title: Indexing crash worthiness and crash aggressivity by vehicle type.
abstract: Crash aggressivity (CA), along with conventional crash worthiness (CW), has been recently studied to deal with the crash incompatibility between vehicles on roads. Clearly, injury severity depends on the attacking ability of striking vehicle as well as the protective ability of struck vehicle. This study proposes a systematic crash-based approach to index CA and CW of various vehicles. The approach deviates from existing methods in three aspects: (a) an explicit definition and specification in the model for CW and CA; (b) Bayesian hierarchical analysis to account for the crash-vehicle two-level data structure; (c) a five-level ordinal model to explicitly consider all levels of crash severity. The case study on major vehicle types illustrated the method and confirmed the consistency of results with previous studies. Both crash worthiness and crash aggressivity significantly vary by vehicle types, in which we identified the dominating effect of vehicle mass, and also highlighted the extraordinary aggressivity of Light Trucks and Vans (LTVs). While it was not surprising to identify least CA and CW of motorcycles, buses were unconventionally found to be less aggressive than other motor vehicles. The method proposed in this research is applicable to detailed crash-based vehicle inspection and evaluation.
authors.affiliation: Urban Transport Research Center, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China. huanghelai@alumni.nus.edu.sg
authors.forename: Helai
authors.initials: H
authors.lastname: Huang
authors.forename: Chowdhury
authors.initials: C
authors.lastname: Siddiqui
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdel-Aty
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality / Q000517:prevention & control / Q000706:statistics & numerical data*; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D003257:Consumer Product Safety; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D018986:Motor Vehicles / Q000592:standards* / Q000706:statistics & numerical data