Modeling realistic human behaviour to understand people's mode choices in order to propose personalised mobility solutions remains challenging. This paper presents an architecture for modeling realistic human mobility behavior in complex multimodal transport systems, demonstrated through a case study in Toulouse, France. We apply Large Language Models (LLMs) within an agent-based simulation to capture decision-making in a real urban setting. The framework integrates the GAMA simulation platform with an LLM-based generative agent, along with General Transit Feed Specification (GTFS) data for public transport, and OpenTripPlanner for multimodal routing. GAMA platform models the interactive transport environment, providing visualization and dynamic agent interactions while eliminating the need to construct the simulation environment from scratch. This design enables a stronger focus on developing generative agents and evaluating their performance in transport decision-making processes. Over a simulated month, results show that agents not only make context-aware transport decisions but also form habits over time. We conclude that combining LLMs with agent-based simulation offers a promising direction for advancing intelligent transportation systems and personalised multimodal mobility solutions. We also discuss some limitations of this approach and outline future work on scaling to larger regions, integrating real-time data, and refining memory models.

Designing a new drug often means inventing molecules that have never existed before. Chemists represent molecules as graphs, where atoms are the "nodes" and chemical bonds the "edges," capturing their connections. This graph representation expands far beyond chemistry: a social network is a graph of people and friendships, the brain is a graph of neurons and synapses, and a transport system is a graph of stations and routes. From molecules to social networks, graphs are everywhere and naturally capture the relational structure of the world around us. Therefore, for many applications, being able to generate new realistic graphs is a central problem.

The rapid expansion of interconnected devices, autonomous systems, and AI applications has created severe fragmentation in adaptive transport systems, where diverse protocols and context sources remain isolated. This survey provides the first systematic investigation of the Model Context Protocol (MCP) as a unifying paradigm, highlighting its ability to bridge protocol-level adaptation with context-aware decision making. Analyzing established literature, we show that existing efforts have implicitly converged toward MCP-like architectures, signaling a natural evolution from fragmented solutions to standardized integration frameworks. We propose a five-category taxonomy covering adaptive mechanisms, context-aware frameworks, unification models, integration strategies, and MCP-enabled architectures. Our findings reveal three key insights: traditional transport protocols have reached the limits of isolated adaptation, MCP's client-server and JSON-RPC structure enables semantic interoperability, and AI-driven transport demands integration paradigms uniquely suited to MCP. Finally, we present a research roadmap positioning MCP as a foundation for next-generation adaptive, context-aware, and intelligent transport infrastructures.

This paper presents an autonomous aerial system specifically engineered for operation in challenging marine GNSS-denied environments, aimed at transporting small cargo from a target vessel. In these environments, characterized by weakly textured sea surfaces with few feature points, chaotic deck oscillations due to waves, and significant wind gusts, conventional navigation methods often prove inadequate. Leveraging the DJI M300 platform, our system is designed to autonomously navigate and transport cargo while overcoming these environmental challenges. In particular, this paper proposes an anchor-based localization method using ultrawideband (UWB) and QR codes facilities, which decouples the UAV's attitude from that of the moving landing platform, thus reducing control oscillations caused by platform movement. Additionally, a motor-driven attachment mechanism for cargo is designed, which enhances the UAV's field of view during descent and ensures a reliable attachment to the cargo upon landing. The system's reliability and effectiveness were progressively enhanced through multiple outdoor experimental iterations and were validated by the successful cargo transport during the 2024 Mohamed BinZayed International Robotics Challenge (MBZIRC2024) competition. Crucially, the system addresses uncertainties and interferences inherent in maritime transportation missions without prior knowledge of cargo locations on the deck and with strict limitations on intervention throughout the transportation.

The design or the optimization of transport systems is a difficult task. This is especially true in the case of the introduction of new transport modes in an existing system. The main reason is, that even small additions and changes result in the emergence of new travel patterns, likely resulting in an adaptation of the travel behavior of multiple other agents in the system. Here we consider the optimization of future Urban Air Mobility services under consideration of effects induced by the new mode to an existing system. We tackle this problem through a bi-level network design approach, in which the discrete decisions of the network design planner are optimized based on the evaluated dynamic demand of the user's mode choices. We solve the activity-based network design problem (AB-NDP) using a Genetic Algorithm on a multi-objective optimization problem while evaluating the dynamic demand with the large-scale Multi-Agent Transport Simulation (MATSim) framework. The proposed bi-level approach is compared against the results of a coverage approach using a static demand method. The bi-level study shows better results for expected UAM demand and total travel time savings across the transportation system. Due to its generic character, the demonstrated utilization of a bi-level method is applicable to other mobility service design questions and to other regions.

The era of graph theory began with Euler in the year 1735 to solve the well-known problem of the Königsberg Bridge. In the modern age, graph theory is an integral component of computer science, artificial engineering, machine learning, deep learning, data science, and social networks. A graph G(V, E) is a non-linear data structure, which consists of pair of sets (V, E) where V is the non-empty set of vertices (points or nodes). E is the set of edges (lines or branches) such that there is a mapping f: E V i.e., from the set E to the set of ordered or unordered pairs of elements of V. The number of called the order of the graphs and the number of edges is called the size of graph G (V, E).

The development of smart city transport systems, including self-driving cars, leads to an increase in the threat of hostile interference in the processes of vehicle control. This interference may disrupt the normal functioning of the transport system, and, if is performed covertly, the system can be negatively affected for a long period of time. This paper develops a simulation stochastic cellular automata model of traffic on a circular two-lane road based on the Sakai-Nishinari-Fukui-Schadschneider (S-NFS) rules. In the presented model, in addition to ordinary vehicles, there are covertly counteracting vehicles; their task is to reduce the quantity indicators (such as traffic flux) of the transport system using special rules of behavior. Three such rules are considered and compared: two lane-changing rules and one slow-down rule. It is shown that such counteracting vehicles can affect the traffic flow, mainly in the region of the maximum of the fundamental diagram, that is, at average values of the vehicle density. In free-flowing traffic or in a traffic jam, the influence of the counteracting vehicle is negligible regardless of its rules of behavior.

From the UAE's first driverless taxi to robo buses and trains, a fleet of 17 autonomous vehicles is now running on Abu Dhabi roads as the emirate's futuristic smart mobility project enters its second phase. Eight Txai self-driving cabs, six mini robo buses, and three autonomous rapid transits (ART) – an improved rapid transport system operating without rails -- are currently being operated on Yas and Saadiyat islands. There are also some 20 charging stations on these destinations. In its first phase, the ART trackless trams on wheels will provide services to Yas Island's main tourist attractions and commercial hubs. The ART service -- along with other autonomous rides -- cover theme parks like Ferrari World Abu Dhabi, Warner Bros World Abu Dhabi, Yas Waterworld, and other hotspots like Yas Mall, Yas Beach, Yas Plaza, and more on Yas Island. The route is 47.5 km long, and the service runs from 8am to 8pm throughout the week.

Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health. The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the United Nations was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation - such as walking and cycling - at a city-wide level, where dominating motorised modes are often of primary concern. This paper presents an open workflow for creating a multi-modal agent-based and activity-based transport simulation model, focusing on Greater Melbourne, and including the process of mode choice calibration for the four main travel modes of driving, public transport, cycling and walking. The synthetic population generated and used as an input for the simulation model represented Melbourne's population based on Census 2016, with daily activities and trips based on the Victoria's 2016-18 travel survey data. The road network used in the simulation model includes all public roads accessible via the included travel modes. We compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behaviour of travellers.