Highway Travel Time Prediction Between Stations Based on Toll Ticket Data

Abstract:

Due to an insufficient section inspection data, toll ticket data were resorted to predicting the travel time between highway toll stations. First, a research was made into a processing method to modify the toll ticket data on real time, and an average travel time calculation model was developed. Then, in order to decrease the model deviation caused by Kalman filter model linearization, a piecewise linear interpolation method was introduced to build the Kalman filter model. Finally, the application system was developed according to the travel time prediction business logic, the system could accurately predict the travel time between highway toll stations on real time. Actual road application shows that the interpolation algorithm can improve travel time prediction accuracy compared to the conventional Kalman filter method in the normal, accident and holiday traffic flow. The relative error of all prediction periods is less than 10%, and the relative error of accident prediction periods is less than 13%. The prediction accuracy of interpolation algorithm is improved effectively, which can provide an effective time reference for public in highway.