Two-Phase Model of Multistep Forecasting of Traffic State Reliability

Autor: Jufen Yang, Zhigang Liu, Guiyan Jiang, Lin Zhu
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Discrete Dynamics in Nature and Society, Vol 2018 (2018)
Druh dokumentu: article
ISSN: 1026-0226
1607-887X
DOI: 10.1155/2018/7650928
Popis: Multistep prediction of traffic state is a key technology for advanced transportation information system. The research results based on the principle of multistep prediction can provide more information about the traffic operating quality in advance. Considering that prediction error increases with the increasing numbers of multistep predictions, this research proposes the concept of dynamic predictability that is related to the characteristic of historical traffic flow data used. The traffic flow is characterized by randomness, regularity, and volatility according to the traffic flow theory. Therefore, three key indexes are firstly calculated to measure the characteristics of reliability series. Then a two-phase model is established based on wavelet neural network optimized by particle swarm optimization. The upper phase is a model to estimate the number of predictable steps, and the lower phase is the multistep prediction model of reliability. Compared with that of backpropagation neural network and support vector machine, results show that the convergence time of the wavelet neural network optimized by particle swarm optimization is the lowest, which only costs 256 and 291 seconds in both two-phase models under the same conditions. The average relative error of multistep prediction reached the lowest value, 8.91% and 12.01%, respectively, for weekday and weekend data used. Moreover, the prediction performance based on weekday is better than that of weekend. The research results lay a decision-making basis for managers in determining the key parts of road network to develop future improvement measures.
Databáze: Directory of Open Access Journals
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