Analysis of Potential Demand of On-Demand Urban Air Mobility Via Agent-Based Simulation

This thesis analyzes the potential demand of Urban Air Mobility (UAM) by performing agent-based simulation. The comprehensive UAM model proposed by this thesis combines demand, supply, and their interactions at fine spatial and temporal levels. It has been implemented in the state-of-the-art mobility simulation platform, SimMobility, and includes the following considerations: (i) demand-centric vertiport placements and realistic vertiport capacity generation; (ii) explicit service operations that include rebalancing, charging and transition activities at vertiports; (iii) a behaviorally sound decision-making process capturing the switching behaviors. Simulations of at-launch, near-term and long-term scenarios, varying in capacity, accessibility, and pricing constraints, are performed for two real U.S. cities, along with the uncertainties. The results show that UAM presents a niche market, with only a penetration rate of 1.45% to 1.81% even in the long-term scenario for the two cities studied. Furthermore, the potential UAM users are primarily high-income and car-oriented, indicating equity issues. Work and drive-alone trips have the highest penetration rate, and short-range trips constitute the majority of the UAM potential demand. Lastly, capacity, accessibility, and pricing show significant impacts on demand, which are city-specific effects. This thesis contributes to the literature by analyzing the impacts of UAM on mobility pattern, specifically focusing on the potential market size and demand characteristics under various supply configurations, allowing policymakers and the industry to make informed decisions regarding UAM market diffusion.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Chen, Kexin

Advisors dc:contributor.advisor

• Ben-Akiva, Moshe E.
• Shamshiripour, Ali

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144557

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144557