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Discovering Spatio-Temporal Clusters of Road Collisions Using the Method of Fast Bayesian Model-Based Cluster Detection

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  • Yeran Sun

    (Department of Geography, College of Science, Swansea University, Swansea SA28PP, UK
    School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Yu Wang

    (Department of Urban and Rural Planning, School of Architecture, Tianjin University, Tianjin 300072, China)

  • Ke Yuan

    (Academy of China Open Economy Studies, University of International Business and Economics, Beijing 100029, China)

  • Ting On Chan

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

  • Ying Huang

    (School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

Public availability of geo-coded or geo-referenced road collisions (crashes) makes it possible to perform geovisualisation and spatio-temporal analysis of road collisions across a city. This study aims to detect spatio-temporal clusters of road collisions across Greater London between 2010 and 2014. We implemented a fast Bayesian model-based cluster detection method with no covariates and after adjusting for potential covariates respectively. As empirical evidence on the association of street connectivity measures and the occurrence of road collisions had been found, we selected street connectivity measures as the potential covariates in our cluster detection. Results of the most significant cluster and the second most significant cluster during five consecutive years are located around the central areas. Moreover, after adjusting the covariates, the most significant cluster moves from the central areas of London to its peripheral areas, while the second most significant cluster remains unchanged. Additionally, one potential covariate used in this study, length-based road density, exhibits a positive association with the number of road collisions; meanwhile count-based intersection density displays a negative association. Although the covariates (i.e., road density and intersection density) exhibit potential impact on the clusters of road collisions, they are unlikely to contribute to the majority of clusters. Furthermore, the method of fast Bayesian model-based cluster detection is developed to discover spatio-temporal clusters of serious injury collisions. Most of the areas at risk of serious injury collisions overlay those at risk of road collisions. Although not being identified as areas at risk of road collisions, some districts, e.g., City of London, are regarded as areas at risk of serious injury collisions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8681-:d:431507
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    References listed on IDEAS

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    1. 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.
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. 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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