TY - JOUR
T1 - Estimating the most likely space-time paths, dwell times and path uncertainties from vehicle trajectory data
T2 - A time geographic method
AU - Tang, Jinjin
AU - Song, Ying
AU - Miller, Harvey J.
AU - Zhou, Xuesong
N1 - Funding 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
Funding 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?.
Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - 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.
AB - 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.
KW - Dynamic shortest path
KW - GPS map matching
KW - Traffic state estimation
KW - Uncertainty estimation
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U2 - 10.1016/j.trc.2015.08.014
DO - 10.1016/j.trc.2015.08.014
M3 - Article
AN - SCOPUS:84941248446
SN - 0968-090X
VL - 66
SP - 176
EP - 194
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
ER -