机非物理隔离路段非机动车行为建模仿真
CSTR:
作者:
中图分类号:

U491.255

基金项目:

上海市浦江人才计划(17PJC103);国家重点研发计划重点专项(2018YFB1600505)


Modeling and Simulation of the Non-motorized Traffic Flow on Physically Separated Bicycle Roadways
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [35]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    为准确刻画非机动车流微观运动特征,并在二维平面上描述非机动车的运动,从骑行者心理角度出发,首次提出舒适空间理论,来描述骑行者行为动机的产生,并以此为依据构建了动机决策执行三层框架非机动车模型,从行为产生的全过程来描述非机动车运动;将模型与上海市一处机非物理隔离路段的实证数据及现有模型进行对比,仿真结果表明,模型能够很好地反映非机动车速度分布等微观特征的同时,轨迹的平均误差仅为0.64 m,更加接近真实情况.

    Abstract:

    To accurately depict the characteristics of microscopic motion, and describe two-dimensional movements of non-motorized traffic flow, the comfort-zone theory was put forward for the first time to describe the generation of behavior motivation of cyclists. Besides, based on this new theory, we proposed a three-layered model to describe the movements of non-motorized vehicles from the whole process of behaviors. Comparing with empirical data collected in a physically separated road section in Shanghai and the social force model, the proposed model can reflect the microscopic features better, and the average error of trajectories is only 0.64 m.

