# Demand Modeling for Advanced Air Mobility

arXiv: https://arxiv.org/abs/2412.06807 · HTML: https://mlsanity.com/paper/2412.06807/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks](https://mlsanity.com/paper/2502.01680/) (2025)

## Abstract (arXiv)

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