Published In
Analytic Methods in Accident Research
Document Type
Pre-Print
Publication Date
6-1-2026
Subjects
Crash Severity, Temporal Instability, Population Heterogeneity, Scenario Analysis.
Abstract
Temporally shifting parameters in crash severity modeling is a well-documented phenomenon, with growing evidence suggesting that the influence of explanatory variables changes over time due to shifts in driver behavior, vehicle technology, and roadway conditions. Many studies have examined this issue by comparing the temporal stability of adjacent-year data using a variety of modeling frameworks that have often assumed a homogenous effect of explanatory variables across the entire crash population. The current research effort departs from past work in two ways. First, it compares data separated by multiple years (instead of comparing adjacent years), and second, it tests the homogeneous effect assumptions by estimating a latent segmentation-based pooled generalized ordered logit model. Using crash-level data from the National Automotive Sampling System/Crashworthiness Data System for the years 2003, 2009, and 2015 (each data wave separated by 5 years), the performance of the proposed latent segmentation model is evaluated against several traditional alternatives to assess their predictive and behavioral relevance. Various model fit measures demonstrate the improved performance of the proposed modeling approach, thus highlighting the importance of capturing population heterogeneity in temporal analyses. Interestingly, the latent segmentation modeling framework reveals that temporal variation in injury-severity outcomes (over the extended time intervals considered) is largely confined to a specific segment of crashes involving what would be considered low-injury risk, while the higher-injury risk group has parameters that remain stable across years. A marginal effect and scenario-based policy analysis provide additional insights. The results suggest that certain crash/individual profiles may be more susceptible to temporal shifts, and hence, accounting for such heterogeneity can be critical for developing targeted and effective safety interventions.
Rights
Copyright (c) 2026 The Authors
DOI
10.1016/j.amar.2026.100428
Persistent Identifier
https://archives.pdx.edu/ds/psu/44717
Publisher
Elsevier BV
Citation Details
Published as: Bhowmik, T., Pervaz, S., Mannering, F., & Eluru, N. (2026). An exploratory latent segmentation approach to account for temporally shifting parameters in driver injury-severity models. Analytic Methods in Accident Research, 50, 100428.
Description
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as: (2026). An exploratory latent segmentation approach to account for temporally shifting parameters in driver injury-severity models. Analytic Methods in Accident Research, 50, 100428.