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.1

Accurately predicting flight demand is essential for optimizing air travel operations and resource allocation. In our research, we explore the relationship between temporal patterns and flight demand, leveraging hourly data rather than traditional meteorological factors. Through analysis, we discovered significant correlations between hour of the day and flight demand, prompting the creation of features such as peak hours and time segments (morning, afternoon, evening). By utilizing these temporal features, we develop predictive models employing various machine learning algorithms, including LSTM, linear regression, and gradient boosting models. We aim to identify the most effective approach for accurately forecasting flight demand, with implications extending to the optimization of Advanced Air Mobility (AAM) solutions, where understanding temporal patterns is crucial for efficient resource allocation and urban air transportation network design.