Abstract
Spatiotemporal trajectory data mining of vehicle groups is an important approach to identify the travel mode of mobile objects. A time series based fast density peaks clustering (TSDPC) algorithm aiming at rapid discovery of construction areas with respect to structural knowledge contained in the trajectory of engineering vehicles is proposed. Firstly, high-density areas extraction of engineering vehicles trajectory data characterized the low-frequency is accomplished by partition clustering to establish a cluster candidate set; Then the DPC algorithm based on the improved local density is utilized to conduct the parallel calculation of the candidate set in the region; Finally, the local clustering results are merged to complete global rapid clustering. The field experiment results demonstrate that the TSDPC algorithm improves the clustering efficiency of the spatiotemporal trajectory data of large-scale engineering vehicles group and effectively realizes the rapid discovery of construction areas.
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Acknowledgments
We would like to appreciate the constructive comments and financial supports from projects of Model analysis, visualization and decision optimization support for urban passenger flow data (No. 41471333), Smart passenger vehicle positioning terminal: [Grant number: GY-H-17040], Development and Demonstration Application of Fujian New Energy Vehicle Supervision Service System: [Grant number: GY-Z160141), RongJingXin Technology (No. 2017-1034). Beidou Navigation and Smart Traffic Innovation Center of Fujian Province is also acknowledged for supporting the experimental dataset.
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Gao, S., Zou, F., Liao, L., Zhang, M., Peng, Y., Song, S. (2019). Fast Clustering Algorithm for Construction Areas Based on Spatiotemporal Trajectory of Engineering Vehicles Group. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_5
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DOI: https://doi.org/10.1007/978-3-030-04585-2_5
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