A Short-Term Traffic Flow Forecasting Method and Its Applications

doi:10.1007/s12204-015-1604-0

摘要/Abstract

摘要： Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast’s precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network (HPSO-NN) with error compensation mechanism. In HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average (ARMA) model, can forecast traffic flow with a higher accuracy. What’s more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.

关键词: short-term velocity forecast, intelligent transportation systems (ITS), neural network (NN), hybrid particle swarm optimization-neural network (HPSO-NN) algorithm, error compensation mechanism

Abstract: Short-term forecast of urban traffic flow is very important to intelligent transportation. Although the conventional methods have some advantages, to some extent, in improving the traffic forecast’s precision, it is still hard to achieve high accuracy. In this paper, we propose a short-term traffic flow forecasting method, which is based on the hybrid particle swarm optimization-neural network (HPSO-NN) with error compensation mechanism. In HPSO-NN, the hybrid PSO algorithm is employed to train the structures and parameters of the feed-forward advanced neural network, while the error compensation mechanism is employed to improve the accuracy. HPSONN is used to forecast the vehicle velocity in Shanghai North-South Viaduct. Experimental results show that the HPSO-NN, compared with the auto-regressive and moving average (ARMA) model, can forecast traffic flow with a higher accuracy. What’s more, we have also found that HPSO-NN with error compensation mechanism has better performance than that of HPSO-NN alone.

Key words: short-term velocity forecast, intelligent transportation systems (ITS), neural network (NN), hybrid particle swarm optimization-neural network (HPSO-NN) algorithm, error compensation mechanism

中图分类号:

TP 273

LIU Si-yan (刘思妍), LI De-wei* (李德伟), XI Yu-geng (席裕庚), TANG Qi-feng (汤奇峰). A Short-Term Traffic Flow Forecasting Method and Its Applications[J]. 上海交通大学学报（英文版）, 2015, 20(2): 156-163.

LIU Si-yan (刘思妍), LI De-wei* (李德伟), XI Yu-geng (席裕庚), TANG Qi-feng (汤奇峰). A Short-Term Traffic Flow Forecasting Method and Its Applications[J]. Journal of shanghai Jiaotong University (Science), 2015, 20(2): 156-163.

参考文献 15

[1]	Qiao De-hua, Zhang Kai-he, Fan Yao-zu. The optimizing of many traffic flow forecasting models [J].Communications Standardization, 2007, 164(4): 207-210 (in Chinese).
[2]	Smith B L, Demetsky M J. Traffic flow forecasting:Comparison of modeling approaches [J]. Journal of Transportation Engineering, 1997, 123(4): 261-266.
[3]	Okutani I, Stephanedes Y J. Dynamic prediction of traffic volume through Kalman filtering theory [J].Transportation Research, 1984, 18(1): 1-11.
[4]	Smith B L, Williams B M, Oswald R K. Comparison of parametric and nonparametric models for traffic flow forecasting [J]. Transportation Research: Emerging Technologies, 2002, 10(4): 303-321.
[5]	Chrobok R, Wahle J, Schreckenberg M. Traffic forecast using simulations of large scale networks[C]//2001 IEEE Intelligent Transportation Systems Conference Proceedings. Oakland, USA: IEEE, 2001:434-439.
[6]	Vanajakshi L, Rilett L R. A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed[C]//2004 IEEE Symposium on Intelligent Vehicles.Parma, Italy: IEEE, 2004: 194-199.
[7]	Kennedy J F, Eberhart R C. Swarm intelligence[M]. San Francisco, USA：Morgan Kaufmann, 2001.
[8]	Tang Q F, Zhao L, Qi R B, et al. Tuning the structure and parameters of a neural network by using cooperative quantum particle swarm algorithm [J]. Applied Mechanics and Materials, 2011, 48(2011): 1328-1332.
[9]	Leung F H F, Lam H K, Ling S H, et al. Tuning of the structure and parameters of neural network using an improved genetic algorithm [J]. IEEE Transactions on Neural Networks, 2003, 14(1): 79-88.
[10]	Tsai J T, Chou J H, Liu T K. Tuning the structure and parameters of neural network by using hybrid Taguchi-genetic algorithm [J]. IEEE Transactions on Neural Network, 2006, 17(1): 69-80.
[11]	Zhao L, Qian F. Tuning the structure and parameters of a neural network using cooperative binary-real particle swarm optimization [J]. Expert Systems with Applications, 2011, 38(5): 4972-4977.
[12]	Jia R, Huang G. Hydroelectric generating unit vibration fault diagnosis via BP neural network based on particle swarm optimization [C]//International Conference on Sustainable Power Generation and Supply.Nanjing, China: IEEE, 2009: 1-4.
[13]	Chen Q, Guo W, Li C H. An improved PSO algorithm to optimize BP neural network [C]//Fifth International Conference on Natural Computation.Madeira, Portugal: IEEE, 2009: 357-360.
[14]	Luo D, Chu Z, Luo L Z, et al. Applications study of particle swarm optimization neural network in CFRD dam deformation monitoring [C]//International Conference on Environmental Engineering and Technology.Phuket, Thailand: IEEE, 2012: 6-12.
[15]	Tang Q F, Li D W, Xi Y G, et al. Soft-sensing design based on semi-closed-loop framework [J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1213-1218.