Abstract
The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness.
Highlights
With respect to urban security, logistics trajectories security plays an important role, which deserves a closer look
The algorithmic framework of this paper is introduced, including data preprocessing (DP), offline processing (OFP), the data manager (DM), and online processing (OLP). e purpose of DP is to obtain the effective loading and unloading positions of raw Global Positioning System (GPS) data. e goal of OFP is to obtain the available subtrajectories from the loading and unloading positions obtained from the DP and to mapping the subtrajectories to a preset grid. e goal of OLP is to real-timely meshing test trajectory points. e DM is mainly used for extraction of spatiotemporal experience constrained from the historical trajectories and detect abnormal trajectories
To detect abnormal trajectories of urban freight vehicles, the historical trajectories provided by a logistics company in Beijing were used in this paper
Summary
With respect to urban security, logistics trajectories security plays an important role, which deserves a closer look. Trajectory detection aims to find a trajectory that is significantly different from most of the historical trajectories with the same origin and destination It is mainly reflected in the process of freight vehicles driving from the station to the distribution point. In the field of anomaly detection of trajectory, many studies have been investigated from different perspectives [11,12,13], some of which are used to detect deceptive driving behavior in taxi drivers. These studies mainly focus on the spatial anomaly detection, and there are few related to the temporal anomaly detection. In terms of existing research methods, the research on space abnormal
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