Analysis of Factors Influencing the Vehicle Damage Level in Fatal Truck-Related Accidents and Differences in Rural and Urban Areas
AbstractAccidents involving large trucks very often end up with deadly consequences. Innocent people getting killed are acknowledged globally as one of the traffic safety greatest problems and challenges. While risk factors on truck-related accidents have been researched extensively, the impact on fatalities has received little or no attention, especially considering rural and urban areas, respectively. In this study, the generalized ordered logit model was used in Stata 11.0 to explore the complex mechanism of truck-related accidents in different areas. Data were obtained from The Trucks in Fatal Accidents database (TIFA). The Akaike Information Criterion (AIC) indicates that the model used in this paper is superior to traditional ordered logit model. The results showed that 9 variables affect the vehicle damage level in a fatal crash in both areas but with different directions. Furthermore, 23 indicators significantly affect the disabling damage in the same manner. Also, there are factors that are significant solely in one area and not in the other: 12 in rural and 2 in urban areas.
U.S.A. Large trucks: 2013 data. (Traffic Safety Facts. DOT HS 812 150). Washington, DC: National Highway Traffic Safety Administration; 2014.
Theofilatos A, Graham D, Yannis G. Factors affecting accident severity inside and outside urban areas in Greece. Traffic Injury Prevention. 2012;13(5):458-467.
Khorashadi A, Niemeier D, Shankar V, Mannering F. Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accident Analysis & Prevention. 2005;37(5):910-921.
Lemp JD, Kockelman KM, Unnikrishnan A. Analysis of large truck crash severity using heteroskedastic ordered probit models. Accident Analysis & Prevention. 2011;43(1):370-380.
Yasmin S, Eluru N. Evaluating alternate discrete outcome frameworks for modeling crash injury severity. Accident Analysis & Prevention. 2013;59:506-521.
Chiou YC, Hwang CC, Chang CC, Fu C. Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach. Accident Analysis & Prevention. 2013;51:175-184.
Jiang X, Huang B, Zaretzki RL, Richards S. Investigating the influence of curbs on single-vehicle crash injury severity utilizing zero-inflated ordered probit models. Accident Analysis & Prevention. 2013;57:55-66.
Yamamoto T, Hashiji J, Shankar VN. Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accident Analysis & Prevention. 2008;40(4):1320-1329.
Wang Z, Chen H, Lu J. Exploring impacts of factors contributing to injury severity at freeway diverge areas. Transportation Research Record: Journal of the Transportation Research Board. 2009;(2102):43-52.
Quddus MA, Wang C, Ison, SG. Road traffic congestion and crash severity: econometric analysis using ordered response models. Journal of Transportation Engineering. 2009;136(5):424-435.
Ye F, Lord D. Investigation of effects of underreporting crash data on three commonly used traffic crash severity models: Multinomial logit, ordered probit, and mixed logit. Transportation Research Record: Journal of the Transportation Research Board. 2011;(2241):51-58.
Chen H, Cao L, Logan DB. Analysis of risk factors affecting the severity of intersection crashes by logistic regression. Traffic Injury Prevention. 2012;13(3):300-307.
Chu HC. Assessing factors causing severe injuries in crashes of high-deck buses in long-distance driving on freeways. Accident Analysis & Prevention. 2014;62:130-136.
Scott Long J. Regression models for categorical and limited dependent variables. Advanced quantitative techniques in the social sciences; 1997.
Williams R. Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata Journal. 2006;6(1):58-82.
Wang X, Abdel-Aty M. Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models. Accident Analysis & Prevention. 2008;40(5):1674-1682.
Chen C, Zhang G, Tarefder R, Ma JM, Wei H, Guan HZ. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes. Accident Analysis & Prevention. 2015;80:76-88.
Sasidharan L, Menéndez M. Partial proportional odds model—An alternate choice for analyzing pedestrian crash injury severities. Accident Analysis & Prevention. 2014;72:330-340.
Bham GH, Javvadi BS, Manepalli URR. Multinomial logistic regression model for single-vehicle and multivehicle collisions on urban us highways in Arkansas. Journal of Transportation Engineering. 2011;138(6):786-797.
