Title: 
Travel Time Prediction with Support Vector Regression
 
Chun-Hsin Wu, Jan-Ming Ho, and D.T. Lee


Abstract

Travel time is a fundamental measure in transportation. Accurate travel 
time prediction is also crucial to the development of intelligent transportation 
systems and advanced traveler information systems. In this paper, 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 support 
vector regression will perform well for time series analysis. Compared to other 
baseline predictors, our results show that the SVR predictor can reduce 
significantly 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.

Appears in IEEE Trans. Intelligent Transportation Systems,
(5,4):276-281, Dec. 2004.