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Implementation of an integrated model for understanding the impact of task complexity and coping capacity on crash risk

2026/07/26 by Eva Michelaraki, George Yannis
Engineering · Psychology · Health Professions · #Traffic and Road Safety #Human-Automation Interaction and Safety #Older Adults Driving Studies

paper · doi:10.1016/j.aap.2026.108684

Abstract

This study investigates how task complexity and coping capacity interact to influence crash risk within the framework of the Safety Tolerance Zone (STZ). The STZ is defined as a dynamic condition in which the driver remains within acceptable boundaries and is operationalised primarily through headway, which was considered as an indicator of crash risk. This work aims to identify the interaction of road, vehicle and driver-related factors to the estimation of task complexity, coping capacity and risk. To this end, data from a naturalistic driving experiment involving 135 drivers and 31,954 trips collected over a four-month period were analysed across experimental phases incorporating real-time and post-trip interventions. Generalised Linear Models were used to examine the effect of explanatory variables on key driving behaviour indicators, while Structural Equation Models were applied to estimate the relationships among the latent constructs of task complexity, coping capacity and risk, expressed through STZ phases. The results showed that environmental factors, including time of day, weather, distance and duration, were positively associated with task complexity and increased crash risk. Driver-related and vehicle-related state factors influenced coping capacity, which was generally negatively associated with risk. The findings also indicated that the relationship between task complexity and coping capacity is dynamic, suggesting behavioural adaptation under more demanding driving conditions. Moreover, the intervention phases showed that real-time warnings and post-trip feedback contributed to safer driving behaviour, including greater headway and fewer harsh events. Overall, the study highlights the potential of data-driven interventions to improve road safety and support more effective driver assistance systems.

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