基于现实与虚拟交互的交通流再现实验方法
CSTR:
作者:
作者单位:

同济大学,同济大学

中图分类号:

U491.2

基金项目:

国家自然科学基金重点项目 (项目编号:51238008)


An Experimental Method for Reproducing Traffic Flow Based on Reality and VirtualInteraction
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [28]
  • |
  • 相似文献 [20]
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    面向连续与间断交通流实验系统框架,利用现实交通流的观测数据,在实验框架的虚拟环境中建立交通流的非参数模型,通过虚拟框架的贝叶斯学习再现与现实等价的实验交通流.选取更为复杂的信号控制交通流场景对该实验方法进行验证.结果表明,该方法在一定精度内可以近似再现信号控制交通流.

    Abstract:

    With the development and application of information technology, it is becoming a new research direction to analyze complex traffic flow based on experimental methods. One of the basic problems is the reproduction of the actual traffic flow in the experiment. Based on the framework of a traffic flow experimental system, this paper proposes an experimental method to reproduce the real traffic flow in virtual environment by giving the observation data of traffic flow in real environment whose system framework includes the nonparametric model of traffic flow and the Bayesian learning algorithm. Subsequently, the experimental method was numerically verified in the scene of traffic flow on signal control. The results show that the method proposed could realize the approximate dynamic traffic flow on signal control in virtual environment.

