No abstract

No abstract

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.

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.