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Identification of Critical Links in Urban Road Network Based on GIS

Author

Listed:
  • Jingwen Yuan

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Hualan Wang

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Yannan Fang

    (China Railway Design Corporation, Tianjin 300308, China)

Abstract

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

Suggested Citation

  • Jingwen Yuan & Hualan Wang & Yannan Fang, 2023. "Identification of Critical Links in Urban Road Network Based on GIS," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14841-:d:1259066
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    References listed on IDEAS

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    3. Feng, Huifang & Bai, Fengshan & Xu, Youji, 2019. "Identification of critical roads in urban transportation network based on GPS trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
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