Demand Modeling for Advanced Air Mobility

TL;DR

This paper analyzes demand patterns for Advanced Air Mobility (AAM) using statistical and machine learning techniques on trip data, providing insights to improve demand forecasting and support urban mobility planning.

Contribution

It introduces a comprehensive demand modeling approach for AAM, integrating multiple datasets and assessing cost, time, and risk factors to enhance demand prediction accuracy.

Findings

Trips with over 70% GCT air transportation are more viable for AAM.

AAM demand increases for journeys longer than 250 miles.

The study offers strategic insights for policy and planning in urban mobility.

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 }