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Identification of critical roads in urban transportation network based on GPS trajectory data

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  • Feng, Huifang
  • Bai, Fengshan
  • Xu, Youji

Abstract

This study proposes a novel identification method of critical roads based on the combination of GPS trajectory data and directed weighted complex network. First, with both the static road network topology and the dynamic traffic flow characteristics taken into account simultaneously, a new spatial temporal model of urban transportation, namely a directed weighted complex network, is proposed. Then, combining the structure of road network with the strength influence of traffic between adjacent roads, a mixed influence-based identification algorithm of critical roads is proposed. Finally, we analyze taxi-GPS trajectory data collected in Lanzhou, China. We perform a comprehensive analysis to visualize the spatial–temporal changes of taxi services, critical roads, and critical intersections. Moreover, the correlation coefficient has been used to evaluate the performance of the identification algorithm of critical roads. The results show that the new identification algorithm is more effective and practical than traditional congestion index analysis. Our investigation should be helpful in urban traffic management and the residential choice of alternative routes.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119313470
    DOI: 10.1016/j.physa.2019.122337
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    1. 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.
    2. Yasir Tariq Mohmand & Aihu Wang, 2014. "Complex Network Analysis of Pakistan Railways," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-5, March.
    3. Yingying Xing & Jian Lu & Shendi Chen, 2016. "Weighted Complex Network Analysis of Shanghai Rail Transit System," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-8, August.
    4. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
    5. Du, Wen-Bo & Liang, Bo-Yuan & Hong, Chen & Lordan, Oriol, 2017. "Analysis of the Chinese provincial air transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 579-586.
    6. Sullivan, J.L. & Novak, D.C. & Aultman-Hall, L. & Scott, D.M., 2010. "Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 323-336, June.
    7. Feng, Shumin & Hu, Baoyu & Nie, Cen & Shen, Xianghao, 2016. "Empirical study on a directed and weighted bus transport network in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 85-92.
    8. Michael Taylor & Somenahalli Sekhar & Glen D'Este, 2006. "Application of Accessibility Based Methods for Vulnerability Analysis of Strategic Road Networks," Networks and Spatial Economics, Springer, vol. 6(3), pages 267-291, September.
    9. Sergio Porta & Paolo Crucitti & Vito Latora, 2006. "The Network Analysis of Urban Streets: A Primal Approach," Environment and Planning B, , vol. 33(5), pages 705-725, October.
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    Cited by:

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    2. Jiayu Liu & Xiangyu Yang & Shaobin Ren, 2023. "Research on the Impact of Heavy Rainfall Flooding on Urban Traffic Network Based on Road Topology: A Case Study of Xi’an City, China," Land, MDPI, vol. 12(7), pages 1-18, July.
    3. Gaspare D’Amico & Roberta Arbolino & Lei Shi & Tan Yigitcanlar & Giuseppe Ioppolo, 2021. "Digital Technologies for Urban Metabolism Efficiency: Lessons from Urban Agenda Partnership on Circular Economy," Sustainability, MDPI, vol. 13(11), pages 1-23, May.
    4. D’Amico, Gaspare & Arbolino, Roberta & Shi, Lei & Yigitcanlar, Tan & Ioppolo, Giuseppe, 2022. "Digitalisation driven urban metabolism circularity: A review and analysis of circular city initiatives," Land Use Policy, Elsevier, vol. 112(C).
    5. 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.
    6. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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