Electric vehicles (EVs) penetration growth is essential to reduce transportation-related local pollutants. Most countries are witnessing a rapid development of the necessary charging infrastructure and a consequent increase in EV energy demand. In this context, power demand forecasting is an essential tool for planning and integrating EV charging as much as possible with the electric grid, renewable sources, storage systems, and their management systems. However, this forecasting is still challenging due to several reasons: the still not statistically significant number of circulating EVs, the different users’ behavior based on the car parking scenario, the strong heterogeneity of both charging infrastructure and EV population, and the uncertainty about the initial state of charge (SOC) distribution at the beginning of the charge. This paper aims to provide a forecasting method that considers all the main factors that may affect each charging event. The users’ behavior in different urban scenarios is predicted through their statistical pattern. A similar approach is used to forecast the EV’s initial SOC. A machine learning approach is adopted to develop a battery-charging behavioral model that takes into account the different EV model charging profiles. The final algorithm combines the different approaches providing a forecasting of the power absorbed by each single charging session and the total power absorbed by charging hubs. The algorithm is applied to different parking scenarios and the results highlight the strong difference in power demand among the different analyzed cases.

The necessity of pushing the road mobility towards more sustainable solutions has become of undeniable importance in last years. For this reason, both research and industry are constantly investigating new technologies able to make the usage of battery electric vehicles(BEV) as accessible and usable as traditional internal combustion engine vehicles (ICEV). One of the most limiting issues concerns the short range of electric vehicles, which complicates their use for long distances, such as for highway travels. A promising solution seems to be the “charge-while-driving” approach, by exploiting the inductive dynamic wireless power transfer (DWPT) technology. Nevertheless, such systems show different issues, first of all, high investment and maintenance costs. Furthermore, it is not clear how extensive a potential dynamic wireless charging infrastructure needs to be to make a real advantage for electric vehicle drivers. As a consequence, the aim of this paper is to introduce a new methodology to estimate the number and length of wireless charging sections necessary to allow the maximum number of electric vehicles to travel a specific highway without the need to stop for a recharge at a service area. Specifically, the methodology is based on a algorithm that, starting by real traffic data, simulates vehicle flows and defines the basic layout of the wireless charging infrastructure. This simulator can provide a decision support tool for highway road operators.

In the context of pavement management, conventional data collection methods for the evaluation of pavement functional condition are limited by relatively slow acquisition speeds, that prevent fast-lane motorway surveying at 120–130 km/h, and by survey frequency, which on vast networks typically occurs twice a year. Given these limitations, continuous pavement condition monitoring from moving vehicles offers an attractive solution to move towards real-time digital road assessment. In particular, such a result is achieved by making use of “intelligent” or “smart” tires, which by means of appropriate arrays of sensors can capture contact patch information, thereby providing quantitative information related to pavement roughness and macrotexture. In this study, smart tire data functional condition indicators, Dynamic Index (DI) and Pr index, were collected over several segments of a motorway network, with a total length of 405 km. Correlations were investigated between such parameters and the results of measurements coming from a traditional pavement monitoring technique, expressed in terms of international roughness index (IRI) and mean profile depth (MPD). Furthermore, the ability of smart tire indicators to identify time-dependent trends and to rank different motorway segments was assessed. Obtained results, which were generated by adopting different data processing and homogenization techniques, showed that DI displays a moderate correlation with IRI, while Pr exhibits a strong correlation with MPD. Pavement-age analysis highlighted the existence of meaningful trends for both dense-graded and open-graded asphalt-wearing courses. Motorway rankings based on average DI and Pr values were found to be in agreement with those obtained from average IRI and MPD values, thereby confirming the potential of smart tire technology as a complementary network-level monitoring tool for pavement asset management systems.

Effective traffic incident management is essential for ensuring safety, minimizing congestion, and reducing response times in emergency situations. Traditional highway incident management relies heavily on radio room operators, who must make rapid, informed decisions in high-stakes environments. This paper proposes an innovative solution to support and enhance these decisions by integrating Large Language Models (LLMs) into a decision-support system for traffic incident management. We introduce two approaches: (1) an LLM + Optimization hybrid that leverages both the flexibility of natural language interaction and the robustness of optimization techniques, and (2) a Full LLM approach that autonomously generates decisions using only LLM capabilities. We tested our solutions using historical event data from Autostrade per l'Italia. Experimental results indicate that while both approaches show promise, the LLM + Optimization solution demonstrates superior reliability, making it particularly suited to critical applications where consistency and accuracy are paramount. This research highlights the potential for LLMs to transform highway incident management by enabling accessible, data-driven decision-making support.