    参考文献
    [1] Daoglas C. Montgomery. Design and Analysis of Experiments (6th edition) [M]. John Wiley Sons, Inc., Hoboken. 2006.
    [2] 杨晓光,孙剑,徐建闽等.实验交通工程基本理论(方法)与信息技术[C].建筑,环境与土木工程学科发展战略研究.科学出版社.2005.YANG Xiaoguang, SUN Jian, XUN Jianmin, et al. The basic theory (method) of experimental traffic engineering and information technology[C]. Research on the development strategy of architecture, environment and civil engineering. Science Press, Beijing. 2005.
    [3] 童梅.面向实验交通系统的建模与计算[D].上海: 同济大学交通运输工程学院.2008.TONG Mei. Modeling and Computing in Experimental Transportation Systems [D]. Shanghai: Tongji University. College of Transportation Engineering. 2008.
    [4] 杨晓光,孙剑.面向ITS的交通仿真实验系统[J].长沙理工大学学报(自然科学版),2006,3(3):43-48YANG Xiaoguang, SUN Jian. Microscopic traffic smiulation and expermiental system under ITS [J]. Journal of Changsha University of Science and Technology (Natural Science), 2006,3(3):43-48.
    [5] 时柏营. 面向交叉口的交通流实验分析方法[D].上海: 同济大学交通运输工程学院.2010.SHI Baiying. The Experimental Method of Traffic Flow Facing Intersection [D]. Shanghai: Tongji University. College of Transportation Engineering. 2010.
    [6] 赵靖. 提升道路通行能力时空协同优化控制理论和方法[D].上海: 同济大学交通运输工程学院.2014.ZHANG Jing. Urban Streets Capacity Enhancement by Coordination Optimization of Lane Reorganization and Signale Contral [D]. Shanghai: Tongji University. College of Transportation Engineering. 2014.
    [7] 杨晓光等.国家863计划项目: 交通状态全息感知与交通战略实验室研究报告[R](课题编号:2012AA112306), 2015.YANG Xiaoguang, et al. The Research Report of National High-tech R D Program (863 Program): Traffic state holographic perception and Traffic Strategy Laboratory (Subject number: 2012AA112306). 2015.
    [8] Mihaylova L., Boel R., Hegyi A. Freeway traffic estimation within particle filtering framework[J]. Automatica, 2007, 43 (2): 290–300.
    [9] Sumalee A., Zhong R.X., Pan T., et al. Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment[J]. Transportation Research Part B, 2011, 45 (3): 507–533.
    [10] Nantes A., Ngoduy D., Bhaskar A., et al. Real-time Traffic State Estimation in Urban Corridors from Heterogeneous Data[J].Transportation Research Part C, 2016, 66: 99–118.
    [11] Ossen S., Hoogendoorn S.P.. Validity of trajectory-based calibration approach of car-following models in presence of measurement errors[J]. Transportation ResearchRecord, 2008, 2088S: 117–125.
    [12] Punzo V., Ciuffo B., Montanino M.. Can results of car-following model calibration based on trajectory data be trusted?[J]. Transportation Research Record, 2012, 2315S: 11–24.
    [13] Wagner P.. Analyzing fluctuation in car-following[J]. Transportation Research Part B, 2012,46, 1384–1392.
    [14] Marcello M., Vincenzo P.. Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns[J]. Transportation Research Part B, 2015, 80 : 82–106
    [15] Federal Highway Administration. Next Generation SIMulation Fact Sheet, FHWA-HRT-06-135, 2006. https://www.fhwa.dot.gov/publications/research/operations/its/06135/index.cfm.
    [16] FAN Jianqing, YAO Qiwei. Nonlinear time series: nonparametric and parametric methods[M]. Springer, New York. 2003.
    [17] Ghahramani Z., Beal M.. Propagation algorithms for variational Bayesian learning[J]. Advances in Neural Information Processing Systems, 2001, 13S: 507–513.
    [18] Gelman A., Carlin J.B., Stern, H.S., et al. Bayesian Data Analysis[M]. Chapman Hall, London. 2004.
    [19] 周商吾等.交通工程[M].同济大学出版社,上海.1987.ZHOU Shangwu et al. Traffic Engineering[M]. Tongji University Press, Shanghai. 1987.
    [20] Ma Wanjing, An Kun, Lo H.K.. Multi-stage stochastic program to optimize signal timings under coordinated adaptive control[J]. Transportation Research Part C, 2016, 72S: 342–359.
    [21] Daganzo, C.F.. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory[J]. Transportation Research Part B, 1994, 28 (4)S: 269–287..
    [22] Daganzo, C.F.. The cell transmission model Part II: Network traffic[J]. Transportation Research Part B,1995, 29 (2)S: 79–93.
    [23] Wu Xinkai, Liu Henry. A shockwave profile model for traffic flow on congested urban arterials[J]. Transportation Research Part B, 2011,45S: 1768–1786.
    [24] Beal M., Ghahramani Z., Rasmussen C.E.. The infinite hidden Markov model[J]. In Advances in Neural Information Processing Systems, 2002, 14S: 577–584,
    [25] Robert C.P.,Casella G.. Monte Carlo Statistical Methods [M]. Springer, New York, 2005.
    [26] Sun, X., Mu?oz, L., Horowitz, R. Highway traffic state estimation using improved mixture Kalman filters for effective ramp metering control[J]. Proceedings of the 42nd IEEE Conference on Decision and Control. Hawaii, USA, 2003S: 6333–6338.
    [27] Sun Shiliang, Zhang Changshui, Yu Guoqiang. A Bayesian Network Approach to Traffic Flow Forecasting [J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(1): 124–132..
    [28] Lei Juyang, Huang Ke, Xu Haixiang, et al..Infinite Dimensional Sampling Inference Algorithm For Linear Dynamic System [J]. Neurocomputing, 2009, 72(4-6):1307-1311
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

杨晓光,张楠.基于现实与虚拟交互的交通流再现实验方法[J].同济大学学报(自然科学版),2018,46(12):1659~1667

复制
分享
文章指标
  • 点击次数:1325
  • 下载次数: 948
  • HTML阅读次数: 894
  • 引用次数: 0
历史
  • 收稿日期:2018-01-26
  • 最后修改日期:2018-10-29
  • 录用日期:2018-09-07
  • 在线发布日期: 2019-01-04
文章二维码