Bike sharing is no longer a novelty in transportation and has now become a mobility solution in its own right. This study investigated the potential scope of application of e-bike sharing solutions for a niche sector such as cruise tourism, the importance of which is growing, with the aim of improving sustainability and reducing pollution levels in cruise ports. A revealed preference survey was administered to cruise tourists, who chose a pilot e-bike service once they had disembarked from the ship to visit the nearby city center, to investigate the main variables affecting satisfaction with the service under investigation. An ordered probit model was specified and calibrated to identify the relationship among the variables influencing e-bike sharing usage by cruise tourists and their satisfaction. Subsequently, the marginal effect of each significant factor was evaluated to quantify its actual impact on the related e-bike sharing satisfaction level. The results obtained are consistent with the literature, but interesting interpretations are provided in terms of the relative importance of significant variables.

Methodologies and approaches for assessing the vulnerability of a public transport network are generally based on quantifying the average delay generated for passengers by some type of disruption. In this work, a novel methodology is proposed, which combines the traditional approach, based on the quantitative evaluation of averaged disruption effects, with the analysis of the asymmetry of effects among users, by means of Lorenz curves and Gini index. This allows evaluating whether the negative consequences of disruptions are equally spread among passengers or if differences exist. The results obtained show the potential of the proposed method to provide better knowledge about the effects of a disruption on a public transport network. Particularly, it emerged that disrupted scenarios that appear similar in terms of average impacts are actually very different in terms of the asymmetry of effects among users.

The decarbonization and automation of port operations are emerging as key strategies to enhance the sustainability and efficiency of maritime logistics. This study proposes a simulation-based framework to assess the operational and environmental impacts of transitioning from traditional Internal Combustion Engine (ICE) tractors to Battery Electric Tractors (BET) and Automated Electric Guided Tractors (e-AGT) in Roll-on/Roll-off (Ro–Ro) port terminal operations. The proposed framework is applied to simulate a full vessel turnaround at the Ro–Ro terminal of the Port of Ravenna (Italy). A set of Key Performance Indicators (KPIs) is defined to evaluate turnaround time, vehicle productivity, energy consumption and CO₂ emissions across three scenarios. The results indicate that both BET and e-AGT configurations significantly reduce emissions compared to ICE, with reductions up to 40%. However, the e-AGT scenario reveals operational drawbacks, including increased unloading time and reduced fleet availability due to charging constraints and routing limitations. These findings highlight the environmental potential of automation and electrification but also emphasize the need for integrated planning of fleet size, charging infrastructure and circulation specifications. The proposed framework provides a replicable decision-support tool for port authorities and logistics operators to evaluate alternative handling technologies under realistic conditions.

Connected and autonomous vehicles (CAVs) are expected to play a transformative role in highway traffic, particularly through platooning strategies. Specifically, they are expected to enhance operational conditions at bottlenecks by reducing the propagation of congestion and the emergence of safety-critical conflicts. In these contexts, CAV platoons might increase flow capacity by operating with shorter headways, while they could enhance safety by adopting more coordinated maneuvers. While existing research has shown the potential of CAVs to enhance traffic performance, significant gaps remain regarding how platoons behave under bottleneck conditions, especially in mixed traffic with both CAVs and human-driven vehicles (HDVs). This chapter explores the impacts of CAV platoons on highway traffic performance under mixed traffic conditions, focusing on their role at bottlenecks. Two cases are considered: (i) a recurrent bottleneck in a highway merging area, where the interaction between entering and mainline flows typically generates congestion, and (ii) a non-recurrent bottleneck produced by a vehicle breakdown, causing temporary capacity reduction. Using microsimulation-based analyses, this study investigates how platoon size and CAV market penetration rate (MPR) influence traffic capacity, travel times, and environmental impacts. Results show that CAV platoons may reduce congestion in recurrent situations and improve resilience under non-recurrent disruptions: specifically, in highway merging areas, platooning increases flow capacity and reduces travel times, while in the case of vehicle breakdowns, platoons help mitigate capacity drops and limit delays, particularly at higher MPRs and moderate platoon sizes. These findings contribute to a deeper understanding of the opportunities and challenges of CAV platoon deployment.