Exploring the heterogeneous relationship between abnormal driving events and freeways crash risk: A two stage analysis framework integrating causal inference and random parameters logit
DOI: 10.1016/j.aap.2026.108557
URL: https://doi.org/10.1016/j.aap.2026.108557
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Abstract
Two-stage analytical framework examining abnormal driving events (ADEs) and crash risk on freeways. Stage 1: causal forest with debiased ML (CF-DML) to quantify ADE exposure effects on crash risk. Stage 2: random parameters logit model (RPLHM) for pre-crash ADE distribution patterns. Results: hard acceleration/braking positively associated with crash risk with context-specific heterogeneity; sharp turning correlates with reduced risk. Temperature, traffic volume, truck proportion, and speed dispersion moderate ADE-crash relationship. Three prototypical risk contexts identified: ADE-informative, pre-crash ADE-dense, ADE-sparse.
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Publisher: Elsevier