基于路口转弯流量的OD估计方法研究

摘要/Abstract

摘要：

机动车起终点矩阵(Origin-Destination Matrix，OD矩阵)的估计是交通规划和交通管理等工作的重要基础.本文主要研究在采集数据日渐精细情况下的机动车OD估计方法.基于车牌识别数据提取出路口转弯流量和路段断面流量，在此基础上建立应用广义最小二乘模型进行机动车OD估计的模型及方法.利用S-Paramics 仿真平台及实测数据，应用Nguyen–Dupuis 网络和实际城市路网对本文研究的方法进行了对比验证，分析对比验证了是否已知真实OD、不同的数据输入类型、不同的已知检测量的比例等.结果显示，与使用路段流量相比，使用转弯流量可以提高OD估计的准确性.

关键词: 交通工程, OD估计, 广义最小二乘模型, 转弯流量

Abstract:

Origin- Destination Matrices, also called OD Matrix, is an important basic for the transport planning and traffic management. A procedure is proposed to estimate the OD matrix based on the abundant precise vehicle trajectory data. The turning movement and link count are provided by the automatic number plate recognition (ANPR) data. An OD estimation method is proposed based on the General Least Squared Model (GLS). Using S-Paramics simulation platform, Nguyen-Dupuis Network and a real city road network are used to compare the estimation results of different circumstances: real OD known or unknown, two different inputs(the turning movement and the link count), and different rates of measurements. The results show that the turning movement can improve the accuracy of OD estimation.

Key words: traffic engineering, OD estimation, generalized least squares, turning movement

中图分类号:

U491

李瑞敏，陈熙怡，张睿博. 基于路口转弯流量的OD估计方法研究[J]. 交通运输系统工程与信息, 2015, 15(6): 170-176.

LI Rui-min, CHEN Xi-yi, ZHANG Rui-bo. OD Estimation Based on Turning Movement[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(6): 170-176.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract19098.shtml

http://www.tseit.org.cn/CN/Y2015/V15/I6/170

参考文献

[1] Van Zuylen H J, Willumsen L G. The most likely trip matrix estimated from traffic counts[J]. Transportation Research Part B- Methodological, 1980, 14（3）: 281- 293.

[2] Alibabai H, Mahmassani H S. Dynamic origindestination demand estimation using turning movement counts[J]. Transportation Research Record, 2008 （2085）: 39-48.

[3] 何兆成, 余志. 城市道路网络动态OD估计模型[J]. 交通运输工程学报, 2005（02）: 94-98. [HE Z C, YU Z. Dynamic OD estimation model of urban network[J]. Journal of Traffic and Transportation Engineering, 2005 （02）: 94-98.]

[4] 蒋云. 基于路段转向流量的OD估计算法研究[D]. 中国科学技术大学, 2015. [JIANG Y. Study of OD estimation based on turning traffic flow[D]. University of Science and Technology of China,2015.]

[5] 林勇, 蔡远利, 黄永宣. 基于广义最小二乘模型的动态交通OD 矩阵估计[J]. 系统工程理论与实践, 2004（01）: 136-140. [LIN Y, CAI Y L, HUANG Y X. GLS model based dynamic origin-destination matrix estimation for traffic systems[J]. Systems Engineering- Theory & Practice, 2004（01）: 136-140.]

[6] Lin P W, Chang G L. A generalized model and solution algorithm for estimation of the dynamic freeway origindestination matrix[J]. Transportation Research Part BMethodological, 2007, 41（5）: 554-572.

[7] Barceló J, Montero L, Bullejos M, et al. A kalman filter approach for exploiting bluetooth traffic data when estimating time- dependent OD matrices[J]. Journal of Intelligent Transportation Systems, 2013, 17（2SI）: 123- 141.

[8] 林勇, 蔡远利, 黄永宣. 基于卡尔曼滤波的动态OD矩阵估计[J]. 系统工程理论与实践, 2003（10）: 135- 139. [LIN Y, CAI Y L, HUANG Y X. Kalman filtering based dynamic OD matrix estimation[J]. Systems Engineering-Theory & Practice, 2003（10）: 135-139.]

