Please wait a minute...
浙江大学学报(工学版)
J4  2012, Vol. 46 Issue (10): 1846-1850    DOI: 10.3785/j.issn.1008-973X.2012.10.017
    
State estimation of freeway based on traffic flow hybrid model
LI Nan1,2, ZHAO Guang-zhou1
1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;
2. Faculty of Electronic and Control Engineering, Liaoning Technical University, Huludao 125105, China
Download:   PDF(0KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

It is hard to obtain satisfactory results using any single time-independent traffic flow model at the situation of realistic timedependent traffic flow. To resolve the issue, a data-driven first-and second-order linear hybrid model of traffic flow was proposed to capture the non-stationary and nonlinearity of traffic flow in a simple manner and help to enhance the ensuing estimation. Based on the linear hybrid model, and incorporated with the extended Kalman filter (EKF) theory, a freeway state estimator was constructed. Distinct from other estimators, the proposed estimator estimated the freeway state and the adaptability weighted coefficient in parallel. The effectiveness of the proposed traffic flow model and estimator was demonstrated by using field data. The time series of the relevant coefficients in different traffic conditions were estimated and compared to the measured values. Results indicated great potentials of the proposed estimator based on the new traffic flow model.

Published: 01 October 2012
CLC:  U 491  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

LI Nan, ZHAO Guang-zhou. State estimation of freeway based on traffic flow hybrid model. J4, 2012, 46(10): 1846-1850.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2012.10.017     OR     http://www.zjujournals.com/eng/Y2012/V46/I10/1846


基于交通流混合模型的高速公路状态估计

针对单一的高速公路交通流模型在时变复杂道路情况下经常无法取得满意状态估计效果的问题,通过引入适应性权重参数,描述不同交通状况下高速公路一阶模型和高阶模型对实际交通状况的近似程度和适用性,提出数据驱动的高速公路一阶/二阶线性交通流混合模型.结合扩展Kalman滤波（EKF）原理,构建新的高速公路状态估计器,通过对交通状态和权重参数进行联合估计,得到不同路况条件下权重参数的变化情况.数值模拟实验结果验证了新的交通流混合模型及估计器的适应性和有效性.

［1］ MCCREA J, MOUTARI S. A hybrid macroscopic based model for traffic flow in road networks ［J］. European Journal of Operational Research, 2010, 207(2): 676-684.
［2］ KOTSIALOS A, PAPAGEORGIOU M. The importance of traffic flow modeling for motorway traffic control ［J］. Networks and Spatial Economics, 2001, 1(1): 179-203.
［3］ TAMPERE C M J, IMMERS L H. An extended Kalman filter application for traffic state estimation using CTM with implicit mode switch and dynamic parameters ［C］∥ Proceedings of the IEEE Intelligent Transportation Systems Conference. Seattle: IEEE, 2007: 209-216.
［4］ VAN HINSBERGEN C P I J, SCHREITER T, ZUURBIER F S, et al. Fast traffic state estimation with the localized extended Kalman filter ［C］∥ 13th International IEEE Annual Conference on Intelligent Transportation Systems. Madeira Island, Portugal: IEEE, 2010: 917-922.
［5］ HEGYI A, GIRIMONTE D, BABUSKA R, et al. A comparison of filter configurations for freeway traffic estimation ［C］∥ Proceedings of IEEE Intelligent Transportation Systems Conference. Toronto: IEEE, 2006: 1029-1034.
［6］ WANG Y B, PAPAGEORGIOU M. Realtime freeway traffic state estimation based on extended Kalman filter: a general approach ［J］. Transportation Research: Part B, 2005, 39(2): 141-167.
［7］ 刘慕仁,薛郁,孔令江.城市道路交通问题与交通流模型［J］.力学与实践,2005, 27(1): 1-6.
LIU Muren, XUE Yu, KONG Lingjiang. Urban highway traffic and traffic flow models ［J］. Mechanics in Engineering, 2005, 27(1): 1-6.
［8］ SERGE P H, PIET H L B. State of the art of vehicular traffic flow modeling ［J］. Proceedings of the Institution of Mechanical Engineers: Part I: Journal of Systems and Control Engineering, 2001, 215(4): 283-303.
［9］ PAPAGEORGIOU M, DIAKAKI C, DINOPOULOU V, et al. Review of road traffic control strategies ［J］. Proceedings of the IEEE, 2003, 91(12): 2043-2067.
［10］ LEVEQUE R J. Numerical methods for conservation laws ［M］. Berlin: Birkhauser Verlag, 1999: 14-17.
［11］ YE Z R, ZHANG Y L, MIDDLETON D R. Unscented Kalman filter method for speed estimation using single loop detector data ［C］∥ 85th Annual Meeting of the Transportation Research Board. Washington DC: Transportation Research Board of the National Academies, 2006: 117-125.
［12］ MIHAYLOVA L, BOEL R.A particle filter for freeway traffic estimation ［C］∥ 43rd IEEE Conference on Decision and Control. Paradise Island, Bahamas: IEEE, 2004: 2106-2111.
［13］ KOTSIALOS A, PAPAGEORGIOU M. Motorway network traffic control systems ［J］. European Journal of Operational Research, 2004, 152(2): 321-333.
［14］ WANG Y B, PAPAGEORGIOU M, MESSMER A. A realtime freeway network traffic surveillance tool ［J］. IEEE Transactions on Control Systems Technology, 2006, 14(1): 18-32.
No related articles found!