上海交通大学学报(英文版) ›› 2015, Vol. 20 ›› Issue (2): 156-163.doi: 10.1007/s12204-015-1604-0

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A Short-Term Traffic Flow Forecasting Method and Its Applications

LIU Si-yan (刘思妍), LI De-wei* (李德伟), XI Yu-geng (席裕庚), TANG Qi-feng (汤奇峰)   

  1. (Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China)
  • 发布日期:2015-04-02
  • 通讯作者: LI De-wei (李德伟) E-mail:dwli@sjtu.edu.cn

A Short-Term Traffic Flow Forecasting Method and Its Applications

LIU Si-yan (刘思妍), LI De-wei* (李德伟), XI Yu-geng (席裕庚), TANG Qi-feng (汤奇峰)   

  1. (Department of Automation; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai Jiaotong University, Shanghai 200240, China)
  • Published:2015-04-02
  • Contact: LI De-wei (李德伟) E-mail:dwli@sjtu.edu.cn

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

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