Long-term travel time prediction using gradient boosting

Abstract

Reliable long-term travel time prediction would be effective support to traffic management, for example, traffic flow control or the pricing of tolls. Gradient boosting (GB) has been suggested as an excellent tool for short-term travel time prediction problems. This paper shows that GB with modifications can also work for long-term prediction. We introduce key variables, regarded as multiple factors, from various sources into a GB model and apply the Fourier filtering process to reduce noise. Those key variables include time of the day, day of the week, holidays, big events or activities, promotion of tolls, and narrowing of roadways. This paper takes the first step in applying key variables (from various sources) to predict long-term travel time. The electronic toll collection (ETC) data of Taiwan Freeway No.1 are used to train and to test in our process. Results demonstrate that the prediction ability of the GB model with Fourier filtering is the best. This paper shows a research direction of long-term travel time prediction, and the relatively important key variables.