In this study, a demand forecasting model for Advanced Air Mobility(AAM) was developed by analyzing and structuring the causal relationships among various variables influencing AAM demand based on the system dynamics methodology. To predict AAM demand...
In this study, a demand forecasting model for Advanced Air Mobility(AAM) was developed by analyzing and structuring the causal relationships among various variables influencing AAM demand based on the system dynamics methodology. To predict AAM demand, the variables influencing AAM demand were categorized into external factors, supply factors, diffusion factors, and imitation factors. A Causal Loop Diagram(CLD) was created to identify the causal relationships among the variables, and a Stock-Flow Diagram(SFD) was constructed to quantitatively represent the relationships among the variables using the CLD. Through the developed SFD, it was predicted that AAM demand would increase to 535,772 people by 2050. Additionally, the number of routes, number of vertiports, imitation coefficient, and diffusion parameter value were found to have a significant impact on AAM demand, with values exceeding 0.7. Scenario analysis was conducted to observe the changes in AAM demand due to variations in modal shift rate, mid-to-long-distance travel ratio, and infrastructure investment. The scenario analysis results indicated that if the mid-to-long-distance travel ratio increases, AAM demand could increase by up to 30.00%. This study developed an AAM demand forecasting model that considers various factors affecting AAM demand and can respond to future environmental changes. The demand forecasting model proposed in this study can be used as a basis for policy decision-making by decision-makers and can be applied to demand forecasting for specific regions or routes by adding or modifying internal model variables.