Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway |
| |
| Affiliation: | 1. Turkish National Police, Traffic Research Center, Ankara, Turkey;2. Vienna University of Technology, Department of Spatial Planning, Vienna, Austria;3. Middle East Technical University, Geodetic and Geographic Information Technologies, Ankara, Turkey;4. Turkish-German University, Faculty of Engineering, İstanbul, Turkey;5. Gazi University, City and Regional Planning, Ankara, Turkey;1. Graduate Research Assistant, Department of Civil, Structural and Environmental Engineering, Engineering Statistics and Econometrics Application Research Laboratory, University at Buffalo – The State University of New York, 204 Ketter Hall, Buffalo, NY 14260, USA;2. Assistant Professor, Department of Transportation and Hydraulic Engineering, School of Rural & Surveying Engineering, Aristotle University of Thessaloniki, Thessaloniki 541 24, Greece;3. Assoc. Professor and Stephen E. Still Chair of Transportation Engineering, Department of Civil, Structural and Environmental Engineering, Stephen Still Institute for Sustainable Transportation and Logistics, University at Buffalo – The State University of New York, 212 Ketter Hall, Buffalo, NY 14260, USA;4. Frederick R. Dickerson Chair and Professor, School of Civil and Environmental Engineering and H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, 790 Atlantic Drive, Atlanta, GA 30332, USA;1. Shamoon College of Engineering, Department of Industrial Engineering and Management, 84 Jabotinski St., Ashdod 77245, Israel;2. Ben Gurion University of the Negev, Department of Industrial Engineering and Management, P.O.B. 653, Beer Sheva 84105, Israel;1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei, Anhui, China;2. Anhui Provincial Traffic Survey and Design Institute Co., Hefei, Anhui, China;3. Centre for Accident Research and Road Safety-Queensland, Queensland University of Technology (QUT), Brisbane, Queensland, Australia;4. School of Transportation, Southeast University, Nanjing, Jiangsu, China;1. Department of Civil Engineering and Applied Mechanics, McGill University, Room 391, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec, H3A 0C3, Canada;2. Department of Civil Engineering and Applied Mechanics, McGill University, Room 268, Macdonald Engineering Building, 817 Sherbrooke Street West, Montréal, Québec, H3A 0C3, Canada;3. Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, C.P. 6079, succ. Centre-Ville, Montréal, Québec, H3C 3A7, Canada;4. IFSTTAR-TS2-LMA, 304 Chemin de la Croix Blanche, F-13300 Salon de Provence, France;1. LAMIDED, Universite de Sousse, Sahloul 4, BP 526 Sousse, Tunisia;2. College of Administrative Sciences, Najran University, BP. 1988 Najran, Saudi Arabia |
| |
| Abstract: | Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested. |
| |
| Keywords: | Driver characteristics Driver choices Behavioral factors in driving Spatio-temporal accident analysis Hotspot identification Network kernel density estimation |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
