Exploring the heterogeneous relationship between abnormal driving events and freeways crash risk: A two stage analysis framework integrating causal inference and random parameters logit.

Establishing the context-dependent relationship between abnormal driving events (ADEs) and crash risk is fundamental for developing next-generation proactive safety systems. However, current methodologies often oversimplify by neglecting that ADE risk significance is influenced by operating contexts, and by ignoring contextual heterogeneity in event evolution within fixed pre-crash windows-both crucial for understanding crash causation and timing interventions. This study proposes a two-stage analytical framework. First, a causal forest with debiased machine learning (CF-DML) approach is employed to quantify the effect of ADE exposure on crash risk and assess its effect heterogeneity across contexts. Second, a random parameters logit model with heterogeneity in means (RPLHM) is used to characterize pre-crash temporal patterns as either ADE-dense or ADE-sparse. The analysis utilizes crash and ADE data from 2023 to 2024 on two freeways in Shandong Province, China, integrated with matched weather, traffic, and roadway geometry. Results show that hard acceleration and braking are positively associated with crash risk under pronounced context-specific heterogeneity, while sharp turning consistently correlates with reduced risk. Several contextual variables-including temperature, traffic volume, truck proportion, and speed dispersion-significantly moderate the ADE-crash relationship. The pre-crash ADE distribution is closely linked to temperature, wind speed, daytime, traffic volume, speed dispersion, and crash type. Accordingly, three prototypical risk contexts are identified: ADE-informative (where ADE exposure strongly indicates crash risk), pre-crash ADE-dense, and pre-crash ADE-sparse. Targeted traffic management countermeasures proposed for each context advance the mechanistic understanding of crash risk and provide a foundation for developing targeted, context-sensitive, and effective safety interventions.