: Modular Autonomous Customised Bus Systems (MACB) promise to significantly enhance public transport attractiveness and accessibility, leading to improved quality of life and reduced emissions. Compared to traditional bus systems, MACB provides a problem that poses a new set of challenges, ranging from the allocation of vehicles to the optimisation of routes and recharges. The MACB problem is attracting increasing interest within the transport research community, but it presents characteristics and dimensions that lend themselves well to approaches based on planning and combinatorial search. In this paper, with the aim of bridging the gap between different research communities, we provide a crisp definition of the MACB problem and present a planning-based approach to solve a specific variant of the problem, together with a set of benchmarks to foster research on this topic.

This article presents a comprehensive study on the simulation of cyber-attacks and potential countermeasures within Urban Traffic Control (UTC) systems. The growing complexity of UTC systems, aimed at improving traffic efficiency, safety, and exploiting connectivity and autonomous capabilities, is leading to increased attack surfaces and vulnerabilities. The potential to exploit these vulnerabilities to adversely affect the system and its users makes them a desirable target, hence the need to increase understanding of the impact coming from security incidents. There is a large volume of research investigating focused attacks and their mitigation in UTC systems; however, there is an absence of understanding of this body of knowledge, identifying challenges and unaddressed areas. To address this gap, this study analyses simulations of attacks against UTC components and connected vehicles, incorporating current mitigation strategies published in recent literature. It discusses the implications of these vulnerabilities for public safety and traffic management, proposing future research directions to address the identified challenges. Six prevalent challenges are identified in the field, emphasising the importance of up-to-date simulations for reliable integration into real-time systems. The findings aim to strengthen UTC systems against cyber threats, contributing to the overarching goal of safeguarding lives and optimising urban traffic flow.

There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches. In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.

The emerging customized bus system based on modular autonomous electric vehicles (MAEVs) shows tremendous potential to improve the mobility, accessibility and environmental friendliness of a public transport system. However, the existing studies in this area almost focus on human-driven vehicles which face some striking limitations (e.g., restricted crew scheduling and fixed vehicle capacity) and can weaken the overall benefits. This paper proposes a two-phase optimization procedure to fully unleash the potential of MAEVs by leveraging the strengths of MAEVs, including automatic allocation and charging of modules. In the first phase, a mixed integer programming model is established in the space-time-state framework to jointly optimize the MAEV routing and charging, passenger-to-vehicle assignment and vehicle capacity management for reserved passengers. A Lagrangian relaxation algorithm is developed to solve the model efficiently. In the second phase, three dispatching strategies are designed and optimized by a dynamic dispatching procedure to properly adapt the operation of MAEVs to emerging travel demands. A case study conducted on a major urban area of Beijing, China, demonstrates the high efficiency of the MAEV adoption in terms of resource utilization and environmental friendliness across a range of travel demand distributions, vehicle supply and module capacity scenarios.

Urban customized bus companies are increasingly motivated by design efforts that entail more efficient route scenarios to incorporate adaptation to temporal and spatial heterogeneity in travel demand. However, such motivations are usually hindered by ubiquitous arrival unpunctuality resulting from traffic congestion. To resolve this problem, we suggest a time-dependent bus route planning methodology that explicitly considers path flexibility between nodes to be visited. First, we establish a mixed-integer programming model to formulate the problem, where decision-making considerations in bus route planning, path choice between nodes, and passenger assignment are concurrently integrated. Then, we develop a hybrid metaheuristic (combining tabu search and variable neighborhood search) to solve the model, in which satisfactory performance is observed from the numerical test in a small-sized example. Finally, the problem and methodology are addressed in a city-scale instance, where the effects of time-window features and traffic congestion, as well as the benefits from path flexibility inclusion in terms of cost, travel time, and distance are investigated.