Determination of the Risk Factors That Influence Occurrence Time of Traffic Accidents with Survival Analysis
AbstractAbstractBackground: This study aimed to determine risk factors that occurrence time of traffic accidents. Traffic accident occurrence time is defined as the time between a driver’s getting his/her license and having the first accident, involving death or injury between 2008-2012 and there were investigated.Methods: This study was conducted with the Cox Regression and life tables models included among survival analysis models. Data of all 11.671 traffic accidents in Kayseri in Turkey were analyzed for the 5-yr period.Results: The non-occurrence rate of traffic accidents involving injury is mostly affected by gender, age, education, number of vehicles involved in accident, road surface material, daylight, type of road, direction of road and time of the day. The non-occurrence rate of fatal traffic accident duration is mostly affected by gender, age, education, daylight and horizontal alignment. The rate of having an accident involving death or injury after getting driver's license is 30.3% in the first 5 yr, it is 50.1% in the first 10 yr and 91.7% in 25 yr.Conclusion: As the non-occurrence time increases, occurrence of accidents in earlier years will decrease. In other words, the number of accidents in earlier years will be lower. This will cause a decrease in the number of accidents in total.
World Health Organization (2013). Global status report on road safety. Switzerland: WHO Library. http://www.who.int/violence_injury_prevention/road_safety_status/2013/en/
2.Global Health Observator (2010). Road traffic deaths. http://www.who.int/gho/road_safety/en/index.html
Soysal Z, Çakalır C (1991). C. Adli Tıp Cilt III. İstanbul: İstanbul Üniversitesi Cerrahpaşa Tıp Fakültesi Yayınları Rektörlük No:4165, Fakülte No:224.
Turkish Statistical Institute. (2004). 3. Türkiye İstatistik Yıllığı, Ankara: T.C. Başbakanlık Devlet İstatistik Enstitüsü Yayın Numarası:2895, Devlet İstatistik Enstitüsü Matbaası.
Trafik Güvenliği Daire Başkanlığı (2013). Trafik Kazaları Özeti. Karayolları Genel Müdürlüğü. http://www.kgm.gov.tr/SiteCollectionDocuments/KGMdocuments/Trafik/TrafikKazalariOzeti2013.pdf
De Oña J, López G, Mujalli R, Calvo FJ (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accid Anal Prev, 51: 1-10.
Chimba D, Sando T (2009). The prediction of highway traffic accident injury severity with neuromorphic techniques. Advances in Transportation Studies, 2009(19): 17-26.
Haleem K, Abdel-Aty M (2010). Examining traffic crash injury severity at unsignalized intersections. J Safety Res, 41(4): 347-357.
Peek-Asa C, Britton C, Young T et al (2010). Teenage driver crash incidence and factors influencing crash injury by rurality. J Safety Res, 41 (6): 487–492.
Kononen DW, Flannagan C, Wong S (2011). Identification and validation of a logistic regression model for predicting serious injuries associated with motor vehicle crashes. Accid Anal Prev, 43(1): 112–122.
Moudon A, Lin L, Jiao J, Hurvitz P, Reeves P (2011). The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. Accid Anal Prev, 43(1): 11–24.
Tortum A, Atalay A (2015). Clustering analysis of traffic accident risk in Turkey. Iran J Public Health, 44(3): 425-6.
Malyshkina NV, Mannering FL (2010). Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents. Accid Anal Prev, 42(1): 131-139.
Rifaat SM, Tay R, De Barros A (2011). Effect of street pattern on the severity of crashes involving vulnerable road users. Accid Anal Prev, 43(1): 276-283.
Ye F, Lord D (2011). Investigation of effects of underreporting crash data on three commonly used traffic crash severity models: multinomial logit, ordered probit, and mixed logit. Transp Res Rec: Journal of the Transportation Research Board, 2241(1): 51-58.
Hu W, Donnell ET (2010). Median barrier crash severity: Some new insights. Accid Anal Prev, 42(6): 1697-1704.
Hashimoto S, Yoshiki S, Saeki R et al (2016). Development and application of traffic accident density estimation models using kernel density estimation. J Traffic Transp Eng Engl Ed, 3(3): 262-270.
Savolainen PT, Mannering FL, Lord D, Quddus MA (2011). The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid Anal Prev, 43(5): 1666-1676.
