Demand Modeling for Advanced Air Mobility
Kamal Acharya, Mehul Lad, Liang Sun, Houbing Song
TL;DR
The paper tackles demand modeling for Advanced Air Mobility (AAM) as a scalable urban mobility option. It develops a four-step framework to generate trip demand and to evaluate ground versus AAM using region-specific data, computing the Generalized Cost of Trip (GCT) as a unifying metric: $GCT_m = -C_m - W T_m - R_m$. By integrating LODES/LODES OD data, IRS mileage rates, OSRM driving distances, ASPM block times, and DB1B airfares, the study identifies a threshold where AAM becomes preferable: trips longer than about 250 miles with air-based cost sharing exceeding 70% of the total GCT. A probabilistic mode-choice model based on utilities $U_G$ and $U_{AAM}$ complements the deterministic GCT comparison, yielding actionable insights for planning and policy. The results support targeted AAM deployment in regions with longer travel distances and higher air-cost relevance, and provide a foundation for incorporating electrified AAM cost dynamics into future analyses.
Abstract
In recent years, the rapid pace of urbanization has posed profound challenges globally, exacerbating environmental concerns and escalating traffic congestion in metropolitan areas. To mitigate these issues, Advanced Air Mobility (AAM) has emerged as a promising transportation alternative. However, the effective implementation of AAM requires robust demand modeling. This study delves into the demand dynamics of AAM by analyzing employment based trip data across Tennessee's census tracts, employing statistical techniques and machine learning models to enhance accuracy in demand forecasting. Drawing on datasets from the Bureau of Transportation Statistics (BTS), the Internal Revenue Service (IRS), the Federal Aviation Administration (FAA), and additional sources, we perform cost, time, and risk assessments to compute the Generalized Cost of Trip (GCT). Our findings indicate that trips are more likely to be viable for AAM if air transportation accounts for over 70\% of the GCT and the journey spans more than 250 miles. The study not only refines the understanding of AAM demand but also guides strategic planning and policy formulation for sustainable urban mobility solutions. The data and code can be accessed on GitHub.{https://github.com/lotussavy/IEEEBigData-2024.git }