Analysis of cost-efficient urban air mobility systems: Optimization of operational and configurational fleet decisions

European Journal of Operational Research(2023)

Cited 0|Views0
No score
Abstract
With the introduction of low-noise and low-emission electric vertical take-off and landing vehicles, passenger air transportation in urban areas is becoming increasingly important. Previous studies have designed vehicle concepts based on reference mission profiles, however, without considering strategic decisions about fleet operations, such as charging infrastructure, range and battery capacity. Comprehensive cost analyses for urban air mobility fleets have been largely neglected. In this paper, we develop an optimization model to analyze the cost-efficient operation of urban air mobility systems. Strategic decisions on vehicle concepts, battery capacity, and charging infrastructure are incorporated and evaluated using a total cost of ownership approach. We consider state-of-the-art modeling approaches, including vehicle-specific parameters, for accurate calculation of energy consumption. The optimization model is applied to the multi-agent transport simulation MATSim in a case study of the Ruhr (Germany) scenario to evaluate key parameter variations. The study results indicate the feasibility of an urban air mobility system in the study region with trip costs ranging from 27 US-$ to 46 US-$ per requested trip, depending on the scenario settings. Based on parameter variations, we find that a high cruising speed has a detrimental effect on the total cost. A medium charging power level is sufficient and energy costs account for only a moderate share. The latter is in contrast to previous research results, but can be attributed to the more detailed modeling approach of vehicle-specific parameters. We propose a cost incentive system as the ground handling time of the vehicles outweighs the recharging and flight time. The overall results provide a promising basis for model extensions.
More
Translated text
Key words
configurational fleet decisions,urban,optimization,cost-efficient
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined