Highlights CMFs for single and multiple treatments with FMNB models were developed. Safety effects of highway features were better reflected by FMNB models. FMNB models can capture nonlinear relationships. FMNB models produced more reasonable combined CMFs than NB. Combined CMFs may be under- or over-estimated with the current HSM method.
Abstract This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual’s method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.
Finite mixture modeling approach for developing crash modification factors in highway safety analysis
Accident Analysis and Prevention ; 97 ; 274-287
2016-10-18
14 pages
Article (Journal)
Electronic Resource
English
PART 2 — HIGHWAY SAFETY PERFORMANCE - Crash Modification Factors: Foundational Issues
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