Travel-Time Prediction With Support Vector Regression

Chih-Hsien Wu · Jan-Ming Ho · Dong-Hyun Lee

1,480 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.

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References

2010Travel time data collection handbook 2002Support Vector Machine Regression for Volatile Stock Market Prediction

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