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Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression

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  • Dong, Chunjiao
  • Nambisan, Shashi S.
  • Richards, Stephen H.
  • Ma, Zhuanglin

Abstract

Given the enormous losses to society resulting from large truck involved crashes, a comprehensive understanding of the effects of highway geometric design features on the frequency of truck involved crashes is needed. To better predict the occurrence probabilities of large truck involved crashes and gain direction for policies and countermeasures aimed at reducing the crash frequencies, it is essential to examine truck involved crashes categorized by collision vehicle types, since passenger cars and large trucks differ in dimensions, size, weight, and operating characteristics. A data set that includes a total of 1310 highway segments with 1787 truck involved crashes for a 4-year period, from 2004 to 2007 in Tennessee is employed to examine the effects that geometric design features and other relevant attributes have on the crash frequency. Since truck involved crash counts have many zeros (often 60–90% of all values) with small sample means and two established categories, car-truck and truck-only crashes, are not independent in nature, the zero-inflated negative binomial (ZINB) models are developed under the bivariate regression framework to simultaneously address the above mentioned issues. In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation. Goodness of fit of the investigated models is evaluated using AIC, SBC statistics, the number of identified significant variables, and graphs of observed versus expected crash frequencies. The bivariate ZINB (BZINB) models have been found to have desirable distributional property to describe the relationship between the large truck involved crashes and geometric design features in terms of better goodness of fit, more precise parameter estimates, more identified significant factors, and improved predictive accuracy. The results of BZINB models indicate that the following factors are significantly related to the likelihood of truck involved crash occurrences: large truck annual average daily traffic (AADT), segment length, degree of horizontal curvature, terrain type, land use, median type, lane width, right side shoulder width, lighting condition, rutting depth (RD), and posted speed limits. Apart from that, passenger car AADT, lane number, and indicator for different speed limits are found to have statistical significant effects on the occurrences of car-truck crashes and international roughness index (IRI) is significant for the predictions of truck-only crashes.

Suggested Citation

  • Dong, Chunjiao & Nambisan, Shashi S. & Richards, Stephen H. & Ma, Zhuanglin, 2015. "Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 30-41.
    • Handle: RePEc:eee:transa:v:75:y:2015:i:c:p:30-41
    DOI: 10.1016/j.tra.2015.03.007
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    References listed on IDEAS

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    1. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, January.
    2. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    3. Felix Famoye, 2010. "On the bivariate negative binomial regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 969-981.
    4. Wang, Peiming, 2003. "A bivariate zero-inflated negative binomial regression model for count data with excess zeros," Economics Letters, Elsevier, vol. 78(3), pages 373-378, March.
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    Cited by:

    1. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    2. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. Yuhuan Zhang & Huapu Lu & Wencong Qu, 2020. "Geographical Detection of Traffic Accidents Spatial Stratified Heterogeneity and Influence Factors," IJERPH, MDPI, vol. 17(2), pages 1-17, January.
    4. Bagloee, Saeed Asadi & Asadi, Mohsen, 2016. "Crash analysis at intersections in the CBD: A survival analysis model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 558-572.
    5. Francisco Calvo-Poyo & José Navarro-Moreno & Juan de Oña, 2020. "Road Investment and Traffic Safety: An International Study," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    6. Yuan, Quan & Wang, Jueyu, 2021. "Goods movement, road safety, and spatial inequity: Evaluating freight-related crashes in low-income or minority neighborhoods," Journal of Transport Geography, Elsevier, vol. 96(C).

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