Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing

TL;DR

This paper presents a quantum annealing-based routing and scheduling framework for urban air mobility fleets, improving airspace utilization and safety through advanced optimization techniques validated in a Singapore airspace simulator.

Contribution

It introduces a novel quantum annealing approach to optimize UAM fleet routing and scheduling, addressing high-density traffic management in urban airspace.

Findings

Enhanced airspace utilization demonstrated in simulations

Effective deconfliction of UAM routes achieved

Potential for scalable urban air traffic management

Abstract

The growing integration of urban air mobility (UAM) for urban transportation and delivery has accelerated due to increasing traffic congestion and its environmental and economic repercussions. Efficiently managing the anticipated high-density air traffic in cities is critical to ensure safe and effective operations. In this study, we propose a routing and scheduling framework to address the needs of a large fleet of UAM vehicles operating in urban areas. Using mathematical optimization techniques, we plan efficient and deconflicted routes for a fleet of vehicles. Formulating route planning as a maximum weighted independent set problem enables us to utilize various algorithms and specialized optimization hardware, such as quantum annealers, which has seen substantial progress in recent years. Our method is validated using a traffic management simulator tailored for the airspace in Singapore. Our approach enhances airspace utilization by distributing traffic throughout a region. This study broadens the potential applications of optimization techniques in UAM traffic management.