its Applications, 2018, 496: 274-285.
[55] WANG Dian-hai, YANG Shao-hui, CHU Lian-yu. Modeling car-following dynamics during the starting and stopping process based on a spring system model[J]. Tsinghua Science and Technology, 2004, 9(6): 643-652.
[56] ZADEH L A. Outline of a new approach to the analysis of complex systems and decision processes[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(1): 28-44.
[57] CHAKROBORTY P. Models of vehicular traffic: an engineering perspective[J]. Physica A: Statistical Mechanics and its Applications, 2006, 372(1): 151-161.
[58] 贾洪飞,隽志才,王晓原.基于神经网络的车辆跟驰模型的建立[J].公路交通科技,2001,18(4):92-94.
JIA Hong-fei, JUAN Zhi-cai, WANG Xiao-yuan. Development of a car-following model based on artificial neural networks[J]. Journal of Highway and Transportation Research and Development, 2001, 18(4): 92-94.(in Chinese)
[59] 徐学明,荣 建,王 丽.混合神经网络跟驰模型的建立[J].公路交通科技,2007,24(3):130-132.
XU Xue-ming, RONG Jian, WANG Li. Development of a car-following model based on combined neural network model[J]. Journal of Highway and Transportation Research and Development, 2007, 24(3): 130-132.(in Chinese)
[60] 周立军,王殿海,李卫青.人工神经网络及粒子群优化算法在跟驰模型中的应用[J].吉林大学学报(工学版),2009,39(4):896-899.
ZHOU Li-jun, WANG Dian-hai, LI Wei-qing. Application of artificial neural network and particle swarm optimization in car-following model[J]. Journal of Jilin University(Engineering and Technology Edition), 2009, 39(4): 896-899.(in Chinese)
[61] HUANG Su-nan, REN Wei. Use of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control[J]. IEEE Transactions on Industrial Electronics, 1999, 46(6): 1090-1102.
[62] MA Xiao-liang. A neural-fuzzy framework for modeling car-
following behavior[C]∥IEEE. IEEE International Conference on Systems, Man and Cybernetics. New York: IEEE, 2007: 1178-1183.
[63] 李德慧,刘小明,荣 建,等.基于模糊神经网络的车辆跟驰建模与仿真研究[J].北京工业大学学报,2007,33(4):398-401.
LI De-hui, LIU Xiao-ming, RONG Jian, et al. Car-following safety modeling and simulation with fuzzy neural network[J]. Journal of Beijing University of Technology, 2007, 33(4): 398-401.(in Chinese)
[64] TOLEDO T, KOUTSOPOULOS H N, AHMED K I.
Estimation of vehicle trajectories with locally weighted regression[J]. Transportation Research Record, 2007(1999): 161-169.
[65] PAPATHANASOPOULOU V, ANTONIOU C. Towards data-driven car-following models[J]. Transportation Research Part C: Emerging Technologies, 2015, 55: 496-509.
[66] HE Zheng-bing, ZHENG Liang, GUAN Wei. A simple
nonparametric car-following model driven by field data[J]. Transportation Research Part B: Methodological, 2015, 80: 185-201.
[67] WEI Da-li, LIU Hong-chao. Analysis of asymmetric driving behavior using a self-learning approach[J]. Transportation Research Part B: Methodological, 2013, 47: 1-14.
[68] HINTON G E, SALAKHUTDINOV R R. Reducing the
dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[69] ZHOU Mo-fan, QU Xiao-bao, LI Xiao-peng. A recurrent
neural network based microscopic car following model to predict traffic oscillation[J]. Transportation Research Part C: Emerging Technologies, 2017, 84: 245-264.
[70] WANG Xiao, JIANG Rui, LI Li, et al. Capturing car-following behaviors by deep learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 910-920.
[71] HUANG Xiu-ling, SUN Jie, SUN Jian. A car-following
model considering asymmetric driving behavior based on long short-term memory neural networks[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 346-362.
[72] 孙 倩,郭忠印.基于LSTM神经网络方法的车辆跟驰模型[J].吉林大学学报(工学版),DOI: 10.13229/j.cnki.jdxbgxb20190287.
SUN Qian, GUO Zhong-yin. Vehicle following model based on LSTM neural network[J]. Journal of Jilin University(Engineering and Technology Edition), DOI: 10.13229/j.cnki. jdxbgxb20190287.(in Chinese)
[73] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518: 529-533.
[74] ZHU Mei-xin, WANG Xue-song, WANG Yin-hai. Human-like autonomous car-following model with deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies, 2018, 97: 348-368.
[75] LAVAL J A, LECLERCQ L. A mechanism to describe the formation and propagation of stop-and-go waves in congested freeway traffic[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2010, 368: 4519-4541.
[76] GONG Si-yuan, SHEN Jing-lai, DU Li-li. Constrained
optimization and distributed computation based car following control of a connected and autonomous vehicle platoon[J]. Transportation Research Part B: Methodological, 2016, 94: 314-334.
[77] HAMDAR S H, QIN Ling-qiao, TALEBPOUR A. Weather and road geometry impact on longitudinal driving behavior: exploratory analysis using an empirically supported acceleration modeling framework[J]. Transportation Research Part C: Emerging Technologies, 2016, 67: 193-213.
[78] HUANG Yueng-hsang, ZHANG Wei, ROETTING M, et al. Experiences from dual-country drivers: driving safely in China and the US[J]. Safety Science, 2006, 44(9): 785-795.
[79] JIANG R, HU M B, ZHANG H M, et al. Traffic experiment reveals the nature of car-following[J]. Plos One, 2014, 9(4): 1-9.
[80] JIANG R, HU M B, ZHANG H M, et al. On some
experimental features of car-following behavior and how to model them[J]. Transportation Research Part B: Methodological, 2015, 80: 338-354.
[81] JIANG R, JIN C J, ZHANG H M, et al. Experimental and empirical investigations of traffic flow instability[J]. Transportation Research Part C: Emerging Technologies, 2018, 94: 83-98.
[82] ZHU Mei-xin, WANG Xue-song, TARKO A, et al. Modeling car-following behavior on urban expressways in Shanghai: a naturalistic driving study[J]. Transportation Research Part C: Emerging Technologies, 2018, 93: 425-445.
[83] 罗 颖,秦文虎.基于IDM与RBFNN的组合型车辆低速跟驰模型[J].计算机应用研究,2019,37(8):1-7.
LUO Ying, QIN Wen-hu. Combination low-speed car-following model based on IDM and RBFNN[J]. Application Research of Computers, 2019, 37(8): 1-7.(in Chinese)

Memo

Memo:
-
Last Update: 2019-11-13