|Table of Contents|

[1] Zhu Senlai, Cheng Lin, Chu Zhaoming,. Bayesian network model for traffic flow estimationusing prior link flows [J]. Journal of Southeast University (English Edition), 2013, 29 (3): 322-327. [doi:10.3969/j.issn.1003-7985.2013.03.017]
Copy

Bayesian network model for traffic flow estimationusing prior link flows()
基于先验路段流的贝叶斯网络交通流量估计模型
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
29
Issue:
2013 3
Page:
322-327
Research Field:
Traffic and Transportation Engineering
Publishing date:
2013-09-20

Info

Title:
Bayesian network model for traffic flow estimationusing prior link flows
基于先验路段流的贝叶斯网络交通流量估计模型
Author(s):
Zhu Senlai, Cheng Lin, Chu Zhaoming
School of Transportation, Southeast University, Nanjing 210096, China
朱森来, 程琳, 褚昭明
东南大学交通学院, 南京 210096
Keywords:
traffic flow estimation Gaussian Bayesian network evidence propagation combined method
交通流估计 高斯贝叶斯网络 证据传递 组合方法
PACS:
U412
DOI:
10.3969/j.issn.1003-7985.2013.03.017
Abstract:
In order to estimate traffic flow, a Bayesian network(BN)model using prior link flows is proposed. This model sets link flows as parents of the origin-destination(OD)flows. Under normal distribution assumptions, the model considers the level of total traffic flow, the variability of link flows and the violation of the conservation law. Using prior link flows, the prior distribution of all the variables is determined. By updating some observed link flows, the posterior distribution is given. The variances of the posterior distribution normally decrease with the progressive update of the link flows. Based on the posterior distribution, point estimations and the corresponding probability intervals are provided. To remove inconsistencies in OD matrices estimation and traffic assignment, a combined BN and stochastic user equilibrium model is proposed, in which the equilibrium solution is obtained through iterations. Results of the numerical example demonstrate the efficiency of the proposed BN model and the combined method.
为了估计交通流量, 提出了一个使用先验路段流的贝叶斯网络模型.该模型把路段流量设为OD流量的父节点.在正态分布假设下, 模型考虑了总交通流水平、路段流可变性以及交通量守恒的随机扰动.根据先验路段流确定所有变量的先验分布.通过更新一些观测的路段流量, 给出后验分布.后验分布的方差往往随着路段流量的逐步更新而不断减小.基于得到的后验分布, 给出点预测和相应的概率区间.为消除OD矩阵估计和交通分配之间的不一致, 组合了贝叶斯网络和随机用户均衡模型, 通过迭代得到均衡解.算例结果验证了提出的贝叶斯网络模型和组合方法的效果.

References:

[1] Doblas J, Benitez F G. An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix [J]. Transportation Research Part B, 2005, 39(7): 565-591.
[2] Parry M, Hazelton M L. Estimation of origin-destination matrices from link counts and sporadic routing data [J]. Transportation Research Part B, 2012, 46(1): 175-188.
[3] Xie C, Kockelman K M, Waller S T. A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation [J]. Transportation Research Part B, 2011, 45(9): 1465-1482.
[4] Lo H P, Zhang N. Estimation of an origin-destination matrix with random link choice proportions: a statistical approach [J]. Transportation Research Part B, 1996, 30(4): 309-324.
[5] Sun S L, Zhang C S, Yu G Q. A Bayesian network approach to traffic flow forecasting [J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(1): 124-132.
[6] Castillo E, Menéndez J M, Sánchez-Cambronero S. Predicting traffic flow using Bayesian networks [J]. Transportation Research Part B, 2008, 42(5): 482-509.
[7] Perrakis K, Karlis D, Cools M, et al. A Bayesian approach for modeling origin-destination matrices [J]. Transportation Research Part B, 2012, 46(1): 200-212.
[8] Wang Jian, Deng Wei, Zhao Jinbao. Short-term freeway traffic flow prediction based on improved Bayesian combined model [J]. Journal of Southeast University: Natural Science Edition, 2012, 42(1):162-167.(in Chinese)
[9] Wang Jian, Deng Wei, Zhao Jinbao. Short-term freeway traffic flow prediction based on multiple methods with Bayesian network [J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(4):147-153.
[10] Xianyu Jianchuan, Juan Zhicai, Zhu Taiying. Travel choice analysis by Bayesian networks [J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(5):167-172.
[11] Xu M, Qu Y, Gao Z. Implementing Frank-Wolfe algorithm under different flow update strategies and line search technologies [J]. Journal of Transportation Systems Engineering and Information Technology, 2008, 8(3): 14-22.
[12] Bar-Gera H. Origin-based algorithm for the traffic assignment problem [J]. Transportation Science, 2002, 36(4): 398-417.
[13] Zhou Z, Chen A, Behkor S. C-logit stochastic user equilibrium model: formulations and solution algorithm [J]. Transportmetrica, 2012, 8(1): 17-41.
[14] Abbe E, Bierlaire M, Toledo T. Normalization and correlation of cross-nested logit models [J]. Transportation Research Part B, 2007, 41(7): 795-808.
[15] Fisk C. On combining maximum trip estimation with user optimal assignment [J]. Transportation Research Part B, 1988, 22(1): 69-79.
[16] Yang H. Heuristic algorithm for the bi-level origin destination matrix estimation problem [J]. Transportation Research Part B, 1995, 29(1): 1-12.
[17] Maher M, Zang X, Van Vliet D. A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows [J]. Transportation Research Part B, 2001, 35(1): 23-40.

Memo

Memo:
Biographies: Zhu Senlai(1989—), male, graduate; Cheng Lin(corresponding author), male, doctor, professor, gist@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.51078085, 51178110).
Citation: Zhu Senlai, Cheng Lin, Chu Zhaoming.Bayesian network model for traffic flow estimation using prior link flows[J].Journal of Southeast University(English Edition), 2013, 29(3):322-327.[doi:10.3969/j.issn.1003-7985.2013.03.017]
Last Update: 2013-09-20