Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method

• 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.

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.

Figure optionsDownload as PowerPoint slide