Spatio-Temporal Pedestrian Accident Analysis to Improve Urban Pedestrian Safety: The case of the Eskisehir Motorway

Abstract

Determining pedestrian accident hotspots on road segments is a crucial part of the pedestrian safety assessment as it is used to prioritize problematic parts of a road network for in particularly planning and implementation strategies. Moreover, the spatial pattern of the pedestrian accidents may change over time due to several factors.  In order to better understand pedestrian safety conditions, pedestrian accident patterns have to be analysed with regard to both space and time. This paper adapts such a spatio-temporal hotspot detection method for the analysis of pedestrian accidents. In this study, 189 traffic accidents involving pedestrians that resulted in injury or fatality on the Eskisehir motorway (Turkey) between the years of 2005 and 2010 are mapped with their spatial and temporal information. Network-based Kernel Density Estimation is used to examine the hotspots of pedestrian accidents and their changes over the years. Then, the significances of the results are evaluated by using Network-based Nearest Neighbor Distance and the K-function methods. The impact of land use change and taken measures are evaluated based on spatio-temporal hotspot analysis.

Keywords

• Spatial analysis
• pedestrian accident
• hotspot
• Eskişehir

References

1. Waddell, P., “UrbanSim: Modeling urban development for land use, transportation, and environmental planning”, Journal of the American Planning Association, 68(3): 297-314, (2002).
2. Dumbaugh, E. and Rae, R., “Safe urban form: revisiting the relationship between community design and traffic safety”, Journal of the American Planning Association, 75(3): 309-329, (2009).
3. Frank, L. D., “Land use and transportation interaction implications on public health and quality of life”, Journal of Planning Education and Research, 20(1): 6-22, (2000).
4. Durning, A.T., The Car and the City: 24 Steps to Safe Streets and Healthy Communities, Northwest Environment Watch, Seattle, Washington (1996).
5. World Health Organization, “Global status report on road safety 2013: supporting a decade of action”, WHO, Switzerland, (2013).
6. Turkish Statistical Institute, “Road traffic accident statistics for 2012”, TUIK, Ankara, Turkey, (2013).
7. World Health Organization, “Pedestrian Safety – A Road Safety Manual for Decision-Makers and Practitioners”, WHO, Switzerland, (2013).
8. Kaygısız, Ö., “Relationships Between Urban Land Use and Traffic Accidents: A Case Study of Eskisehir”, Ph.D. Thesis, Gazi University, Institute of Science and Technology, Ankara, (2012).

9. Kaygısız, Ö., “Locating The Traffic Accident Intervention Units In Metropolitan Areas: The Case of Ankara”, M.Sc. Thesis, Gazi University, Institute of Science and Technology, Ankara, (2012).
10. Rodrigues, D. S., Ribeiro, P. J. G. and da Silva Nogueira, I. C., “Safety classification using GIS in decision-making process to define priority road interventions”, Journal of Transport Geography, 43: 101-110, (2015).
11. Yannis, G., Weijermars, W., Gitelman, V., Vis, M., Chaziris, A., Papadimitriou, E. and Azevedo, C. L. “Road safety performance indicators for the interurban road network”, Accident Analysis & Prevention, 60: 384-395, (2013).
12. Geedipally, S. R. and Lord, D., “Identifying hot spots by modeling single-vehicle and multivehicle crashes separately”, Transportation Research Record: Journal of the Transportation Research Board, 2147(1): 97-104, (2010).
13. Xie, Z. and Yan, J., “Kernel density estimation of traffic accidents in a network space”, Computers, Environment and Urban Systems, 32(5): 396-406, (2008).
14. Kaygısız, Ö., Düzgün, Ş., Yildiz, A. and Senbil, M., “Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway”, Transportation Research Part F: Traffic Psychology and Behaviour, 33: 128-140, (2015).
15. Kaygısız, Ö., Düzgün, S., Akın, S. and Celik, Y., “Trafik kazalarinin zamansal ve mekânsal analizi yardimiyla sabit radara yer seçilmesi: Güney Anadolu Otoyolu örneği”, Road Traffic Safety Symposium, Ankara, Turkey, 1-23, (2011).
16. Kaygısız, Ö., Düzgün, S., Akın, S. and Celik, Y., Coğrafi Bilgi Sistemleri Kullanılarak Trafik Kazalarının Zamansal ve Mekansal Analizi, Aydoğdu Ofset, (2012).
17. Yalcin, G. and Duzgun, S., "Spatial analysis of two-wheeled vehicles traffic crashes: Osmaniye in Turkey", KSCE Journal of Civil Engineering, 1-8, (2015).
18. Bíl, M., Andrášik, R. and Janoška, Z., “Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation”, Accident Analysis & Prevention, 55: 265-273, (2013).
19. Young, J. and Park, P. Y., “Hotzone identification with GIS-based post-network screening analysis”, Journal of Transport Geography, 34: 106-120, (2014).
20. Ferreira, S. and Couto, A., “Hot-spot identification”, Transportation Research Record: Journal of the Transportation Research Board, 2386(1): 1-6, (2013).
21. Krisp, J. M. and Durot, S., “Segmentation of lines based on point densities - An optimisation of wildlife warning sign placement in southern Finland”, Accident Analysis and Prevention, 39(1): 38-46, (2007).
22. Longley, P., Goodchild, M. F., Maguire, D. J. and Rhind, D. W., Geographic information systems and science 2 nd ed., John Wiley and Sons, Chichester, (2005).
23. Schneider, R. J., Khattak, A. J. and Zegeer, C. V., “Method of improving pedestrian safety proactively with geographic information systems: example from a college campus”, Transportation Research Record: Journal of the Transportation Research Board, 1773(1): 97-107, (2001).
24. Jang, K., Park, S. H., Kang, S., Song, K. H., Kang, S. and Chung, S., “Evaluation of Pedestrian Safety”. Transportation Research Record: Journal of the Transportation Research Board, 2393(1): 104-116, (2013).
25. Blazquez, C. A. and Celis, M. S., “A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile”, Accident Analysis & Prevention, 50: 304-311, (2013).
26. Okabe, A., Okunuki, K. I. and Shiode, S., “SANET: a toolbox for spatial analysis on a network” Geographical Analysis, 38(1): 57-66, (2006).
27. Anderson, T. K., “Kernel density estimation and K-means clustering to profile road accident hotspots”, Accident Analysis and Prevention, 41(3): 359-364, (2009).
28. Okabe, A. and Yamada, I., “The K-function method on a network and its computational implementation”, Geographical Analysis, 33(3): 271-290, (2001).
29. Lord, D., “Analysis of pedestrian conflicts with left-turning traffic”, Transportation Research Record: Journal of the Transportation Research Board, 1538(1): 61-67, (1996).
30. Kaparias, I., Bell, M. G., Dong, W., Sastrawinata, A., Singh, A., Wang, X. and Mount, B., “Analysis of pedestrian-vehicle traffic conflicts in street designs with elements of shared space”, Transportation Research Record: Journal of the Transportation Research Board, 2393(1): 21-30, (2013).
31. SweRoad, “Highway Design Report, Appendix 4 – Proposed Design Principles for City Passings”, General Directorate on Highways, Ankara, Turkey, 3-20, (2000).