A travel time estimation model for Route Guidance Systems

In this paper, several exiting methods of travel time estimation are analyzed. An isoparametric ratio travel time prediction model is formulated by using Kalman Filtering theory. The model can predict the travel time by using detected traffic volume on the basic link in the urban transportation networks. Since the traffic patterns are rather similar in the same weekdays of two consecutive weeks, the state vectors in the model are assumed stationary with respect to weekday. The travel time is predicted with the estimation of the ratio of travel times in two weekdays of two consecutive weeks. The model is validated with detected data collected in Changchun City. The results show that the new model is promising for practical application.