考虑车流异质性的信号交叉口疏解车辆跟驰模型

交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (3): 67-74.

• 智能交通系统与信息技术 • 上一篇下一篇

考虑车流异质性的信号交叉口疏解车辆跟驰模型

王益^{1a, 1b}，荣建^{1a, 1b}，周晨静^*2，高亚聪^{1a, 1b}，刘思杨^{1a, 1b}

1. 北京工业大学a. 城市交通学院，b. 北京市交通工程重点实验室，北京 100124； 2. 北京建筑大学土木与交通工程学院，北京 100044

收稿日期:2020-02-13 修回日期:2020-03-25 出版日期:2020-06-25 发布日期:2020-06-28
基金资助:
国家自然科学基金/National Natural Science Foundation of China(51708017).

Improved Car-following Model Considering Mixed Traffic Flow on Queue Discharge at Signalized Intersections

WANG Yi^{1a, 1b}, RONG Jian^{1a, 1b}, ZHOU Chen-jing², GAO Ya-cong^{1a, 1b}, LIU Si-yang^{1a, 1b}

1a. College of Metropolitan Transportation, 1b. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China; 2. School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received:2020-02-13 Revised:2020-03-25 Online:2020-06-25 Published:2020-06-28

摘要/Abstract

摘要：

为客观地描述绿灯期间交叉口进口道异质疏解车流的跟驰行为，基于实测数据验证全速差模型发现，其加速度、速度、车间距的仿真结果存在较大误差. 考虑不同车型车辆性能和驾驶员驾驶行为差异，基于4 种跟驰情景，即小客车跟驰小客车(car-car)，小客车跟驰公交车(car-bus)，公交车跟驰小客车(bus-car)，公交车跟驰公交车(bus-bus)，建立考虑车流异质性的车辆跟驰模型. 结果表明，改进模型的性能提升明显，较全速差模型，速度和跟驰间距的均方根百分比误差(RMSPE)分别下降了15.29%，22.32%，更符合交叉口进口道异质疏解车流的跟驰行为.

关键词: 交通工程, 信号交叉口, 异质性车流, 疏解车辆, 跟驰模型

Abstract:

The Full Velocity Difference Model(FVD) had been calibrated and verified based on the real traffic data to reflect the car-following behavior of the mixed traffic flow at the intersection approach during the signal green time. However, the simulation results showed big gaps on acceleration, speed and vehicle spacing by using the FVD model. This study considered the differences of vehicle types and driver's behavior at four scenarios: car following car, car following bus, bus following car, and bus following bus, and then developed a new car-following model. The analysis results show that the performance of the proposed model has been significantly improved, and the Root Mean Square Percentage Errors (RMSPE) of speed and vehicle space are 15.29% and 22.32% , respectively, which are lower than the FVD model results. The improved model performs better than the FVD model in describing the car-following behavior at approaches of signalized intersections.

Key words: traffic engineering, signalized intersections, mixed traffic flow, queue discharge, car-following model

中图分类号:

U491

王益，荣建，周晨静，高亚聪，刘思杨. 考虑车流异质性的信号交叉口疏解车辆跟驰模型[J]. 交通运输系统工程与信息, 2020, 20(3): 67-74.

WANG Yi, RONG Jian, ZHOU Chen-jing,GAO Ya-cong, LIU Si-yang. Improved Car-following Model Considering Mixed Traffic Flow on Queue Discharge at Signalized Intersections[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(3): 67-74.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract20047.shtml

http://www.tseit.org.cn/CN/Y2020/V20/I3/67

参考文献

[1] AGHABAYK K, SARVI M, FOROUZIDEH N, et al. New car-following model considering impacts of multiple lead vehicle types[J]. Transportation Research Record, 2013, 2390(1): 131-137.

[2] 杨龙海, 张春, 仇晓赟, 等.车辆跟驰模型研究进展[J]. 交通运输工程学报, 2019, 19(5): 125-138. [YANG L H, ZHANG C, QIU X Y, et al. Research progress on car- following models[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 126-138.]

[3] GAZIS D C, HERMAN R, ROTHERY R W. Nonlinear follow-the-leader models of traffic flow[J]. Operations Research, 1961, 9(4): 545-567.

[4] GIPPS P G. A behavioural car-following model for computer simulation[J]. Transportation Research Part B: Methodological, 1981, 15(2): 105-111.

[5] BANDO M, HASEBE K, NAKAYAMA A, et al. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E, 1995, 51(2): 1035.

[6] HELBING D, TILCH B. Generalized force model of traffic dynamics[J]. Physical Review E, 1998, 58(1): 133.

[7] JIANG R, WU Q, ZHU Z. Full velocity difference model for a car-following theory[J]. Physical Review E, 2001, 64(1): 017101.

[8] WIEDEMANN R. Simulation des strassenverkehrsflusses [M]. Germany: Institut fürVerkehrswesen der Universität Karlsruhe, 1974.

[9] 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 L J, WANG D H, LI W Q. 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.]

[10] 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 F C. Studies on mixed traffic flow modeling based on extended cellular automation[D]. Hangzhou: Zhejiang University, 2012.]

[11] 王雪松, 朱美新.基于自然驾驶数据的中国驾驶人城市快速路跟驰模型标定与验证[J]. 中国公路学报, 2018, 31(9): 129-137. [WANG X S, ZHU M X. Calibrating and validating car- following models on urban expressways for Chinese drivers using naturalistic driving data[J]. China Journal of Highway and Transport, 2018, 31(9): 130-138.]

[12] COHEN S L. Application of car- following systems to queue discharge problem at signalized intersections[J]. Transportation Research Record, 2002, 1802(1): 205- 213.

[13] 支梁, 史忠科, 刘通. 一种交叉路口全速度差改进模型 [J]. 交通运输系统工程与信息, 2013, 13(6): 80-85, 100. [ZHI L, SHI Z K, LIU T. An improved FVD model for intersection[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(6): 80-85, 100.]

[14] 周文琳, 张骏, 史忠科, 等.全速度差简化模型及其实验验证[J].西北工业大学学报, 2013, 31(6): 952-957. [ZHOU W L, ZHANG J, SHI Z K, et al. A simplified full velocity difference model and its experimental verification[J]. Journal of Northwestern Polytechnical University, 2013, 31(6): 952-957.]

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考虑车流异质性的信号交叉口疏解车辆跟驰模型

Improved Car-following Model Considering Mixed Traffic Flow on Queue Discharge at Signalized Intersections

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