SOCIETAL ACCEPTANCE OF ADVANCED AIR MOBILITY (AAM): ANALYSIS OF THE RELATIVE IMPORTANCE AND POTENTIAL USER

• Grant number: 23/15420-7
• Support Opportunities: Scholarships abroad - Research
• Start date: January 02, 2025
• End date: February 01, 2025
• Field of knowledge: Engineering - Transportation Engineering - Transportation Planning
• Principal Investigator: Giovanna Miceli Ronzani Borille
• Grantee: Giovanna Miceli Ronzani Borille
• Host Investigator: Michael Schultz
• Host Institution: Divisão de Engenharia Civil (IEI). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
• Institution abroad: Universität Der Bundeswehr München, Germany

Abstract

The rapid growth of urbanization has led to various challenges, including congestion, environmental issues, and inadequate infrastructure. To address these challenges, Advanced Air Mobility (AAM) has emerged as a promising solution, aiming to provide safe, efficient, and secure transportation for passengers, cargo, and emergencies on-demand in urban, suburban, and rural areas. Extensive research has been conducted to identify potential AAM markets, understand barriers to implementation, and explore factors influencing adoption and usage. However, understanding the perception, acceptance, and adoption of AAM remains a significant challenge for its successful implementation. This study aims to investigate the decision-making process of potential customers when selecting their preferred mode of transportation in a scenario involving AAM and multiple other options. The goal is to measure the probability of selecting each transportation option. Furthermore, we seek to explore the hypothesis that the mean probability (attractivity score) of selecting AAM as the preferred mode of transport can be estimated using sociodemographic data and service characteristics. To achieve these objectives, we propose a framework that combines an Adaptive Choice-Based Conjoint (ACBC) survey, logistic regression, and machine learning techniques to predict the favorability of AAM. By analyzing micro-region samples (in Brazil and in Germany), utilizing the collected data, we aim to develop models that can accurately predict the attractiveness of AAM as a transportation option. By gaining insights into customers' decision-making processes and preferences, this study can contribute to the advancement and successful implementation of AAM in urban and regional transportation systems.