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Formulating alcohol-influenced driver’s injury severities in intersection-related crashes

    Qiong Wu Affiliation
    ; Guohui Zhang Affiliation

Abstract

Approximately one third of all traffic fatal crashes are alcohol-related in the US according to the National Highway Traffic Safety Administration (NHTSA), alcohol-related crashes cost more than $37 billion annually. Considerable research efforts are needed to understand better significant causal factors for alcohol-related crash risks and driver’s injury severities in order to develop effective countermeasures and proper policies for system-wide traffic safety performance improvements. Furthermore, since two thirds of urban Vehicle Miles Traveled (VMT) is on signal-controlled roadways, it is of practical importance to investigate injury severities of all drivers who are involved in intersection-related crashes and their corresponding significant causal factors due to control and geometric impacts on flow progression interruptions. This study aims to identify and quantify the impacts of alcohol/non-alcohol-influenced driver’s behavior and demographic features as well as geometric and environmental characteristics on driver’s injury severities around intersections in New Mexico. The econometric models, multinomial Logit models, were developed to analyze injury severities for regular sober drivers and alcohol-influenced drivers, respectively, using the crash data collected in New Mexico from 2010 to 2011. Elasticity analyzes were conducted in order to understand better the quantitative impacts of these contributing factors on driver’s injury outcomes. The research findings provide a better understanding of contributing factors and their impacts on driver injury severities in crashes around intersections. For example, the probability of having severe injuries is higher for non-alcohol-influenced drivers when the drivers are 65 years old or older. Drivers’ left-turning action will increase non-alcohol-influenced driver injury severities in crash occurring around intersections. However, different characteristics are captured for alcohol-influenced drivers involved in intersection-related crashes. For example, more severe injuries of alcohol-influenced drivers can be observed around intersections with three or more lanes on each approach. The model specifications and estimation results are also helpful for transportation agencies and decision makers to develop cost-effective solutions to reduce alcohol-involved crash severities and improve traffic system safety performance.


First published online 29 February 2016

Keyword : injury severity, alcohol-influenced drivers, discrete choice model, intersection-related crashes, traffic safety

How to Cite
Wu, Q., & Zhang, G. (2018). Formulating alcohol-influenced driver’s injury severities in intersection-related crashes. Transport, 33(1), 165-176. https://doi.org/10.3846/16484142.2016.1144221
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Jan 26, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abdel-Aty, M. A.; Radwan, A. E. 2000. Modeling traffic accident occurrence and involvement, Accident Analysis & Prevention 32(5): 633–642. http://dx.doi.org/10.1016/S0001-4575(99)00094-9

Bédard, M.; Guyatt, G. H.; Stones, M. J.; Hirdes, J. P. 2002. The independent contribution of driver, crash, and vehicle characteristics to driver fatalities, Accident Analysis & Prevention 34(6): 717–727. http://dx.doi.org/10.1016/S0001-4575(01)00072-0

Chang, L.-Y.; Mannering, F. 1999. Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents, Accident Analysis & Prevention 31(5): 579–592. http://dx.doi.org/10.1016/S0001-4575(99)00014-7

Demetriades, D.; Gkiokas, G.; Velmahos, G. C.; Brown, C.; Murray, J.; Noguchi, T. 2004. Alcohol and illicit drugs in traumatic deaths: prevalence and association with type and severity of injuries, Journal of the American College of Surgeons 199(5): 687–692. http://dx.doi.org/10.1016/j.jamcollsurg.2004.07.017

Duncan, C.; Khattak, A.; Council, F. 1998. Applying the ordered probit model to injury severity in truck-passenger car rear-end collisions, Transportation Research Record: Journal of the Transportation Research Board 1635: 63–71. http://dx.doi.org/10.3141/1635-09

FHA. 2012. Highway Statistics 2010. US Department of Transportation, Federal Highway Administration (FHA), Washington, DC. Available from Internet: https://www.fhwa.dot.gov/policyinformation/statistics/2010

FHA. 2011. Our Nation’s Highway 2011. US Department of Transportation, Federal Highway Administration (FHA), Washington, DC. 64 p. Available from Internet: http://www.fhwa.dot.gov/policyinformation/pubs/hf/pl11028/onh2011.pdf

FHA. 2009. Introduction to Intersection Safety Issue Briefs. Federal Highway Administration Safety Program FHWA-SA-10-005, US Department of Transportation, Federal Highway Administration (FHA), Washington, DC.

