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Location algorithm of transfer stations based on density peak and outlier detection

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Abstract

In the express industry, few people pay attention to the rationality of the layout of the transfer station at the end of rural express outlets. Aiming at the difficulty in selecting the location of express terminal transfer stations in rural areas, this paper presents a new transfer station location algorithm. This method is based on the reverse nearest neighbor algorithm and local density characteristics to initially screen the location of the transfer station in the area, and then determine its location and coverage area based on the density from the appropriate distance. Finally, by calculating the outlier index of each station in the distribution area, the boundary of the distribution area is redefined. Experiments show that the express transfer stations selected by this method can meet the service requirements of 98.8% of the area and 99.5% of the population in Yutian County, and the division accuracy of the distribution range has been improved to a certain extent compared with the traditional algorithm. In particular, for the selection of small-range peak points, the accuracy of the traditional density peak algorithm has been significantly improved.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (no.51974131), by Science and technology project of Hebei Education Department (no.BJ2017021), by NCST Natural Science Funds for Distinguished Young Scholars (no. JQ201711), by Hebei Province Natural Science Fund for Excellent Young Scholars (no. E2018209248), by Hebei Provincial postgraduate demonstration course project in 2020 (no. KCJSX2020053), by NCST Project establishment and construction of postgraduate demonstration course, by National Undergraduate Innovation and Entrepreneurship Plan (no.202010081027).

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Authors and Affiliations

  1. North China University of Science and Technology, Tangshan, 063210, Hebei, China

    Yan Shao-hong, Niu Jia-yang, Chen Tai-long, Liu Qiu-tong, Yang Cen, Cheng Jia-qing & Fu Zhi-zhen

  2. Hebei Key Laboratory of Data Science and Application, Tangshan, 063210, Hebei, China

    Yan Shao-hong

  3. College of Metallurgy and Energy, North China University of Science and Technology, Tangshan, 063210, China

    Li Jie

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  1. Yan Shao-hong
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Shao-hong, Y., Jia-yang, N., Tai-long, C. et al. Location algorithm of transfer stations based on density peak and outlier detection. Appl Intell 52, 13520–13532 (2022). https://doi.org/10.1007/s10489-022-03206-y

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