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Mining Raw Trajectories for Network Optimization from Operating Vehicles

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6GN for Future Wireless Networks (6GN 2020)

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Abstract

Improving the user peak rate in hot-spots is one of the original intention of design for 5G networks. The cell radius shall be reduced to admit less users in a single cell with the given cell peak rate, namely Hyper-Dense Networks (HDN). Therefore, the feature extraction of the node trajectories will greatly facilitate the development of optimal algorithms for radio resource management in HDN. This paper presents a data mining of the raw GPS trajectories from the urban operating vehicles in the city of Shenzhen. As the widely recognized three features of human traces, the self-similarity, hot-spots and long-tails are evaluated. Mining results show that the vehicles to serve the daily trip of human in the city always take a short travel and activate in several hot-spots, but roaming randomly. However, the vehicles to serve the goods are showing the opposite characteristics.

Sponsored by the Young Innovative Project from Guangdong Province of China (No. 2018KQNCX403) and the Teaching Reform Project from Shenzhen Technology University (No. 2018105101002).

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Authors and Affiliations

  1. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China

    Lei Ning, Runzhou Zhang & Fajun Li

  2. College of Applied Technology, Shenzhen University, Shenzhen, China

    Runzhou Zhang

  3. Chaincomp Technologies, Shenzhen, China

    Jing Pan

Authors
  1. Lei Ning
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  2. Runzhou Zhang
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  3. Jing Pan
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  4. Fajun Li
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Corresponding author

Correspondence to Lei Ning .

Editor information

Editors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, China

    Xiaofei Wang

  2. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

  3. School of Computer Science and Technology, Tianjin University, Tianjin, Tianjin, China

    Keqiu Li

  4. University of Science and Technology, Beijing, China

    Haijun Zhang

  5. Shenzhen University, Shenzhen, China

    Xiping Hu

  6. National University of Defense Technology, Changsha, China

    Qiang Liu

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ning, L., Zhang, R., Pan, J., Li, F. (2020). Mining Raw Trajectories for Network Optimization from Operating Vehicles. In: Wang, X., Leung, V.C.M., Li, K., Zhang, H., Hu, X., Liu, Q. (eds) 6GN for Future Wireless Networks. 6GN 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-030-63941-9_15

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  • DOI: https://doi.org/10.1007/978-3-030-63941-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63940-2

  • Online ISBN: 978-3-030-63941-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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