Human factors studies are becoming more and more crucial in the automotive sector due to the need to evaluate the drivers reactions to the increasingly sophisticated driving assistant technologies. Driving simulators allow performing this kind of study in a controlled and safe environment. However, the driving simulation's Level of Detail (LOD) can affect the users perception of driving scenarios and make an experimental campaigns outcomes unreliable. This paper proposes a study investigating possible correlations between drivers behaviors and emotions, and simulated driving scenarios. Four scenarios replicating the same real area were built with four LODs from LOD0 (only the road is drawn) to LOD3 (all buildings with real textures for facades and roofs are inserted together with items visible from the road). 32 participants drove in all the four scenarios on a fixed-base driving simulator; their performance relating to the vehicle control (i.e. speed, trajectory, brake and gas pedal use, and steering wheel), their physiological data (electrodermal activity, and eye movements), their subjective perceptions, opinions and emotional state (questionnaires concerning the research and Self-Assessment Manikin Scale) were measured. The results showed that drivers behavior changes in a very complex way. Geometrical features of the route and environmental elements constrain much more driving behavior than LOD does, as observed for vehicle trajectory and speed; skin conductance and gas pedal appear to be more sensitive to LODs. Gaze position changes according to LODs, but differently for rectilinear and curvilinear segments. Pupil diameter increases with higher LODs. Emotions are not affected by LODs. Generally, different signals showed different correlations with the LOD level, suggesting that future studies should consider their measures while modeling the virtual scenario. It is hypothesized that scenario realism is more relevant during leisurely environmental interaction, whilst simulator fidelity is crucial in task-driven interactions.

This paper introduces a new methodology to evaluate the performance of rural road networks when a link in the network is disrupted due to events such as natural disasters, accidents, and maintenance closures. As a measure of network resilience, we propose a simpler approach to quantify redundancy by introducing two indices that link the concept of road network redundancy with the increase in travel distance of the overall network when a road link fails. We apply the problem formulation to a real-world rural road network which was heavily affected by the 2015 Gorkha Earthquake in Nepal. Based on the results from our analyses, we found these indices easy to use, pragmatic, and reliable for the case under study. With the proposed tool, decision-makers can predict and monitor the performance of rural road networks for pre-disaster (or pre-disruption) planning, thereby ensuring the smooth connectivity for goods and services during emergencies.

COVID-19 measures implied many changes to travel behaviour and transport mode choice during the pandemic. This study seeks to understand what individual characteristics and travel attributes are related to transport mode choice before, during, and after the first lockdown in Italy. Based on an online survey (carried out in May 2020 in Milan), three multinomial regression models are presented. The results show that and in which measure parameters regarding distance and duration of daily travel are markedly related to transport mode before the lockdown. However, these factors are less significantly associated with the transport mode during and after the lockdown. Meanwhile, factors such as Preferences and Worry about using public transport have more significant relationship with the modal choice during the pandemic. Regarding individual characteristics, women are more likely to use active mode during and after the lockdown. Additionally, two personality traits of Agreeableness, and Openness to experiences are related to transport mode during and after the lockdown, respectively. Overall, this study reveals that in addition to socio-demographic factors, other variables such as worry about using public transport, preference, and personality are associated with the choice of transport mode during the lockdown.