[9] Shou-Ren Hu, Chi-Bang Chen. Dynamic estimation of freeway origin- destination demand and travel time using extended kalman filtering algorithm[M]. 2004: 1329-1334.

[10] Mishalani Rabi G, Coifman Benjamin, Gopalakrishna Deepak. Evaluating real time origin destination flow estimation using remote sensing-based surveillance data[M]. ASCE, Cambridge, MA, August, 2002.

[11] Nanne Van Der Zijpp. Dynamic OD-matrix estimation from traffic counts and automated vehicle identification data[R]. Transportation Research Record 1607, 1997.

[12] 杨小宝, 王新伟, 张宁. 基于ATIS 下的OD 矩阵估计[J]. 公路交通科技, 2007（09）: 100-103. [YANG X B, WANG X W, ZHANG N. Estimation of OD matrix based on information from ATIS[J]. Journal of Highway and Transportation Research and Development, 2007 （09）: 100-103.]

[13] 孙剑, 冯羽. 基于车辆自动识别技术的动态OD矩阵估计新方法[J]. 同济大学学报（自然科学版）, 2013, 41（9）: 1366-1371, 1377. [SUN J, FENG Y. A novel dynamic OD estimation approach based on automatic vehicle identification data[J]. Journal of Tongji University(Natural Science), 2013, 41（9）: 1366-1371, 1377.]

[14] 李树彬. 城市交通系统运行状态评估及控制策略研究[D]. 北京交通大学, 2012. [LI S B. Research on the run state evaluation of urban transportation system and control strategies[D]. Beijing Jiaotong University, 2012.]

[15] Barceló J, Montero L, Marqués L, et al. A kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and WiFi data collection[M]. Lisbon, Portugal, 2010.

[16] Antoniou C, Ben-Akiva M, Koutsopoulos H N. Dynamic traffic demand prediction using conventional and emerging data sources[J]. IEE Proceedings Intelligent Transport Systems, 2006, 153（1）: 97-104.

[17] 彭建, 徐猛, 高自友. 基于有序加权平均算子的公交线路OD矩阵估计[J]. 管理科学学报, 2013, 16（1）: 36- 41, 58. [PENG J, XU M, GAO Z Y. Transit route OD estimation based on ordered weighted averaging operator[J]. Journal of Management Sciences in China, 2013, 16（1）: 36-41, 58]

[18] Cascetta E. Estimation of trip matrices from traffic counts and survey data-A generalized least-squares estimator[J]. Transportation Research Part BMethodological, 1984, 18（4-5）: 289-299.

[19] Frederix Rodric, Viti Francesco, Himpe Willem W E, et al. Dynamic origin-destination matrix estimation on large- scale congested networks using a hierarchical decomposition scheme[J]. Journal of Intelligent Transportation Systems, 2014, 18（1）.

[20] 陆化普, 交通规划理论与方法（第二版）[M]. 北京: 清华大学出版社，2006. [LU H P. Theory and method in transportation planing(Second edition) [M]. Beijing: Tsinghua University Press, 2006.]

[21] Hillier Frederick S, Lieberman Gerald J. 运筹学导论[M].（第9版）北京: 清华大学出版社，2010. [Hillier Frederick S, Lieberman Gerald J. Introduction to operations research(Ninth edition)[M]. Beijing: Tsinghua University Press, 2010.]

[22] Castillo Enrique, Maria Menendez Jose, Jimenez Pilar. Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations[J]. Transportation Research Part B-Methodological, 2008, 42（5）: 455-481.

[23] Castillo E, Jimenez P, Menendez J M, et al. The observability problem in traffic models: Algebraic and topological methods[J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9（2）: 275-287.

[24] Sanchez-Rico Maria Teresa, Garcia-Rodenas Ricardo, Espinosa-Aranda Jose Luis. A Monte Carlo approach to simulate the stochastic demand in a continuous dynamic traffic network loading problem[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15（3）: 1362-1373.

[25] Lewis C D. Industrial and business forecasting methods[M]. London: Butterworth- Heinemann，1982.