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Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm

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  • Jian Zhang

    (School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China
    State Grid Tianjin Electric Power Company, No. 39, Wujing Road, Hebei District, Tianjin 300160, China)

  • Shuai Ling

    (College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China)

  • Ping Wang

    (College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China)

  • Xiaoyang Hu

    (College of Management and Economics, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300192, China)

  • Lu Liu

    (School of Geographic and Environmental Sciences, Tianjin Normal University, No. 393, Binshui Road, Xiqing District, Tianjin 300382, China)

Abstract

Electronic maps play an important role in the field of urban traffic management, but the interface functions provided by map service agencies are limited, and commercial maps are generally expensive. Furthermore, the map generation algorithms based on the Global Positioning System (GPS) data can be very complex and take up a lot of storage space, which limits their application to specific practical problems, such as the real-time update of area maps, temporary road control, emergency route planning, and other scenarios. In order to solve this problem, an intuitive, extensible, and flexible method of constructing urban road maps is proposed. Using the Othello-coordinated method, the representation of the unit grid cell was redesigned. Through this method, the disadvantages of the raster map’s large storage space and computing resource requirements are compensated for during processing, improving the topological expression ability of the raster map and the speed with which the construction of the map is realized. The application potential of the proposed method is demonstrated by the evaluation of public transport service and road network resilience. In our experiments, the optimization efficiency of storage space was up to 99.914%, and the calculation accuracy of bus coverage was about 99.86%.

Suggested Citation

  • Jian Zhang & Shuai Ling & Ping Wang & Xiaoyang Hu & Lu Liu, 2021. "Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm," Land, MDPI, vol. 10(12), pages 1-17, December.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:12:p:1322-:d:693114
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    References listed on IDEAS

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    1. E. E. Koks & J. Rozenberg & C. Zorn & M. Tariverdi & M. Vousdoukas & S. A. Fraser & J. W. Hall & S. Hallegatte, 2019. "A global multi-hazard risk analysis of road and railway infrastructure assets," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. M.-B. Hu & R. Jiang & Y.-H. Wu & W.-X. Wang & Q.-S. Wu, 2008. "Urban traffic from the perspective of dual graph," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(1), pages 127-133, May.
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