Real-Time Short-Term Traffic Flow Forecasting Based on Process Neural Network

Authors:
Shan He;Cheng Hu;Guo-Jie Song;Kun-Qing Xie;Yi-Zhou Sun
Affiliations:
Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing 100871;Key Laboratory of Machine Perception (Peking University), Ministry of Education, Beijing 100871;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, USA
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

Existing short-term Traffic flow forecasting models have not fully considered the characteristics of spatio-temporal process and online analysis, we imported the process neural network which can model spatio-temporal process well into short-term traffic forecasting. The model use wavelet radix as weighted function expanding radix of process neurons to deal with the inputs on multi-scale. By using principal component analysis to consider the space affect of traffic flow, the model was optimized. In addition, online learning algorithm of the model was proposed. The experimental results show that the forecasting accuracy of the model is better than ordinary neural networks, and the model can meet the demand of real-time forecasting of short-term traffic flow.