The increasing concentration of human activities in cities has been leading to a worsening in air quality, thus negatively affecting the lives and health of humans living in urban contexts. Transport is one of the main sources of pollution in such environments. Several local authorities have therefore implemented strict traffic-restriction measures. The aim of this paper is to evaluate the effectiveness and limitations of these interventions, by analyzing the relationship between traffic flows and air quality. The used dataset contains concentrations of NO, NO₂, NOx and PM₁₀, vehicle counts and meteorology, all collected during the COVID-19 lockdown in the city of Padova (Italy), in which severe limitations to contain the spread of the virus simulated long and large-scale traffic restrictions in normal conditions. In particular, statistical tests, correlation analyses and multivariate linear regression models were applied to non-rainy days in 2020, 2018 and 2017, in order to isolate the effect of traffic. Analysis indicated that vehicle flows significantly affect NO, NO₂, and NOx concentrations, although no evidence of a relationship between traffic and PM₁₀ was highlighted. According to this perspective, measures to limit traffic flows seem to be effective in improving air quality only in terms of reducing nitrogen oxide.

The diffusion of the COVID-19 pandemic has induced fundamental changes in travel habits. Although many previous authors have analysed factors affecting observed variations in travel demand, only a few works have focused on predictions of future new normal conditions when people will be allowed to decide whether to travel or not, although risk mitigation measures will still be enforced on vehicles, and innovative mobility services will be implemented. In addition, few authors have considered future mandatory trips of students that constitute a great part of everyday travels and are fundamental for the development of society. In this paper, logistic regression models were calibrated by using data from a revealed and stated-preferences mobility survey administered to students and employees at the University of Padova (Italy), to predict variables impacting on their decisions to perform educational and working trips in the new normal phase. Results highlighted that these factors are different between students and employees; furthermore, available travel alternatives and specific risk mitigation measures on vehicles were found to be significant. Moreover, the promotion of the use of bikes, as well as bike sharing, car pooling and micro mobility among students can effectively foster sustainable mobility habits. On the other hand, countermeasures on studying/working places resulted in a slight effect on travel decisions.

Road network vulnerability analysis is helpful in the improvement of vulnerable links with proper maintenance investments and management strategies. This paper addresses issues that have received limited attention in past studies: estimation of travel demand after link disruption and analysis of accessibility variation. An activity-based model was used to estimate travel demand changes due to link closure, and link importance was evaluated using a set of vulnerability indicators. Accessibility changes induced by link closure are presented and discussed. Vulnerability analysis was conducted for the road network of the municipality of Dolo in northern Italy. Considering the spatial distribution of activities and trips, the results obtained with the activity-based model were more reliable than those obtained with the fixed demand model, which makes the unrealistic assumption of unchanged travel demand after network degradation. These findings are relevant for appropriate resource allocation strategies, which depend on correct link vulnerability analysis and ranking.

The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet of Things (IoT)-oriented wireless sensor network (WSN). Meanwhile, 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. Within this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traffic monitoring and management. Efficient sensor selection thus represents a critical concern while deploying WSNs on urban networks. In this paper, we provide an overview of such a notably hard problem. The contribution is twofold: (i) surveying state-of-the-art model-based techniques for efficient sensor selection in traffic flow monitoring, emphasizing challenges of sensor placement, and (ii) advocating for the development of data-driven methodologies to enhance sensor deployment efficacy and traffic modeling accuracy. Further considerations underscore the importance of data-driven approaches for adaptive transportation systems aligned with the IoV paradigm.

Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport.