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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 6-7Short-Term Traffic State Forecasting for Dynamic...

Short-Term Traffic State Forecasting for Dynamic Traffic Routing System

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Abstract:

The paper researches the problem of how to forecast traffic information in one minute interval. A kalman filter algorithm for short-term traffic forecasting based on the construction of dynamic traffic routing system of Nanning city of China was proposed in this paper, and selected National Road as field calibration, which compared with the real traffic conditions, and the error of predicted results is less than 10%. The results demonstrate the effectiveness of the proposed forecasting model in paper, and the research has a significant contribution for data process of Dynamic Traffic Routing System.

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Information Technology for Manufacturing Systems III

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Periodical:

Advanced Engineering Forum (Volumes 6-7)

Pages:

506-512

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.6-7.506

Citation:

Cite this paper

Online since:

September 2012

Authors:

Hua Suo Ru, De Pin Peng, Lian Xiong Gao, Yuan Zhou

Keywords:

Dynamic Traffic Routing System, Forecasting, Kalman Filter (KF), Short-Term Traffic State, Traffic Engineering

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Citation:

[1] Wang Zhengwu, Huang Zhongxiang. An Analysis and Discussion on Short-Term Traffic Flow Forecasting [J]. System Engineering, 21(6) (2003), pp.97-100.

[2] He Guoguang, Li Yu, Ma Shoufeng. Discussion on Short-Term Traffic Flow Forecasting Methods Based on Mathematical Models [J]. System Engineering-Theory &Preactice. 20(12) (2000), pp.51-56.

[3] Bart Van Arem, Howard R Kirby, Martie J, et al. Recent Advances and Application in the Field of Short-Term Traffic Forecasting[J]. International Journal of Forecasting, , 13(1997), pp.1-12.

DOI: 10.1016/s0169-2070(96)00695-4

[4] Chrobok R, Kaumann O, Wahle J, et al. Different Methods of Traffic Forcast Based on Real Data[J]. European Journal of Operational Research, 155(2004), pp.558-568.

DOI: 10.1016/j.ejor.2003.08.005

[5] Song Chunguang, Song Jingyan, Ren Jiangtao. Short-Term Traffic Flow Forecasting Research based on Phase Space Reconstruction [J]. Journal of Highway and Transportation Research and Development, 20(4) (2003), pp.71-75.

DOI: 10.1109/itsc.2003.1252024

[6] He Guoguang, Ma Shoufeng, Li Yu. Study on the Short-term Forecasting for Traffic Flow Based on Wavelet Analysis [J]. System Engineering-Theory & Practice. 22(9) (2002), pp.101-106.

[7] Zhang Ya-ping, Zhang Qi-sen. The Application of Cusp Catastrophe Theory in the Traffic Flow Forecast[J]. Journal of Systems Engineering, 2000, 15 (3), pp.272-276.

[8] Wang Hongjie, Lin Liangming, Xu Dagan. A Traffic Flow Prognostication Algorithm Based on Improved BP Neural Network [J]. Computer and Communications, 2001, 19(3), pp.11-14.

[9] Gong Xiao-yan, Tang Shu-ming. Integrated Traffic Flow Forecasting and Traffic Incident Detection Algorithm Based on Non-parametric Regression[J]. China Journal of Highway and Transport, 2003, 16 (1), pp.82-86.

[10] Anderson, J. M., Bell, M. G. H., Sayers, T. M., Busch, F. M., and Heymann, G. 'The short-term prediction of link travel times in signal controlled road networks. ', Proc., Transp. Sys.: Theory and Application of Advanced Technol., IFAC Symp. (1994).

DOI: 10.1016/s1474-6670(17)47540-9

[11] Florio, L., and Mussone, L. 'Neural network models for classification and forecasting of freeway traffic flow stability. ', Proc., Transp. Sys.: Theory and Application of Advanced Technol., IFAC Symp. (1994), p.773–784.

DOI: 10.1016/s1474-6670(17)47567-7

[12] Park, D., and Rilett, L. R. 'Forecasting multiple-period freeway link travel times using modular neural networks. ', Transp. Res. Rec. 1617, Transportation Research Board, (1998), p.63–70.

DOI: 10.3141/1617-23

[13] Park, D., and Rilett, L. R. 'Forecasting freeway link travel times with a feedforward multilayer neural network. ', Comp. -Aided Civ. And Infrastruct. Engrg., 14, (1999), p.357–367.

DOI: 10.1111/0885-9507.00154

[14] Park, D., Rilett, L. R., and Han, G. 'Forecasting multiple-period freeway link travel times using neural networks with expanded input nodes. ', Proc., 5th Int. Conf. of Advanced Technol. Application in Transp. Engrg. (1998), p.325–332.

DOI: 10.3141/1617-23

[15] Dongjoo Park, Laurence R. Rilett, and Gunhee Han. Spectral basis Neural Networks for Real-Time Travel Time Forecasting. Journal of Transportation Engineering, (1999), pp.515-523.

DOI: 10.1061/(asce)0733-947x(1999)125:6(515)

[16] Hussein Dia. An object-oriented neural network approach to short-term traffic forecasting. European Journal of Operational Research, Volume 131, Issue 2, (2001), pp.253-261.

DOI: 10.1016/s0377-2217(00)00125-9

[17] Haibo Chen, Susan Grant-Muller, Lorenzo Mussone and Frank Montgomery. A Study of Hybrid Neural Network Approaches and the Effects of Missing Data on Traffic Forecasting, Neural Computing&Applications, Vol 10, NO. 3, (2001), pp.277-286.

DOI: 10.1007/s521-001-8054-3

[18] Hongbin Yin, S. C. Wong, Jianmin Xu and C.K. Wong. Urban Traffic Flow Prediction Using a Fuzzy-neural Approach. Transportation Research Part C: Emerging Technologies, Vol. 10, ISSUE 2, (2002), pp.85-98.

DOI: 10.1016/s0968-090x(01)00004-3