Accid Anal Prev. 2026 Jan 20;228:108404. doi: 10.1016/j.aap.2026.108404. Online ahead of print.
ABSTRACT
Visual distraction is a major contributor to crash risk, particularly in car-following situations that demand continuous monitoring and rapid response. Although prior research using simulators and Naturalistic Driving Study (NDS) data has advanced our understanding, evidence remains limited on how visual distraction increases risk in real-world contexts and under which conditions it is amplified. Visual distraction is not an isolated factor, but a context-dependent phenomenon shaped by roadway conditions, traffic dynamics, and external stimuli. Beyond measuring its overall effect, it is essential to identify the circumstances in which visual distraction becomes especially hazardous. To address this gap, this study applies causal inference methods to NDS data. A Causal Forest was used to estimate the causal effect of visual distraction on two safety indicators: time-to-collision (TTC) and braking intensity. Subsequently, mediation analysis using Double Machine Learning (DML) was applied to disentangle the extent to which visual distraction mediates driving risk from the portion attributable directly to roadway and traffic conditions, thereby clarifying the indirect behavioral pathways versus structural design effects. Results show that visual distraction significantly reduces TTC, indicating heightened conflict seriousness, whereas its effect on braking intensity was not statistically significant. Mediation analysis further revealed that the effect of visual distraction on TTC varied across contexts, with stronger effects under high traffic density, ADAS-equipped vehicles, wider sidewalks, and fewer lanes. These findings underscore the importance of integrated safety strategies that mitigate visual distraction while also accounting for roadway design, traffic environment, and vehicle technologies in shaping driver behavior and risk.
PMID:41564451 | DOI:10.1016/j.aap.2026.108404
