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

TL;DR

This study proposes a routing and scheduling framework to address the needs of a large fleet of UAM vehicles operating in urban areas, using mathematical optimization techniques to plan efficient and deconflicted routes for a fleet of vehicles.

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.

Figures (7)

• Figure 1: Routing Network in Singapore. The labeled nodes represent aerodromes, while the lines represent corridors. This airspace structure was generated using the OneSky UTM simulator.
• Figure 2: Dynamic scheduling and routing framework. The framework consists of two main components: route generation, and optimization.
• Figure 3: An example of a generated graph and its corresponding solution to the MWIS problem.
• Figure 4: Cumulative number of approved flights. The plots show the cumulative number of approved flights during the simulation. The triangle, circle, square, and down-pointing triangle markers represent the results of the shortest FIFO, quantum annealing, exact optimization, and greedy algorithm, respectively.
• Figure 5: Cumulative number of approved flights. The plots show the average number of aircraft in the airspace at any given time during the simulation. The triangle, circle, square, and down-pointing triangle markers represent the results of the shortest FIFO, quantum annealing, exact optimization, and greedy algorithm, respectively.