Conference Paper
Demand Modeling for Advanced Air Mobility
An IEEE BigData 2024 paper on Tennessee census-tract AAM demand modeling using employment trips, cost-time-risk modeling, and generalized cost of trip.
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.
In brief
What This Work Does
The paper estimates which Tennessee work-related trips could shift to AAM by comparing ground and air options through cost, time, and risk.
Research impact
Why It Matters
AAM planning depends on realistic demand estimates that account for employment flows, trip distance, airport access, cost, time, and risk. This paper provides a tract-level framework for evaluating where AAM may be viable in Tennessee.
Paper at a glance
Research Scope
Census-Tract AAM Demand Modeling Workflow
Collect Employment Trips
Uses Tennessee census-tract employment-based trip data to identify candidate AAM demand.
Model Cost, Time, and Risk
Combines BTS, IRS, FAA, LODES, and related datasets to estimate modal cost, travel time, and risk.
Compute GCT
Calculates Generalized Cost of Trip for ground transportation and AAM alternatives.
Estimate AAM Selection
Identifies trips likely to shift to AAM based on air share of GCT and trip-distance thresholds.
Key Contributions
- Models AAM demand at the Tennessee census-tract level using employment-based trip data.
- Develops cost, time, and risk models for ground transportation and AAM.
- Computes Generalized Cost of Trip to compare mode viability.
- Considers all 70 Tennessee airports as AAM hubs.
- Identifies thresholds for AAM-favorable trips based on air share of GCT and trip distance.
Demand Modeling Components
Employment-Based OD Demand
Uses census-tract commuting and employment flows as proxy demand for passenger AAM trips.
Airport Hub Network
Considers all 70 Tennessee airports as possible AAM hubs for access and egress modeling.
Cost-Time-Risk GCT
Combines monetary cost, travel duration, and risk into a comparable mode-choice measure.
Threshold Analysis
Finds AAM trips are more likely when air transportation exceeds 70% of GCT and distance exceeds 250 miles.
Research Gaps
- Employment-flow proxies
- Electric aircraft costs
- Airport access modeling
- Risk-aware demand
- Census-tract scalability
Publication Details
- Type
- Conference Paper
- Venue
- IEEE BigData 2024
- Year
- 2024
- Pages
- 2855-2863
Research Topics
Links and Access
Citation
@inproceedings{acharya2024demand,
author={Acharya, Kamal and Lad, Meet and Sun, Liang and Song, Houbing},
title={Demand Modeling for Advanced Air Mobility},
booktitle={IEEE BigData 2024},
year={2024},
pages={2855--2863},
doi={10.1109/BigData62323.2024.10825121}
}