Gray, R. C.; Quddus, M. A.; Evans, A. 2008. Injury severity analysis of accidents involving young male drivers in Great Britain, Journal of Safety Research 39(5): 483–495. http://dx.doi.org/10.1016/j.jsr.2008.07.003

Hu, W.; Donnell, E. T. 2011. Severity models of cross-median and rollover crashes on rural divided highways in Pennsylvania, Journal of Safety Research 42(5): 375–382. http://dx.doi.org/10.1016/j.jsr.2011.07.004

Huang, H.; Chin, H. C.; Haque, M. M. 2008. Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis, Accident Analysis & Prevention 40(1): 45–54. http://dx.doi.org/10.1016/j.aap.2007.04.002

IIHS & NHTSA. 2006. Beginning Teenage Drivers. Insurance Institute for Highway Safety (IIHS), National Highway Traffic Safety Administration (NHTSA). Washington, DC. 8 p.

Islam, S.; Mannering, F. 2006. Driver aging and its effect on male and female single-vehicle accident injuries: some additional evidence, Journal of Safety Research 37(3): 267–276. http://dx.doi.org/10.1016/j.jsr.2006.04.003

Khattak, A. J. 2001. Injury severity in multivehicle rear-end crashes, Transportation Research Record: Journal of the Transportation Research Board 1746: 59–68. http://dx.doi.org/10.3141/1746-08

Khorashadi, A.; Niemeier, D.; Shankar, V.; Mannering, F. 2005. Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis, Accident Analysis & Prevention 37(5): 910–921. http://dx.doi.org/10.1016/j.aap.2005.04.009

Kim, J.-K.; Kim, S.; Ulfarsson, G. F.; Porrello, L. A. 2007. Bicyclist injury severities in bicycle–motor vehicle accidents, Accident Analysis & Prevention 39(2): 238–251. http://dx.doi.org/10.1016/j.aap.2006.07.002

Kockelman, K. M.; Kweon, Y.-J. 2002. Driver injury severity: an application of ordered probit mode, Accident Analysis & Prevention 34(3): 313–321. http://dx.doi.org/10.1016/S0001-4575(01)00028-8

Lau, M.; May, A. 1989. Accident Prediction Model Development for Unsignalized Intersections: Final Report. Research Report UCB-ITS-RR-89-12. Institute of Transportation Studies, University of California, Berkeley, US. 298 p.

Maistros, A.; Schneider, W. H.; Savolainen, P. T. 2014. A comparison of contributing factors between alcohol related single vehicle motorcycle and car crashes, Journal of Safety Research 49: 129–135. http://dx.doi.org/10.1016/j.jsr.2014.03.002

NHTSA. 2009. Traffic Safety Facts. 2009 Data: Alcohol-Impaired Driving. US Department of Transportation, National Highway Traffic Safety Administration (NHTSA), Washington, DC. 6 p.

NMDOT. 2010. New Mexico Comprehensive Transportation Safety Plan: 2010 Update. New Mexico Department of Transportation (NMDOT). 116 p. Available from Internet: http://dot.state.nm.us/content/dam/nmdot/planning/NM_Comprehensive_Transportation_Safety_Plan.pdf

Neyens, D. M.; Boyle, L. N. 2007. The influence of driver distraction on the severity of injuries sustained by teenage drivers and their passengers, Accident Analysis & Prevention 40(1): 254–259. http://dx.doi.org/10.1016/j.aap.2007.06.005

O’Donnell, C. J.; Connor, D. H. 1996. Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice, Accident Analysis & Prevention 28(6): 739–753. http://dx.doi.org/10.1016/S0001-4575(96)00050-4

Pai, C.-W.; Hwang, K. P.; Saleh, W. 2009. A mixed logit analysis of motorists’ right-of-way violation in motorcycle accidents at priority T-junctions, Accident Analysis & Prevention 41(3): 565–573. http://dx.doi.org/10.1016/j.aap.2009.02.007

