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  543      Downloads  123
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.
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 © 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.
World Health Organization (WHO). Global status report on road safety 2015. Available at: http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/ (Accessed in 22/06/2016).
Transport Canada. 2011. Motor vehicle safety with support from the public health agency of Canada. Fact Sheet TP 15145E, Motor Vehicle Safety Directorate.
Khan, G., Qin, X. and Noyce, D. A., 2008. Spatial analysis of weather crash patterns. Journal of Transportation Engineering, 134(5), pp.191-202.
Anderson, T. K., 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41(3), pp.359-364.
Borruso, G., 2005, May. Network density estimation: analysis of point patterns over a network. In International Conference on Computational Science and Its Applications (pp. 126-132). Springer Berlin Heidelberg.
Harirforoush, H. and Bellalite, L., 2016. A new integrated GIS-based analysis to detect hotspots: a case study of the city of Sherbrooke. Accident Analysis & Prevention. Steenberghen, T., Aerts, K. and Thomas, I., 2010. Spatial clustering of events on a network. Journal of Transport Geography, 18(3), pp.411-418.
Sugihara, K., Satoh, T. and Okabe, A., 2010, October. Simple and unbiased kernel function for network analysis. In Communications and Information Technologies (ISCIT), 2010 International Symposium on (pp. 827-832). IEEE.
Vemulapalli, S. S., 2015. GIS-based spatial and temporal analysis of aging-involved crashes in Florida (Doctoral dissertation, THE FLORIDA STATE UNIVERSITY).
Young, J. and Park, P. Y., 2014. Hotzone identification with GIS-based post-network screening analysis. Journal of Transport Geography, 34, pp.106-120.
Xie, Z. and Yan, J., 2008. Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), pp.396-406.
Loo, B. P. and Yao, S., 2013. The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment. Computers, Environment and Urban Systems, 41, pp.249-261.
Gatrell, A. C., Bailey, T. C., Diggle, P. J. and Rowlingson, B. S., 1996. Spatial point pattern analysis and its application in geographical epidemiology. Transactions of the Institute of British geographers, pp.256-274.
Barnao, V., 2009. Analysis of Single Vehicle Crashes Using Geospatial Techniques, 1999–2008. Undergraduate Project. Curtin University of Technology.
Lu, Y. and Chen, X., 2007. On the false alarm of planar K-function when analyzing urban crime distributed along streets. Social Science Research, 36(2), pp.611-632.
Yamada, I. and Thill, J. C., 2004. Comparison of planar and network K-functions in traffic accident 597 analysis. Journal of Transport Geography, 12(2), pp.149-158.
Erdogan, S., 2009. Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. Journal of safety research, 40(5), pp.341-351.
Silverman, B. W., 1986. Density estimation for statistics and data analysis (Vol. 26). CRC press.
Chainey, S. and Ratcliffe, J., 2013. GIS and crime mapping. John Wiley & Sons.
Brunsdon, C., 2001. The comap: exploring spatial pattern via conditional distributions. Computers, environment and urban systems, 25(1), pp.53-68.
Ljubič, P., Todorovski, L., Lavrač, N. and Bullas, J. C., 2002. Time-series analysis of uk traffic accident data. In Proceedings of the Fifth International Multi-conference Information Society (pp. 131-134).
El-Sadig, M., Norman, J. N., Lloyd, O. L., Romilly, P. and Bener, A., 2002. Road traffic accidents in the United Arab Emirates: trends of morbidity and mortality during 1977–1998. Accident Analysis & Prevention, 34(4), pp.465-476.
Lavrac, N., Jesenovec, D., Trdin, N. and Kosta, N. M., 2008. Mining spatio-temporal data of traffic accidents and spatial pattern visualization. Metodoloski zvezki, 5(1), p.45.
Prasannakumar, V., Vijith, H., Charutha, R. and Geetha, N., 2011. Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia-Social and Behavioral Sciences, 21, pp.317-325.
Plug, C., Xia, J. C. and Caulfield, C., 2011. Spatial and temporal visualisation techniques for crash analysis. Accident Analysis & Prevention, 43(6), pp. 1937-1946.
Asgary, A., Ghaffari, A. and Levy, J., 2010. Spatial and temporal analyses of structural fire incidents and their causes: A case of Toronto, Canada. Fire Safety Journal, 45(1), pp.44-57.
Bíl, M., Andrášik, R. and Janoška, Z., 2013. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. Accident Analysis & Prevention, 55, pp. 265-273.
Jeefoo, P., Tripathi, N. K. and Souris, M., 2010. Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand. International journal of environmental research and public health, 8(1), pp.51-74.
Nie, K., Wang, Z., Du, Q., Ren, F. and Tian, Q., 2015. A network-constrained integrated method for detecting spatial cluster and risk location of traffic crash: A case study from Wuhan, China. Sustainability, 7(3), pp.2662-2677.
Meng, Q., 2016. The spatiotemporal characteristics of environmental hazards caused by offshore oil and gas operations in the Gulf of Mexico. Science of The Total Environment, 565, pp.663-671.
Anselin, L., 1995. Local indicators of spatial association—LISA. Geographical analysis, 27(2), pp.93-115.
Fotheringham, A. S., Brunsdon, C. and Charlton, M., 2000. Quantitative geography: perspectives on spatial data analysis. Sage.
O'Sullivan, D. and Unwin, D., 2014. Geographic information analysis. John Wiley & Sons.
Cleveland, W. S., 1993. Visualizing data. Hobart Press.
Corcoran, J., Higgs, G., Brunsdon, C. and Ware, A., 2007. The Use of Comaps to Explore the Spatial and Temporal Dynamics of Fire Incidents: A Case Study in South Wales, United Kingdom. The Professional Geographer, 59(4), pp.521-536.
Cromley, E. K. and McLafferty, S. L., 2011. GIS and public health. Guilford Press.
Moons, E., Brijs, T. and Wets, G., 2009. Improving Moran’s I to Identify Hot Spots in Traffic Safety.
Xie, Z. and Yan, J., 2013. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. Journal of transport geography, 31, pp.64-71.
Agent, K. R., 1973. Evaluation of the High-Accident Location Spot-Improvement Program in Kentucky.
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