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A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

Author

Listed:
  • Ke Nie

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Zhensheng Wang

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Qingyun Du

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Fu Ren

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

  • Qin Tian

    (School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079,China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China)

Abstract

Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration.

Suggested Citation

  • Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:3:p:2662-2677:d:46389
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    References listed on IDEAS

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    2. Zhensheng Wang & Yang Yue & Qingquan Li & Ke Nie & Wei Tu & Shi Liang, 2017. "Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
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    4. Miao, Congcong & Chen, Xiang & Zhang, Chuanrong, 2024. "Assessing network-based traffic crash risk using prospective space-time scan statistic method," Journal of Transport Geography, Elsevier, vol. 119(C).
    5. Yusheng Liang & Fan Zhang & Kun Yang & Zhenqi Hu, 2022. "A Surface Crack Damage Evaluation Method Based on Kernel Density Estimation for UAV Images," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    6. Shino Shiode & Narushige Shiode, 2022. "Network-Based Space-Time Scan Statistics for Detecting Micro-Scale Hotspots," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    7. Mingyu Kang & Anne Vernez Moudon & Haena Kim & Linda Ng Boyle, 2019. "Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS," IJERPH, MDPI, vol. 16(19), pages 1-14, September.
    8. Mirco Nanni & Leandro Tortosa & José F Vicent & Gevorg Yeghikyan, 2020. "Ranking places in attributed temporal urban mobility networks," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-25, October.
    9. Xiping Yang & Zhiyuan Zhao & Shiwei Lu, 2016. "Exploring Spatial-Temporal Patterns of Urban Human Mobility Hotspots," Sustainability, MDPI, vol. 8(7), pages 1-18, July.
    10. Shenjun Yao & Jinzi Wang & Lei Fang & Jianping Wu, 2018. "Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    11. Norhafizah Manap & Muhamad Nazri Borhan & Muhamad Razuhanafi Mat Yazid & Mohd Khairul Azman Hambali & Asyraf Rohan, 2021. "Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway," Sustainability, MDPI, vol. 13(3), pages 1-19, February.
    12. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    13. Yeran Sun & Yu Wang & Ke Yuan & Ting On Chan & Ying Huang, 2020. "Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    14. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    15. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.

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