This study explored the role of advanced simulator technologies in assessing risk factors associated with sustainable mobility in both urban and rural environment. A controlled experimental setup was conducted, leveraging simulator-based methodology to analyze key mobility risks. The research integrates real-world data from vehicles, drivers, road conditions and environmental factors to evaluate potential hazards and enhance transport safety. Within the framework of this work, a driving simulator experiment was implemented involving 55 drivers and 165 trips across different road environments. Taking into account that targeted risks typically occur in urban and rural areas, three location types were examined: six-lane two-way highways, rural undivided two-lane roads and urban single-lane roads. Risk levels were assessed using the STZ framework, categorizing driving behaviour into three levels: normal (low risk), dangerous (moderate risk) and avoidable accident (high risk). Through the application of SEM model, the structural model between the latent variables revealed some interesting results. Firstly, task complexity and coping capacity were inter-related with a negative correlation. This means that as the complexity of a task increases, an individual’s coping capacity tends to decrease. This negative correlation highlights the inverse relationship between the difficulty of tasks and the ability to handle stress effectively. The findings provided valuable insights for policymakers, transport planners and industry stakeholders, highlighting the potential of new vehicle technologies to support the transition toward sustainable mobility in both urban and rural areas. Simulator experiments offer a cost-effective, data-driven approach to evaluate risk factors and design evidence-based interventions.
| ID | pc572 |
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