Spatial and Temporal Analysis of Seasonal Traffic Accidents

Homayoun Harirforoush; Lynda Bellalite; Goze Bertin Bénié

doi:doi:10.11648/j.ajtte.20190401.12

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Spatial and Temporal Analysis of Seasonal Traffic Accidents

Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié

Published in American Journal of Traffic and Transportation Engineering (Volume 4, Issue 1)

Received: 17 January 2019 Accepted: 21 February 2019 Published: 26 April 2019

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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.

Published in	American Journal of Traffic and Transportation Engineering (Volume 4, Issue 1)
DOI	10.11648/j.ajtte.20190401.12
Page(s)	7-16
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Geographic Information Systems (GIS), Kernel Density Estimation (KDE), Traffic Accidents, Spatiotemporal Analysis, Hotspot, Local Moran’s I

References

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APA Style

Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié. (2019). Spatial and Temporal Analysis of Seasonal Traffic Accidents. American Journal of Traffic and Transportation Engineering, 4(1), 7-16. https://doi.org/10.11648/j.ajtte.20190401.12

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ACS Style

Homayoun Harirforoush; Lynda Bellalite; Goze Bertin Bénié. Spatial and Temporal Analysis of Seasonal Traffic Accidents. Am. J. Traffic Transp. Eng. 2019, 4(1), 7-16. doi: 10.11648/j.ajtte.20190401.12

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AMA Style

Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié. Spatial and Temporal Analysis of Seasonal Traffic Accidents. Am J Traffic Transp Eng. 2019;4(1):7-16. doi: 10.11648/j.ajtte.20190401.12

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@article{10.11648/j.ajtte.20190401.12,
  author = {Homayoun Harirforoush and Lynda Bellalite and Goze Bertin Bénié},
  title = {Spatial and Temporal Analysis of Seasonal Traffic Accidents},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {4},
  number = {1},
  pages = {7-16},
  doi = {10.11648/j.ajtte.20190401.12},
  url = {https://doi.org/10.11648/j.ajtte.20190401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20190401.12},
  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.},
 year = {2019}
}

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TY - JOUR
T1 - Spatial and Temporal Analysis of Seasonal Traffic Accidents
AU - Homayoun Harirforoush
AU - Lynda Bellalite
AU - Goze Bertin Bénié
Y1 - 2019/04/26
PY - 2019
N1 - https://doi.org/10.11648/j.ajtte.20190401.12
DO - 10.11648/j.ajtte.20190401.12
T2 - American Journal of Traffic and Transportation Engineering
JF - American Journal of Traffic and Transportation Engineering
JO - American Journal of Traffic and Transportation Engineering
SP - 7
EP - 16
PB - Science Publishing Group
SN - 2578-8604
UR - https://doi.org/10.11648/j.ajtte.20190401.12
AB - 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.
VL - 4
IS - 1
ER -

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Author Information

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

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APA Style

Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié. (2019). Spatial and Temporal Analysis of Seasonal Traffic Accidents. American Journal of Traffic and Transportation Engineering, 4(1), 7-16. https://doi.org/10.11648/j.ajtte.20190401.12

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ACS Style

Homayoun Harirforoush; Lynda Bellalite; Goze Bertin Bénié. Spatial and Temporal Analysis of Seasonal Traffic Accidents. Am. J. Traffic Transp. Eng. 2019, 4(1), 7-16. doi: 10.11648/j.ajtte.20190401.12

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AMA Style

Homayoun Harirforoush, Lynda Bellalite, Goze Bertin Bénié. Spatial and Temporal Analysis of Seasonal Traffic Accidents. Am J Traffic Transp Eng. 2019;4(1):7-16. doi: 10.11648/j.ajtte.20190401.12

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@article{10.11648/j.ajtte.20190401.12,
  author = {Homayoun Harirforoush and Lynda Bellalite and Goze Bertin Bénié},
  title = {Spatial and Temporal Analysis of Seasonal Traffic Accidents},
  journal = {American Journal of Traffic and Transportation Engineering},
  volume = {4},
  number = {1},
  pages = {7-16},
  doi = {10.11648/j.ajtte.20190401.12},
  url = {https://doi.org/10.11648/j.ajtte.20190401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20190401.12},
  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.},
 year = {2019}
}

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