In a short time, the COVID-19 pandemic turned into a global emergency. The fear of becoming infected and the lockdown measures have drastically changed people's daily routine. The aim of this study is to establish the psychological impact that the COVID-19 pandemic is entailing, particularly with regards to levels of stress, anxiety and depression, and to the risks of developing Post-Traumatic Stress Disorder (PTSD). The study, carried out with a sample of 1612 subjects distributed in seven countries (Australia, China, Ecuador, Iran, Italy, Norway and the United States), allowed us to collect information about the psychological impact of COVID-19. The findings of this study show that the levels of stress, depression and anxiety, as well as the risks of PTSD, are higher than average in over half of the considered sample. The severity of these disorders significantly depends on gender, type of outdoor activities, characteristics of their homes, eventual presence of infected acquaintances, time dedicated to looking for related information (in the news and social networks), type of source information and, in part, to the level of education and income. We conclude that COVID-19 has a very strong psychological impact on the global population. This appears to be linked to the coping strategies adopted, level of mindful awareness, socio-demographic variables, people's habits and the way individuals use means of communication and information.

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains.

As largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.

This study investigates the empirical presence of a theoretical transportation paradox, defined as the “Online Information Paradox” (OIP). The paradox suggests that, for certain road networks, the provision of online information deteriorate travel conditions for all users of that network relative to the situation where no online information is provided to users. The analytical presence of the paradox was derived for a specific network structure by using two equilibrium models, the first being the Expected User Equilibrium (EUE) solution (no information scenario) and the other being the User Equilibrium with Recourse (UER) solution (with information scenario). An incentivised computerised route choice game was designed using the concepts of experimental economics and administered in a controlled laboratory environment to investigate the physical presence of the paradox. Aggregate statistics of path flows and Total System Travel Costs (TSTC) were used to compare the experimental results with the theoretical findings. A total of 12 groups of 12 participants completed the experiment and the OIP and the occurrence of the OIP being significant was observed in 11 of the 12 cases. Though information increased travel costs for users on average, it reduced the volatility of travel costs experienced in the no information scenario indicating that information can achieve a more reliable system. Further replications of similar experiments and more importantly field based identification of the phenomena will force transport professionals to be aware of the emergence of the paradox. In addition, studies such as this emphasise the need for the adoption of adaptive traffic assignment techniques to appropriately model the acquisition of information on a road network.