The objective of this work, a simulator experiment was conducted with 55 participants and data from 165 trips were collected and analyzed. Key explanatory variables related to risk and the most reliable indicators of task complexity (e.g. weather, time of the day) and coping capacity (e.g. headway, speed, harsh brakings) were evaluated. In order to evaluate the model, several goodness-of-fit measures were used. These include GFI and AGFI, which assess how well the model matches the observed covariance matrix and RMSEA, where values ≤0.05 indicate a close fit. Comparative indices such as CFI and TLI compare the hypothesised model against an independence model, with values above 0.90 considered indicative of good fit. Lastly, AIC and BIC were examined to balance model fit with model complexity, with lower values reflecting better models. Results showed that distance and duration were positively correlated with task complexity. Hands-on events and fatigue were positively associated with coping capacity, suggesting that fatigued drivers may adopt more cautious behaviours. In contrast, TTC and average speed were negatively correlated with coping capacity, indicating reduced ability to manage driving demands at longer TTC and higher speeds. Overall, the study offers a holistic view of driver safety by integrating driver, vehicle and environmental factors within a unified mobility resilience framework. The STZ proved effective for understanding how drivers respond to changing task demands and coping requirements. Future work with larger, more diverse datasets could improve the generalisability and applicability of this approach.
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