Abstract
Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ a z-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employs k-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on the \(S_Dbw\) indicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.
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Acknowledgments
This research is sponsored by the Scientific Research Project of Sichuan Provincial Public Security Department under Grant No. 2015SCYYCX06, the Scientific Research Project of State Grid Sichuan Electric Power Company Information and Communication Company under Grant No. SGSCXT00XGJS1800219, the Science and Technology Planning Project of Sichuan Province under Grant No. 2017FZ0094, the Science and Technology Project of Chengdu under Grant No. 2017-RK00-00021-ZF, and the Joint Funds of the Ministry of Education of China.
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Zhu, J., Niu, X., Wu, C.Q. (2019). On a Clustering-Based Approach for Traffic Sub-area Division. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_45
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