Global Positioning System and other location-based services record vehicles' spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space-time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network-time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network-time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.
|Original language||English (US)|
|Number of pages||19|
|Journal||Transportation Research Part C: Emerging Technologies|
|State||Published - May 1 2016|
Bibliographical noteFunding Information:
The material in this paper is based on research supported by National Science Foundation – United States under Grant No. BCS-1224102 “Measuring the Environmental Costs of Space–time Prisms in Sustainable Transportation Planning”. Appendix A
The material in this paper is based on research supported by National Science Foundation ? United States under Grant No. BCS-1224102 ?Measuring the Environmental Costs of Space?time Prisms in Sustainable Transportation Planning?.
© 2015 Elsevier Ltd.
- Dynamic shortest path
- GPS map matching
- Traffic state estimation
- Uncertainty estimation