Identifying the Differences in the Causal Factors of Truck-Involved Crashes in Rural and Urban Areas

Identifying the Differences in the Causal Factors of Truck-Involved Crashes in Rural and Urban Areas

Donghyung Yook Jun Lee Sunnie Haam

Korea Research Institute for Human Settlements, Republic of Korea

Korea Transport Institute, Republic of Korea

University of Seoul, Republic of Korea

Available online: 
| Citation



Various factors such as speed, variability of speed, traffic flows and the proportion of trucks affect the probability of truck-involved crashes. Numerous attempts have been made to identify the causal factors of truck-involved crashes, such as traffic volume, speed characteristics and geometric characteristics. Most of the research focused on identifying the causal factors or establishing models to represent the relationship between crashes and the identified factors. However, few studies have compared the differences in the impact of a coefficient by the type of region. This study aims to analyse the differences in the causal factors of truck-involved crashes in rural and urban areas. The applicability of the count models is examined owing to the low number of trucks involved in the crashes. The models for each area type are established using zero-inflated Poisson regression and negative binomial regression model for rural and urban areas, respectively. Our results indicate that sight distance is the single factor responsible for truck-involved crashes in rural areas, whereas annual average daily traffic, shoulder width and alignment are the contributors to truck-involved crashes in urban areas.


count model, Poisson regression, truck-involved crashes, zero inflated regression


[1] Mihan, M., Mahdi, R., Khaled, K., An investigation of influential factors of downgradetruck crashes: a logistic regression approach. Journal of Traffic and TransportationEngineering, 6(2), pp. 185–195, 2019.

[2] Zou, W, Wang, X, Zhang, D., Truck crash severity in New York city: an investigationof the spatial and the time of day effects. Accident Analysis & Prevention, 99, pp. 249–261, 2017.

[3] Muhammad, T.H., Milan, Z., Khaled, K., Investigating occupant injury severity oftruck-involved crashes based on vehicle types on a mountainous freeway: a hierarchicalBayesian random intercept approach. Accident Analysis & Prevention, 144, pp. 105654,2020.

[4] Blower, D., Green P. E., Matteson A., Condition of trucks and truck crash involvement:evidence from the large truck crash causation study. Transportation Research Record:Journal of the Transportation Research Board, 2194(1), pp. 21–28, 2010.

[5] Ali, B., Al-Bdairi, N.S.S., Determinant of injury severities in large truck crashes: aweekly instability analysis. Safety Science, 131, pp. 104911, 2020.

[6] Bhaven, N., Tung, L.W., Shanshan, Z et al., Weather impacts on single-vehicle truckcrash injury severity. Journal of Safety Research, 58, pp. 57–65, 2016.

[7] Mouyid, B.I., Salvador, H., Modeling Injury outcomes of crashes involving heavyvehicles on Texas highways. Transportation Research Record, 2388, pp. 28–36, 2013.

[8] Dong, C., David, B.C., Stephen, H.R. et al., Differences in passenger car and large truckinvolved crash frequencies at urban signalized intersections: an exploratory analysis.Accident Analysis & Prevention, 62, pp. 87–94, 2014.

[9] Mohamed, M.A., Rebecca, F., Khaled, K. et al., Effects of truck traffic on crash injuryseverity on rural highways in Wyoming using Bayesian binary logit models. AccidentAnalysis & Prevention, 117, pp. 106–113, 2018.

[10] David, E.C., Thomas, M.C., Curtis, M.G. et al., A driver focused truck crash predictionmodel. Transportation Research Part E: Logistics and Transportation Review, 46, pp.683–692, 2010.

[11] Hyun, K., Jeong, K.S., Tok, A. et al., Assessing crash risk considering vehicleinteractions with trucks using point detector data. Accident Analysis & Prevention, 130,pp. 75–83, 2019.

[12] Craft, R., Federal motor carrier safety administration research and technology: todayand tomorrow. Proceedings of the 84th Annual Meeting of the Transportation ResearchBoard. Washington, DC, 2005.

[13] Milhan, M., Mahdi, R., Mustaffa, N.R., Khaled, K., Predicting injury severity and crashfrequency: insights into the impacts of geometric variables on downgrade crashes inWyoming. Journal of Traffic and Transportation Engineering, 7, pp. 375–383, 2020.

[14] Matteson, A., Jarossi, L., Woodrooffe, J., Trucks Involved in Fatal Accidents Factbook2002, The University of Michigan, Transportation Research Institute: Ann Arbor, MI,2004.

[15] Matteson A., Jarossi L., Woodrooffe J., Trucks involved in Fatal Accidents Factbook2005, The University of Michigan, Transportation Research Institute: Ann Arbor, MI,2008.

[16] Khorashadi, A., Niemeier, D., Shankar, V., et al., Differences in rural and urban driverinjuryseverities in accidents involving large-trucks: an exploratory analysis. AccidentAnalysis & Prevention, 37, pp. 910–921, 2005.

[17] US General Accounting Office (GAO), Report to Congressional Committees, Federaland State Efforts to Address Rural Road Safety Challenges, Washington, DC: GAO,May 2004 (GAO-04-663).

[18] Uddin, M., Huynh, N., Truck-involved crashes injury severity analysis for differentlighting conditions on rural and urban roadways. Accident Analysis & Prevention, 108,pp. 44–55, 2017.

[19] Zhu, X., Srinivasan, S., A comprehensive analysis of factors influencing the injuryseverity of large-truck crashes. Accident Analysis & Prevention, 43, pp. 49–57, 2011.

[20] Lemp, J.D., Kockelman, K.M., Unnikrishnan, A., Analysis of large truck crash severityusing heteroskedastic ordered probit models. Accident Analysis & Prevention, 43, pp.370–380, 2011.

[21] Ahmed, M.M., Franke, R., Ksaibati, K., Shinstine, D.S., Effects of truck traffic on crashinjury severity on rural highways in Wyoming using Bayesian binary logit models.Accident Analysis & Prevention, 117, pp. 106–113, 2018.

[22] W. Horrey, C. Wickens Multiple resource modeling of task interference in vehiclecontrol, hazard awareness and in-vehicle task performance. Proceedings of the Second International Driving Symposium on Human Factors in Driver Assessment, Trainingand Vehicle Design, Park City, UT (2003).

[23] J. Lee, F. Mannering Impact of roadside features on the frequency and severity of runoff-roadway accidents: an empirical analysis. Accident Analysis & Prevention, 34(2),pp. 149–161, 2002.

[24] Nabeel Saleem, S.A.B., Salvador, H., Comparison of contributing factors for injuryseverity of large truck drivers in run-off-road crashes on rural and urban roadways:accounting for unobserved heterogeneity. International Journal of TransportationScience and Technology, 9, pp. 116–127, 2020.

[25] Golob, F.T., Regan, A.C., Traffic Conditions and Truck Accidents on Urban Freeways.University of California, Institute of Transportation Studies: Irvine, CA, 2004.

[26] Greene, W. H. Accounting for excess zeros and sample selection in Poisson and negativebinomial regression models, NYU Working Paper, No. EC-94-10. New York, NY, 1994.

[27] Cohen, A. C. Estimation in mixtures of discrete distributions. Proceedings of theInternational Symposium on Discrete Distributions, Montreal, 373–378, 1963.

[28] Lambert, D. Zero-inflated Poisson regression, with an application to defects inmanufacturing, Technometrics, 34, pp. 1–14, 1992.