Two-Phase Model of Multistep Forecasting of Traffic State Reliability

• Published in: Discrete dynamics in nature and society 2018-01, Vol.2018 (2018), p.1-12
• Main Authors: Zhu, Lin, Jiang, Guiyan, Liu, Zhigang, Yang, Jufen
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Back propagation; Decision making; Flow theory; Kalman filters; Morlet wavelet; Network reliability; Neural networks; Particle swarm optimization; Performance indices; Short term; Support vector machines; System theory; Time series; Traffic congestion; Traffic flow; Traffic information; Travel; Volatility
• ISSN: 1026-0226; 1607-887X
• DOI: 10.1155/2018/7650928

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.