This paper proposes an optimization algorithm to support decision makers in the dimensioning of a new type of public transport service. In particular, vehicles capable of traveling on both rail and road are exploited to revitalize capillary railway lines, characterized by low demand but very high societal impact. To support decisions on infrastructure modifications and fleet size, we propose an algorithm based on the solution of a mixed-integer linear programming model. In particular, the infrastructure modifications considered consist in the construction of road-type sidings on the existing railway line, to allow vehicle crossing. We assess the performance of our algorithm on a real case study: the line joining the cities of Limoux and Quillan, in France. The results highlight how investing in siding construction can indeed improve the regularity of the timetable that can be planned. However, depending on the available fleet and the desired frequency, they may not be strictly necessary.
Achieving sustainability objectives necessitates a transformation in passenger mobility behaviour. This paper introduces a Generative AI framework developed by the MUV platform to address the enduring attitude-behaviour gap in sustainable urban mobility. Anchored in Self-Determination Theory, the system utilises behavioural, contextual, and qualitative data to create personalised competitions that enhance intrinsic motivation. The framework features a threelayer architecture and a virtual mobility trainer to support game dynamics, while federated learning and bias audits provide ethical safeguards. The framework is set to be piloted at ISPRA, with a mixed-methods evaluation planned to assess its impact on behaviour and inclusivity. This study presents a replicable model for responsible AI-driven behavioural change in alignment with the European Green Deal.
Truck-drone combined logistics has received increasing attention, and the multi-drops flying sidekick traveling salesman problem (mFSTSP) is one of the classic models. For the mFSTSP, we establishes an integer programming model based on drone range constraint, and designs a sub-circuit construction algorithm according to the modeling idea. Based on the range of the drone, the obtained TSP route is segmented and the optimal sub-circuit are constructed in sequence. Further optimization through dynamic adjustment augmented service mechanism, and three discussions are conducted on the starting position of the segmentation. Considering different operational scenarios, a set of numerical experiments are conducted. Compared with several advanced heuristic algorithms, the proposed sub-circuit construction algorithm shows remarkable advantages in solving large-scale problems, with a maximum solution scale exceeding 700 nodes.
Evaluating autonomous vehicle performance in complex traffic scenarios requires both calibrated sensors and realistic traffic conditions. However, existing traffic datasets focus on vehicle trajectories but lack comprehensive sensor data, while perception datasets provide sensor data but offer limited traffic diversity. To bridge this gap, we introduce an open-source toolset that generates perception datasets in CARLA by embedding realworld vehicle trajectories within a simulated sensor environment. This approach produces sensor data that accurately mirrors real-world traffic dynamics, providing a valuable resource for testing AI-driven autonomous systems in complex scenarios such as roundabouts, where sensor-based perception is essential for safe AV navigation.
The rapid technological evolution in the automotive sector demands increasingly advanced testing platforms capable of integrating high-fidelity virtual simulations with real hardware components. In this work, we present a modular platform capable of operating in Software-in-the-Loop (SIL), Vehicle-in-the-Loop (VIL), and Passenger-in-the-Loop (PIL) configurations. The proposed solution combines photorealistic rendering using Unreal Engine 5, real-time processing via SpeedGoat and MATLAB/Simulink, advanced tracking systems (Vicon, IMU, GNSS RTK), and a Dynamic Traffic Behaviour Manager for autonomous management of heterogeneous traffic scenarios. The platform is designed to simulate complex urban environments while ensuring low latencies (average below 24 ms) and minimizing motion sickness risk—key aspects for validating autonomous and assisted driving systems. Preliminary results highlight the correct integration of the various modules, demonstrating the system's ability to manage real-time interactions between vehicles, pedestrians, and virtual scenarios. These developments provide a valuable tool for optimizing and validating control algorithms, paving the way for safer, more reliable, and immersive testing.
