Tendency-Based Approach for Link Travel Time Estimation

Abstract

From the historical bus trajectories, it was found that the headway bias amplified as buses travel on the route. When controls on buses are unavailable, the following buses will maintain the movement tendency toward their previous one, whether being closer to, farther away, or stable, judged by the running state in which buses fall. A tendency-based model for link travel time estimation was proposed, and three tendency-based corrections were introduced in the model, which are the long-term tendency, the short-term tendency, and the combined-term tendency. Then, contrast experiments were conducted in which the boundary of the running state is a control variable to show the performance of the tendency-based model under different boundary values. The experiment results show that, with the increase of the boundary value, the degree of improvement of the tendency-based mode to the historical data–based model first increases, then decreases, and converges to zero finally. The optimal boundary value for the tendency-based model was calibrated, judged by the net number of trips improved and net mean absolute error reduced, and the results show that the long-term and the combined-term tendency-based models have a lower optimal boundary and higher optimization potential, and are faster to be steady enough, which made the short-term tendency-based model less competitive.