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.

In this study, passive task-related fatigue effects on highway driving were analyzed by means of driving simulator experiments. Ten drivers were asked to drive in various environments in the morning (9:00-11:00 a.m.) and early afternoon (1:00-3:00 p.m.). Mean of Absolute Steering Error and Standard Deviation of Lateral Position, calculated on sub-intervals of 4 minutes, were analysed as response variables. The results confirmed the negative influence of the duration of driving tasks and circadian effects on driving performance, increasing the likelihood of “near misses” and accidents

This work contributes to study the application of extreme value theory (EVT) in road safety analysis, estimating the risk of being involved in an entering–circulating collision in single-lane roundabouts. Detailed trajectory data of the vehicles were derived from a driving simulator experiment, and the time-to-collision (TTC) was used as a surrogate measure of safety. Three EVT approaches were applied, tested and compared: (1) the Generalized Extreme Value distribution used in the block maxima (BM) approach, (2) the Generalized Pareto Distribution used in the peak-over-threshold approach (POT), with negated-TTC (nTTC), and (3) shifted-reciprocal-TTC (srTTC). Case-study results analysis showed that BM and POT with shifted-reciprocal-TTC confidence intervals included the number of observed crashes, while POT with negated-TTC did not include it. According to these findings, both BM and POT-with-shifted-reciprocal-TTC appear promising and deserve further attention in order to develop effective ready-to-practice crash prediction models, useful in intersection design and operational analysis.

Fleet management represents a relevant activity at tactical and operational levels to be faced by private companies and public agencies devoted to passengers and freight transportation. Mathematical models and computation techniques have been developed for optimising and simulating the operation of transport fleets in order to serve the customers demand with the objective of cost efficiency. Many fleet management problems correspond to combinatorial optimization problems, such as vehicle routing and scheduling, that are notoriously difficult to solve, even in a static context. Moreover, dynamic fleet management concern real-time requirements of the provided service and the control of unforeseen events that could affect the performances of the transport operations. The aim of this paper is to identify the most relevant problems in fleet management relatively to different transport modes, each one with specific characteristics, and to present an overview of recent contributions both in the development of mathematical models and in the design of computation algorithms