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.
Highlights
The distribution of road collisions is spatially heterogenous as road collisions are more likely to cluster in certain places than in others [1,2,3]
As the potential covariates used in this study, length-based road density exhibits a positive association with the number of road collisions
As the potential covariates used in this study, length-based road density exhibits a positive association with the number of road collisions while count-based intersection density exhibits a negative association
Summary
The distribution of road collisions is spatially heterogenous as road collisions are more likely to cluster in certain places than in others [1,2,3]. A spatio-temporal analysis of road collision across a city can help: (1) investigate the associations between road collisions and environmental characteristics (e.g., road infrastructure, land use and demographics); and (2) identify areas with a high risk of road traffic safety issues. The former can offer empirical evidence on the necessity of traffic safety interventions (e.g., improving road infrastructure, reducing traffic speed, etc.). Point-level collision data enables us to identify spatial clustering of collisions without considering spatial distribution of “population”. The existing studies on collision cluster detection have three limitations: (1) they focus mainly on spatial cluster detection but have not been extended to spatio-temporal cluster detection; (2) they mainly choose residential population or working population to represent the “population variable” in the cluster detection setting while traffic flow volume can, better represent “population variable”; and (3) as Kulldorff’s spatial cluster detection methods are computationally demanding, they are not suitable for a large data set
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