Comparison of Traffic Speed and Travel Time Predictions on Urban Traffic Network

Many intelligent transportation systems (ITS) often rely on prediction of traffic variables in the near future to provide useful information for the users. Therefore, accurate traffic prediction is essential in the development of ITS. In this study, we are comparing prediction models for traffic speed and travel time on urban traffic network. We present three ways to build travel time predictors: by using recent travel time and speed of neighboring links, by using recent travel time of individual links, and by using speed predictors. Model-tree ensemble is implemented for both traffic speed and travel time predictions. Experimental result shows that the predictors perform better on travel time prediction than on traffic speed prediction. Among the three discussed travel time prediction methods and two baseline predictors, travel time prediction via speed predictors achieves best accuracy in all tested paths and prediction horizons.