Volume 4, Issue 1, January 2019, Page: 7-16
Spatial and Temporal Analysis of Seasonal Traffic Accidents
Homayoun Harirforoush, Department of Applied Geomatics, University of Sherbrooke, Quebec, Canada
Lynda Bellalite, Department of Applied Geomatics, University of Sherbrooke, Quebec, Canada
Goze Bertin Bénié, Department of Applied Geomatics, University of Sherbrooke, Quebec, Canada
Received: Jan. 17, 2019;       Accepted: Feb. 21, 2019;       Published: Apr. 26, 2019
DOI: 10.11648/j.ajtte.20190401.12      View  27      Downloads  18
Abstract
This paper presents an approach to analyze spatial and temporal (spatiotemporal) patterns of traffic accidents and to organize them according to their level of significance. This approach was tested using three years (2011-2013) of traffic accident data for Sherbrooke. The spatiotemporal patterns of traffic accidents were analyzed using kernel density estimation (KDE) for four different seasons. Two different crash measures were compared: simple crash counts and severity-weighted crash counts. The results show that severity-weighted crash counts reveal the effect of a single fatal/severe injury or light injury crash on the patterns. However, the lack of a significance test is the main drawback of the KDE. Therefore, this paper integrates the KDE with local Moran’s I to identify clusters of statistical significance for traffic accidents within each area. Thus, after the density is calculated by the KDE, it is then applied as the attribute (input value) for calculating local Moran’s I. Our findings show that the method was successful to detect traffic accident clusters and hazardous areas in Sherbrooke.
Keywords
Geographic Information Systems (GIS), Kernel Density Estimation (KDE), Traffic Accidents, Spatiotemporal Analysis, Hotspot, Local Moran’s I
To cite this article
Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié, Spatial and Temporal Analysis of Seasonal Traffic Accidents, American Journal of Traffic and Transportation Engineering. Vol. 4, No. 1, 2019, pp. 7-16. doi: 10.11648/j.ajtte.20190401.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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