    参考文献
    [1] Vasic J, Ruskin H J. Cellular automata simulation of traffic including cars and bicycles [J]. Physica A Statistical Mechanics Its Applications, 2012, 391(8): 2720-2729.
    [2] Edwards R D, Mason C N. Spinning the wheels and rolling the dice: Life-cycle risks and benefits of bicycle commuting in the U.S. [J]. Preventive Medicine. 2014, 64: 8-13.
    [3] 上海市城市规划管理局.上海市第五次综合交通调查总报告[R] . 上海:上海市城市规划管理局,2015.Urban Planning Administration of Shanghai. General report on fifth comprehensive traffic survey in Shanghai [M]. Shanghai: Urban Planning Administration of Shanghai, 2015.
    [4] Hang Jiang, Gang Ren, Liang Zheng, et al. Properties analyses for the heterogeneous nonmotorized vehicle traffic based on cellular automaton model [J]. International Journal of Modern Physics B, 2014, 28(16):1490-
    [5] Dozza M, Werneke J. Using naturalistic data to assess e-cyclist behavior [J]. Transportation Research Part F Traffic Psychology Behaviour, 2016, 41: 217-226.
    [6] Schleinitz K, Petzoldt T, Franke-Bartholdt L, et al. The German Naturalistic Cycling Study – Comparing cycling speed of riders of different e-bikes and conventional bicycles [J]. Safety Science, 2017, 92:290-297.
    [7] Piccinini G F B, Dozza M. Do cyclists on e-bikes behave differently than cyclists on traditional bicycles? [C]// International Cycling Safety Conference. 2014.
    [8] Shuichao Zhang. Simulation model of speed–density characteristics for mixed bicycle flow—Comparison between cellular automata model and gas dynamics model [J]. Physica A Statistical Mechanics Its Applications, 2013, 392(20): 5110-5118.
    [9] Zian Ma. A two-dimensional simulation model for modelling turning vehicles at mixed-flow intersections [J].Transportation Research Part C Emerging Technologies, 2017, 75(75):103-119.
    [10] Sutomo. H. Appropriate saturation flow at traffic signals in Javanese cities: a modelling approach [J]. Chemistry - A European Journal. 1992, 15(4): 885-900.
    [11] Fellendorf M, Vortisch P. Microscopic Traffic Flow Simulator VISSIM [J]. 2010, 145: 63-93.
    [12] 梁肖,毛保华,许奇. 自行车微观行为的心理生理力模型[J]. 交通运输系统工程与信息, 2012, 12(2): 91-97.Liang, Xiao, M. A. O. Baohua, and X. U. Qi. "Psychological-physical force model for bicycle dynamics. SJournal of Transportation Systems Engineering and Information TechnologyS12.2 (2012): 91-97.
    [13] Xue S, Jia B, Jiang R, et al. An improved Burgers cellular automaton model for bicycle flow [J]. Physica A Statistical Mechanics Its Applications, 2017, 487:164-177.
    [14] Kai Nagel, Schreckenberg M. A cellular automaton model for freeway traffic [J]. Journal De Physique I, 1992, 2(12): 2221-2229.
    [15] De Zhao. Modeling of Passing Events in Mixed Bicycle Traffic with Cellular Automata[J]. Transportation Research Record Journal of the Transportation Research Board, 2013, 2387(2387):26-34.
    [16] Shan X, Li Z, Chen X, et al. A Modified Cellular Automaton Approach for Mixed Bicycle Traffic Flow Modeling [J]. Discrete Dynamics in Nature and Society, 2015, (2015-7-13), 2015, 2015(6):1-11.
    [17] Jia B, Li X G, Jiang R, et al. Multi-value cellular automata model for mixed bicycle flow [J]. The European Physical Journal B-Condensed Matter and Complex Systems, 2007, 56(3):247-252.
    [18] Jin S, Qu X, Xu C, et al. An improved multi-value cellular automata model for heterogeneous bicycle traffic flow [J]. Physics Letters A, 2015, 379(39):2409-2416.
    [19] Ioanna Spyropoulou. Modelling a signal controlled traffic stream using cellular automata [J]. Transportation Research Part C Emerging Technologies, 2007, 15(3): 175-190.
    [20] Sven Maerivoet, Moor B D. Cellular automata models of road traffic [J]. Physics Reports, 2005, 419(1):1-64.
    [21] Wagner, P. Traffic simulations using cellular automata: comparison with reality[C]. Workshop on Traffic and Granular Flow. World Scientific Publishers, 1996, 199–203.
    [22] D Helbing, P Molnár. Social force model for pedestrian dynamics [J]. Canadian Metallurgical Quarterly, 1995, 51(5): 4282-4286
    [23] Li M, Shi F, Chen D. Analyze bicycle-car mixed flow by social force model for collision risk evaluation[J]. 2011.
    [24] Sch?nauer R, Stubenschrott M, Huang W, et al. Modeling concepts for mixed traffic steps toward a microscopic simulation tool for shared space zones [J]. Transportation Research Record, 2012, 2316, 114-121.
    [25] 梁肖. 自行车微观行为动力学建模及仿真研究[D]. 北京交通大学, 2012.Liang, Xiao. Dynamic model to simulate bicycle microscopic behavior [D]. Beijing Jiaotong University, 2012.
    [26] Hall E T. The Hidden Dimension [J]. Leonardo, 1966, 6(1): 94.
    [27] B?rgman J, Smith K, Werneke J. Quantifying drivers’ comfort-zone and dread-zone boundaries in left turn across path/opposite direction (LTAP/OD) scenarios [J]. Transportation Research Part F Psychology Behaviour, 2015, 35:170-184.
    [28] Summala H. Towards Understanding Motivational and Emotional Factors in Driver Behaviour: Comfort Through Satisficing [M]// Modelling Driver Behaviour in Automotive Environments. 2007: 189-207.
    [29] Reed R, Lewin K. Field Theory in Social Science [J]. American Catholic Sociological Review, 1951, 12(2): 103.
    [30] https://en.oxforddictionaries.com/definition/us/comfort
    [31] Horowitz M J, Duff D F, Stratton L O. BODY-BUFFER ZONE; EXPLORATION OF PERSONAL SPACE [J]. Arch Gen Psychiatry, 1964, 11(6):651-656.
    [32] Xuhong Li, Ying Ni, Tienan Li. Effects of Refuge Island Settings on Pedestrian Safety Perception and Signal Violation at Signalized Intersections[C]// Transportation Research Board. 2017.
    [33] 游峰. 智能车辆自动换道与自动超车控制方法的研究[D]. 吉林大学, 2005.YOU Feng. Study on Autonomous Lane Changing and Autonomous Overtaking Control Method of Intelligent Vehicle [D] Jilin University, 2005.
    [34] Manar A, Desmarais J P. Cyclist Behavior on Exclusive Bicycle Facility - A Longitudinal Analysis[C]. 2013.
    [35] Gazis D C, Herman R, Rothery R W. Nonlinear follow-the-leader models of traffic flow[J]. Operations Research. 1961, 9(4): 545-567.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

倪颖,李逸昕,李旭红,孙剑.机非物理隔离路段非机动车行为建模仿真[J].同济大学学报(自然科学版),2019,47(06):0778~0786

复制
分享
文章指标
  • 点击次数:1354
  • 下载次数: 993
  • HTML阅读次数: 1213
  • 引用次数: 0
历史
  • 收稿日期:2018-07-17
  • 最后修改日期:2019-04-24
  • 录用日期:2018-12-05
  • 在线发布日期: 2019-07-03
文章二维码