Travel Time Prediction for Time-Table-Based Vehicles Traveling on Known Routes

Abstract

This paper studies travel time prediction for time-table-based vehicles traveling on known routes using both the current and historical travel time information. In our experiment, the historical travel times stored in the database can be further dichotomized into uncongested (i.e., normal) and congested (i.e., abnormal) classes. With this fine-structured data of travel times, the method that embeds the empirical mode decomposition and grey theory (Chen and Wu, 2012) can be adopted to improve short-term travel time prediction. To demonstrate, using a real traffic data under abnormal traffic condition, this proposed method has been compared with the other four travel time prediction methods. The results show that our method is superior in terms of the mean absolute percentage error (MAPE) and hence more suitable to be applied in the real world.