Mannering FL, Bhat CR (2014). Analytic methods in accident research: methodological frontier and future directions. Anal Methods Accid Res, 1: 1-22.
Haadi AR (2014). Identification of factors that cause severity of road accidents in ghana: a case study of the Northern Region. Int J Appl Sci Technol, 4(3): 242-249.
Ratanavaraha V, Suangka S (2014). Impacts of accident severity factors and loss values of crashes on expressways in Thailand. IATSS Research, 37(2): 130-136.
Tavakoli-Kashani A, Shariat-Mohaymany A, Ranjbari A (2011). A data mining approach to identify key factors of traffic injury severity. PROMET-Traffic&Transportation, 23(1): 11-17.
Tavakoli-Kashani, A, Shariat-Mohaymany, A Ranjbari A (2012). Analysis of factors associated with traffic injury severity on rural roads in Iran. J Inj Violence Res, 4(1): 36-41.
Harruff R, Avery A, Alter-Pandya A (1998). Analysis of circumstances and injuries in 217 pedestrian traffic fatalities. Accid Anal Prev, 30(1): 11–20.
Tiwari G, Bangdıwala S, Saraswat A, Gaurav S (2007). Survival Analysis: Pedestrian risk exposure at signalized intersections. Transp Res Part F Traffic Psychol Behav, 10(2): 77-89.
Kardiyen F, Kaygısız G (2011). Kırmızı ışık ihlali nedeni ile meydana gelen trafik kazalarının değerlendirilmesi. Karayolu Trafik Güvenlik Sempozyum ve Fuarı. 10-12 May. Ankara.
Roine M. (1999). Accident risk of car drivers in wintertime traffic. Finland: Technical Research Centre of Finland.
Guo H, Wang W, Guo W, Zhao F (2013). Modeling lane-keeping behavior of bicyclists using survival analysisi approach. Discrete Dyn Nat Soc, 2013:197518.
Ross M (2002). An analysıs of traffic deaths by vehicle type and model. Washington, D.C.: ACEEE Publications.
Kang G, Fang S (2011). Applying survival analysis approach to traffic incident duration prediction. American Society of Civil Engineers ICTIS, 1523-1531.
Wu J, Subramanian R, Craig M et al (2013). The effect of earlier or automatic collision notification on traffic mortality by survival analysis. Traffic Inj Prev,14 Suppl:S50-7.
Kachman S (1999). Applications in survival analysis. J Anim Sci, 77 Suppl 2:147-53.
Ducrocq V, Sölkner J (1994). The Survival Kit’-A Fortran Package For The Analysis Of Survival Data. Proc. 5th World Congr. Genet. Appl. Livest. Prod., (s. XXII: 51). Guelph.
Hintze J (2001). NCSS and PASS, Number Cruncher Statistical Systems. Kaysville, Utah.
Kanık A, Kul S (2012). Sağdan sansürlü gözlemlerin yerleşiminin hazard oranı tahminine etkisi. Turkiye Klinikleri J Biostat, 20-26.
Lee ET (1992). Statistical Methods for Survival Data Analysis. Second Edition. New York: John Wiley&Sons. Inc.
Cox DR, Oakes D (1984). Analysis of Survival Data. London: Chapman and Hall.
Collett D (2003). Modelling Survival Data in Medical Research, 2nd ed. New York: Chapman and Hall/CRC.
Machin D, Cheung Y, Parmar M (2006). Survival Analysis: A Practical Approach, 2nd edition. Chichester: John Wiley & Sons, Ltd.
Akyol M. Yaşam çözümlemesine yeni bir yaklaşım: Mars [Phd. Thesis]. Institute of Health Science, Ankara University. Ankara; 2011.
Cox DR (1972). Regression models and life tables. J R Stat Soc Series B Stat Methodol, 34(2): 187-220.
Alkaabi A, Dissanayake D, Bird R (2011). Analyzing clearance time of urban traffic accidents in Abu Dhabi, United Arab Emirates, with hazard-based duration modeling method. Transp Res Rec: Journal of the Transportation Research Board, 2229: 46-43.
Nam D. Mannering F (2000). An explorato-ry hazard-based analysis of highway inci-dent duration. Transp Res A Policy Pract, 34(2): 85-102.
Lee JT, Fazio J (2005). Influential factors in freeway crash response and clearance times by emergency management services in peak periods. Traffic Inj Prev, 6(4): 331-339.