title: Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations.
abstract: Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Cong
authors.initials: C
authors.lastname: Chen
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: guohui@unm.edu.
authors.forename: Guohui
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Civil and Environmental Engineering, University of Nevada, Reno, MS 0258, Reno, NV 89557, USA.
authors.forename: Zong
authors.initials: Z
authors.lastname: Tian
authors.affiliation: Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Bogus
authors.affiliation: Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
authors.forename: Yin
authors.initials: Y
authors.lastname: Yang
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D009516:New Mexico; D012424:Rural Population / Q000706:statistics & numerical data*; D012622:Seat Belts / Q000706:statistics & numerical data*; D012720:Severity of Illness Index; D014505:Urban Population / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: Bayesian inference; Cross-level interaction; Random intercept model; Traffic safety; Truck driver injury; Unobserved heterogeneity