Multi-Objective Model-Based Optimization of Pilot Decision Making for Urban Air Mobility

Abstract

Abstract The emergence of an Urban Air Mobility (UAM) market utilizing fleets of vertical takeoff and land (VTOL) aircraft has the potential to shorten commutes, alleviate traffic congestion, and transform the way people interact with cities. Both industry and academic effort has been aimed towards predicting and simulating fleet activity for the purpose of informing vehicle design decisions and infrastructure planning activities. However, little effort has been targeted at analyzing aircraft fleet performance and sensitivity to pilot decision making, infrastructure availability, and changing vehicle characteristics. In this work, we utilize an existing proprietary, industry developed, discrete-event simulation tool that calculates air vehicle fleet performance with user specified vehicle and demand parameters. Building on this simulation, we then apply an optimization and sensitivity analysis, identifying the relative importance of different vehicle parameters, network attributes, decision making policies, and demand characteristics as they ultimately relate to objective functions that aim to maximize vehicle utilization and minimize the number of deadhead trips. The result of this analysis will inform critical requirements of the UAM market and highlight the importance of effective vehicle scheduling in a UAM scenario.