Synchronized trajectory analysis of multi-sources tracking data from taxi drivers

doi:10.7523/j.ucas.2021.0078

Abstract

Abstract: Due to the shifts among partner taxi drivers, a taxi GNSS (global navigation satellite system) trajectory is usually not a driver's operational trajectory, and thus it is impossible to deeply analyze the mobile behavior characteristics of individuals or community with a single GNSS data source. Both a satellite navigation and positioning system and a ground mobile communication network can track and locate the moving objects on the road, forming the spatio-temporal trajectory data sources of different qualities. In this paper, we propose a novel synchronized trajectory analysis for multi-source temporal and spatial trajectories of taxi drivers, integrating the above two kinds of data to enhance trajectory semantics and extract taxi driver travel space. Based on the track of the points accumulated weighted similarity of similarity metrics, in which the spatial association analysis and homogeneity test analysis were carried out between a taxi GNSS trajectory and a mobile Cell-ID trajectory and correspondingly the association of "taxi-driver-cellphone" was reconstructed and the space-time position of the taxi driver's start-of-work and end-of-work was detected. The taxi GNSS data of Beijing Taxi and the mobile signaling data of Beijing Mobile collected on August 4, 2016 were used for experimental analysis. The statistical results show that the F₁ score of identifying cellphone Cell-ID trajectories by matching a GNSS trajectory is 0.91, and the F₁ score of recognizing cellphone user by clustering analysis is 0.94. The averaged time and space difference between drivers during their shifting a taxi are 1.5 h and 91 m respectively. Moreover, the handover points of taxi drivers are densely distributed nearby transportation hubs. The modeling results are highly consistent with the manually interpreted ones, well verifying the effectiveness of the proposed method.

Key words: taxi GNSS trajectory, cellphone Cell-ID trajectory, spatiotemporal similarity, association analysis, Pettitt’s test

CLC Number:

WANG Weifeng, HU Jinghao, HE Yan, SONG Xianfeng, RUI Xiaoping, LIU Junli, ZHU Kemin. Synchronized trajectory analysis of multi-sources tracking data from taxi drivers[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(3): 313-321.

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