Trip-oriented travel time prediction (TOTTP) with historical vehicle trajectories

Abstract

Accurate travel time prediction is undoubtedly of importance to both traffic managers and travelers. In highly-urbanized areas, trip-oriented travel time prediction (TOTTP) is valuable to travelers rather than traffic managers as the former usually expect to know the travel time of a trip which may cross over multiple road sections. There are two obstacles to the development of TOTTP, including traffic complexity and traffic data coverage. With large scale historical vehicle trajectory data and meteorology data, this research develops a BPNN-based approach through integrating multiple factors affecting trip travel time into a BPNN model to predict trip-oriented travel time for OD pairs in urban network. Results of experiments demonstrate that it helps discover the dominate trends of travel time changes daily and weekly, and the impact of weather conditions is non-trivial.

Keywords

trip-oriented travel time prediction (TOTTP) / urban network / Back Propagation Neural Networks (BPNN) / historical vehicle trajectories

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Acknowledgements

This research was sponsored by the National Natural Science Foundation of China (Grant No. 41271441).

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg