Kamal Acharya

Postdoctoral Research Associate at Baylor University

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Conference Paper

Regional Air Mobility Flight Demand Modeling in Tennessee State

By Kamal Acharya Mehul Lad Houbing Herbert Song Liang Sun

An AIAA SCITECH 2025 paper on Tennessee Regional Air Mobility demand modeling using MSA-level trip demand, cost-time-risk regression, generalized travel cost, and airport network scenarios.

2025 AIAA SCITECH 2025 Forum DOI: 10.2514/6.2025-2783

Regional Air Mobility Demand Modeling Advanced Air Mobility

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Abstract

Advanced Air Mobility (AAM), encompassing Urban Air Mobility (UAM) and Regional Air Mobility (RAM), offers innovative solutions to mitigate the issues related to ground transportation like traffic congestion, environmental pollution etc. RAM addresses transportation inefficiencies over medium-distance trips (50-500 miles), which are often underserved by both traditional air and ground transportation systems. This study focuses on RAM in Tennessee, addressing the complexities of demand modeling as a critical aspect of effective RAM implementation. Leveraging datasets from the Bureau of Transportation Statistics (BTS), Internal Revenue Service (IRS), Federal Aviation Administration (FAA), and other sources, we assess trip data across Tennessee’s Metropolitan Statistical Areas (MSAs) to develop a predictive framework for RAM demand. Through cost, time, and risk regression, we calculate a Generalized Travel Cost (GTC) that allows for comparative analysis between ground transportation and RAM, identifying factors that influence mode choice. When focusing on only five major airports (BNA, CHA, MEM, TRI, and TYS) as RAM hubs, the results reveal a mixed demand pattern due to varying travel distances to these central locations, which increases back-and-forth travel for some routes. However, by expanding the RAM network to include more regional airports, the GTC for RAM aligns more closely with traditional air travel, providing a smoother and more competitive option against ground transportation, particularly for trips exceeding 300 miles. The analysis shows that RAM demand is likely to be selected when air transportation accounts for more than 80% of the total GTC, air travel time is more than 1 hour and when the ground GTC exceeds 300 for specific origin-destination pairs.

In brief

What This Work Does

The paper estimates when regional air mobility flights could be a better option than driving for trips between Tennessee metropolitan areas, especially for longer trips where driving cost, time, and risk become high.

Research impact

Why It Matters

This work gives planners a quantitative basis for deciding where RAM service could improve regional connectivity in Tennessee, and shows why airport network design matters as much as aircraft performance for medium-distance markets.

Paper at a glance

Research Scope

4Research stages
5Methods or application areas
6Future research directions

Tennessee RAM Demand Modeling Framework

1

Generate Trip Demand

Uses NextGen NHTS passenger OD data at the MSA level and filters trips to the RAM-relevant 50-500 mile range.

2

Model Cost, Time, and Risk

Builds regression models for monetary cost, travel time, and fatality-risk cost using BTS, FAA, IRS, Google Maps, BLS, and USDoT datasets.

3

Compute GTC

Combines cost, wage-weighted travel time, and risk into Generalized Travel Cost for ground, airline, and RAM alternatives.

4

Predict RAM Selection

Uses a probabilistic mode-choice formulation to estimate when travelers are likely to switch from ground transportation to RAM.

Key Contributions

  • Develops an MSA-level Regional Air Mobility demand modeling framework for Tennessee.
  • Uses cost, time, and risk regression to compare ground transportation, airlines, and RAM.
  • Integrates generalized travel cost with a probabilistic mode-choice model for RAM selection.
  • Compares a five-major-airport RAM hub network against an expanded regional-airport network.
  • Identifies RAM-favorable conditions based on trip distance, air-share of GTC, air travel time, and ground GTC.

Methods and Network Scenarios

MSA-Level Demand

Represents travel between Tennessee MSAs using population centroids for origin and destination locations.

Five-Hub Case

Tests RAM service using BNA, CHA, MEM, TRI, and TYS as major airport hubs, revealing inefficiencies for some OD pairs due to added access travel.

Expanded-Airport Case

Adds regional airports nearest to each MSA, reducing backtracking and making RAM GTC align more closely with traditional air travel.

Risk-Aware GTC

Includes fatality-risk cost using value of statistical life assumptions, alongside direct cost and wage-weighted travel time.

Selection Thresholds

Finds RAM is more likely for trips exceeding about 300 miles, air travel times above one hour, and OD pairs where ground GTC exceeds 300.

Research Gaps

  1. MSA-level RAM demand
  2. Risk-aware mode choice
  3. Regional airport access
  4. Seasonal demand variation
  5. Underutilized MSAs
  6. Balanced infrastructure investment

Publication Details

Type
Conference Paper
Venue
AIAA SCITECH 2025 Forum
Year
2025
Pages
1-21

Research Topics

Regional Air Mobility Demand Modeling Advanced Air Mobility Generalized Travel Cost Risk Modeling

Citation

@inproceedings{acharya2025regional,
  author={Acharya, Kamal and Lad, Meet and Song, Houbing and Sun, Liang},
  title={Regional Air Mobility Flight Demand Modeling in Tennessee State},
  booktitle={AIAA SCITECH 2025 Forum},
  year={2025},
  pages={1--21},
  doi={10.2514/6.2025-2783}
}