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- W3145776304 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." @default.
- W3145776304 created "2021-04-13" @default.
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- W3145776304 date "2015-01-01" @default.
- W3145776304 modified "2023-09-23" @default.
- W3145776304 title "A Short-Term Traffic Flow Forecasting Method and Its Applications" @default.
- W3145776304 hasPublicationYear "2015" @default.
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