Capacity and demand forecasting in Urban Air Mobility - A simulation of air transportation in Hamburg

Abstract

This paper presents an innovative approach to urban air transportation planning through the development of a "digital twin" representation encompassing airspace and infrastructure elements, including route structures, existing airports, and future vertiports. The primary objective is to formulate a model that mirrors the dynamics of contemporary and prospective urban air mobility (UAM), driven by the focus on sustainability and electrification in the transportation sector. Given the multifaceted advancements in both personal aerial vehicles (PAVs) and unmanned aerial vehicles (UAVs), along with the emergence of cutting-edge electric aircraft, a multitude of scenarios become feasible. The air transport system we focus on, is the urban area of Hamburg, Germany, and its two inner-city airports and a prototype vertiport. Anticipating a growth in demand for air transport services for the nearly 1.9 million current residents of Hamburg over the next two decades, we assume various feasible scenarios, which in turn would result in an increasing energy demand. Our SIMMOD simulation model utilizes flight schedules for each airport in Hamburg, to which flights are incrementally added until practical capacity is reached at 80% above baseline levels. Our findings reveal that, as demand increases at Hamburg Airport and average operational delay approaches the level-of-service (LoS) threshold of 4 minutes per flight, the necessity for expanded air traffic and ground infrastructure becomes evident. Electric grids should be dimensioned according to these demands. Our results suggest that the capacity of the Hamburg airspace is currently limited by the main airport, and that the Airbus production facility in Hamburg-Finkenwerder can substantially augment its capacity by constructing a parallel taxiway alongside its runway. Moreover, vertiport capacity primarily is limited by factors such as UAV parking and charging positions, drone speeds, turnaround times, available airspace, degree of autonomy and the length of service routes.