A Short-Term Traffic Flow Forecasting Method and Its Applications

doi:10.1007/s12204-015-1604-0

Abstract

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

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

CLC Number:

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]. Journal of shanghai Jiaotong University (Science), 2015, 20(2): 156-163.

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