Abstract: Anomalous trajectory detection is an important approach to detecting taxi fraud behaviors in urban traffic systems. The existing methods usually ignore the integration of the trajectory access location with the time and trajectory structure, which incorrectly detects normal trajectories that bypass the congested road as anomalies and ignores circuitous travel of trajectories. Therefore, this study proposes an anomalous trajectory detection algorithm using the popular routes in different traffic periods to solve this problem. First, to obtain popular routes in different time periods, this study divides the time according to the time distribution of the traffic trajectories. Second, the spatiotemporal frequency values of the nodes are obtained by combining the trajectory point moments and time span to exclude the interference of the temporal anomaly trajectory on the frequency. Finally, a gridded distance measurement method is designed to quantitatively measure the anomaly between the trajectory and the popular routes by combining the trajectory position and trajectory structure. Extensive experiments are conducted on real taxi trajectory datasets; the results show that the proposed method can effectively detect anomalous trajectories. Compared to the baseline algorithms, the proposed algorithm has a shorter running time and a significant improvement in F-Score, with the highest improvement rate of 7.9%, 5.6%, and 10.7%, respectively.

Lina Xu, Yonglong Luo, Qingying Yu, Xiao Zhang, Wen Zhang and Zhonghao Lu, “Anomalous Taxi Trajectory Detection using Popular Routes in Different Traffic Periods” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140739

@article{Xu2023,
title = {Anomalous Taxi Trajectory Detection using Popular Routes in Different Traffic Periods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140739},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140739},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Lina Xu and Yonglong Luo and Qingying Yu and Xiao Zhang and Wen Zhang and Zhonghao Lu}
}