Kim JK, Ulfarsson F, Kim S, Shankar, VN. Driver-injury severity in single-vehicle crashes in California: a mixed logit analysis of heterogeneity due to age and gender. Accident Analysis & Prevention. 2013;50:1073-1081.
Zhang G, Wang Z, Persad KR, Walton, CM. Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas. Transportation. 2014;41(2):231-249.
Celik AK, Oktay E. A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey. Accident Analysis & Prevention. 2014;72:66-77.
Savolainen PT, Mannering FL, Lord D, Quddus MA. The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accident Analysis & Prevention. 2011;43(5):1666-1676.
Yasmin S, Eluru N, Bhat CR, Tay R. A latent segmentation based generalized ordered logit model to examine factors influencing driver injury severity. Analytic Methods in Accident Research. 2014;1:23-38.
Washington SP, Karlaftis MG, Mannering FL. Statistical and econometric methods for transportation data analysis. CRC press; 2010.
Lewis-Beck MS, Bryman A, Liao TF. Encyclopedia of social science research methods. Sage Publications; 2004.
Akaike H. Information theory and an extension of the maximum likelihood principle. In: Kotz S, Johnson NL, editors: Breakthroughs in statistics. New York: Springer, 1992. p. 610-624.
Kaplan S, Prato CG. Risk factors associated with bus accident severity in the United States: A generalized ordered logit model. Journal of Safety Research. 2012;43(3):171-180.
Elvik R. Does the influence of risk factors on accident occurrence change over time? Accident Analysis & Prevention. 2016;91:91-102.
Wang Y, Li L, Feng L, Peng H. Professional drivers' views on risky driving behaviors and accident liability: a questionnaire survey in Xining, China. Transportation Letters: The International Journal of Transportation Research. 2014;6(3):126-135.
Scott-Parker B, Watson B, King MJ, Hyde MK. “I drove after drinking alcohol” and other risky driving behaviours reported by young novice drivers. Accident Analysis & Prevention. 2014;70:65-73.
Gjerde H, Bogstrand ST, Lillsunde P. Commentary: Why is the odds ratio for involvement in serious road traffic accident among drunk drivers in Norway and Finland higher than in other countries? Traffic Injury Prevention. 2014;15(1):1-5.
Wang X, Kockelman K. Use of heteroscedastic ordered logit model to study severity of occupant injury: distinguishing effects of vehicle weight and type. Transportation Research Record: Journal of the Transportation Research Board. 2005;(1908):195-204.
Savolainen P, Mannering F. Probabilistic models of motorcyclists’ injury severities in single-and multi-vehicle crashes. Accident Analysis & Prevention. 2007;39(5):955-963.
Weiss HB, Kaplan S, Prato CG. Analysis of factors associated with injury severity in crashes involving young New Zealand drivers. Accident Analysis & Prevention. 2014;65:142-155.
Castillo-Manzano JI, Castro-Nuño M, Fageda X. Exploring the relationship between truck load capacity and traffic accidents in the European Union. Transportation Research Part E: Logistics and Transportation Review. 2016;88:94-109.
Roudsari BS, Mock CN, Kaufman R, Grossman D, Henary BY, Crandall J. Pedestrian crashes: higher injury severity and mortality rate for light truck vehicles compared with passenger vehicles. Injury Prevention. 2004;10(3):154-158.
McKnight AJ, Bahouth GT. Analysis of large truck rollover crashes. Traffic Injury Prevention. 2009;10(5):421-426.
Chen F, Chen S. Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways. Accident Analysis & Prevention. 2011;43(5):1677-1688.
Ma X, Chen F, Chen S. Empirical Analysis of Crash Injury Severity on Mountainous and Nonmountainous Interstate Highways. Traffic Injury Prevention. 2015;16(7):1-9.
Chang LY, Chen WC. Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research. 2005;36(4):365-375.
Lee JY, Chung JH, Son B. Analysis of traffic accident size for Korean highway using structural equation models. Accident Analysis & Prevention. 2008;40(6):1955-1963.
Xie Y, Zhang Y, Liang F. Crash injury severity analysis using Bayesian ordered probit models. Journal of Transportation Engineering. 2009;135(1):18-25.
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