Persaud, B.; Lord, D.; Palmisano, J. 2002. Calibration and transferability of accident prediction models for urban intersections, Transportation Research Record: Journal of the Transportation Research Board 1784: 57−64. http://dx.doi.org/10.3141/1784-08

Poch, M.; Mannering, F. 1996. Negative binomial analysis of intersection-accident frequencies, Journal of Transportation Engineering 122(2): 105−113. http://dx.doi.org/10.1061/(ASCE)0733-947X(1996)122:2(105)

Renski, H.; Khattak, A. J.; Council, F. M. 1999. Effect of speed limit increases on crash injury severity: analysis of single-vehicle crashes on North Carolina interstate highways, Transportation Research Record: Journal of the Transportation Research Board 1665: 100–108. http://dx.doi.org/10.3141/1665-14

Retting, R. A.; Weinstein, H. B.; Solomon, M. G. 2003. Analysis of motor-vehicle crashes at stop signs in four U.S. cities, Journal of Safety Research 34(5): 485−489. http://dx.doi.org/10.1016/j.jsr.2003.05.001

Savolainen, P.; Mannering, F. 2007. Effectiveness of motorcycle training and motorcyclists’ risk-taking behavior, Transportation Research Record: Journal of the Transportation Research Board 2031: 52–58. http://dx.doi.org/10.3141/2031-07

Sayed, T.; Rodriguez, F. 1999. Accident prediction models for urban unsignalized intersections in British Columbia, Transportation Research Record: Journal of the Transportation Research Board 1665: 93−99. http://dx.doi.org/10.3141/1665-13

Shankar, V.; Mannering, F. 1996. An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity, Journal of Safety Research 27(3): 183–194. http://dx.doi.org/10.1016/0022-4375(96)00010-2

Smink, B. E.; Ruiter, B.; Lusthof, K. J.; De Gier, J. J.; Uges, D. R. A.; Egberts, A. C. G. 2005. Drug use and the severity of a traffic accident, Accident Analysis & Prevention 37(3): 427–433. http://dx.doi.org/10.1016/j.aap.2004.12.003

Train, K. E. 2009. Discrete Choice Methods with Simulation. 2nd edition. Cambridge University Press. 400 p.

Traynor, T. L. 2005. The impact of driver alcohol use on crash severity: a crash specific analysis, Transportation Research Part E: Logistics and Transportation Review 41(5): 421–437. http://dx.doi.org/10.1016/j.tre.2005.03.005

Ulfarsson, G. F.; Mannering, F. L. 2004. Differences in male and female injury severities in sport-utility vehicle, minivan, pickup and passenger car accidents, Accident Analysis & Prevention 36(2): 135–147. http://dx.doi.org/10.1016/S0001-4575(02)00135-5

Wang, J.; Zhang, G. 2011. Modeling and examining the teenage and adult freeway crash risks and injury severities in Washington State, Journal of Transportation Safety & Security 3(3): 207–221. http://dx.doi.org/10.1080/19439962.2011.591518

Washington, S. P.; Karlaftis, M. G.; Mannering, F. L. 2003. Statistical and Econometric Methods for Transportation Data Analysis. Chapman and Hall/CRC. 440 p.

Williams, T.; Alves, P.; Lachapelle, G.; Basnayake, C. 2012. Evaluation of GPS-based methods of relative positioning for automotive safety applications, Transportation Research Part C: Emerging Technologies 23: 98–108. http://dx.doi.org/10.1016/j.trc.2011.08.011

Yamamoto, T.; Shankar, V. N. 2004. Bivariate ordered-response probit model of driver’s and passenger’s injury severities in collisions with fixed objects, Accident Analysis & Prevention 36(5): 869–876. http://dx.doi.org/10.1016/j.aap.2003.09.002

Zador, P. L. 1991. Alcohol-related relative risk of fatal driver injuries in relation to driver age and sex, Journal of Studies on Alcohol and Drugs 52(4): 302–310. http://dx.doi.org/10.15288/jsa.1991.52.302