Graphical abstract Display Omitted
Highlights Develop a time-dependent graph model to estimate their likely space–time paths. Find network-time paths, link travel times and dwell times at possible intermediate stops. Develop a dynamic programming algorithm for both offline and real-time applications. Use the potential path area for all feasible network–time paths to estimate path uncertainty.
Abstract 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.
Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method
Tramsportation Research, Part C: Emerging Technologies ; 66 ; 176-194
2015-08-25
19 pages
Article (Journal)
Electronic Resource
English
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