Routing and scheduling optimization for urban air mobility fleet management using quantum annealing

Area of Science:

• Operations Research
• Transportation Science
• Aerospace Engineering

Background:

• Increasing traffic congestion in urban areas necessitates advanced solutions for transportation and delivery.
• Urban Air Mobility (UAM) integration faces challenges in managing high-density, complex air traffic for safe operations.

Purpose of the Study:

• To develop and validate a routing and scheduling framework for a large fleet of UAM vehicles in urban environments.
• To address the critical need for efficient and deconflicted air traffic management in cities.

Main Methods:

• Formulation of the route planning problem as a maximum weighted independent set problem.
• Application of mathematical optimization techniques and exploration of specialized hardware like quantum annealers.
• Validation using a traffic management simulator for Singapore's airspace.

Main Results:

• The proposed framework generates efficient and deconflicted routes for UAM fleets.
• The method enhances overall airspace utilization by distributing traffic across the region.
• Demonstrated feasibility of using advanced optimization for UAM traffic management.

Conclusions:

• The developed routing and scheduling framework effectively manages urban air mobility traffic.
• Optimization techniques, including those suited for quantum computing, offer a promising path for future UAM traffic solutions.
• This research expands the application of optimization in the burgeoning field of UAM.