Simultaneously Prediction of Network Traffic Flow Based on PCA-SVR

Authors:
Xuexiang Jin;Yi Zhang;Danya Yao
Affiliations:
Department of Automation, Tsinghua University, Beijing, 100084, China;Department of Automation, Tsinghua University, Beijing, 100084, China;Department of Automation, Tsinghua University, Beijing, 100084, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

The ability to predict traffic variables such as speed, travel time and flow, based on real time and historic data, collected by various systems in transportation networks, is vital to the intelligent transportation systems (ITS). The present paper proposes a method based on Principal Component Analysis and Support Vector Regression (PCA-SVR) for a short-term simultaneously prediction of network traffic flow which is multidimensional compared with traditional single point. Data from a typical traffic network of Beijing City, China are used for the analysis. Other models such as ANN and ARIMA are also developed as a comparison of the performance of both these techniques is carried out to show the effectiveness of the novel method.