Forecasting Demand for Urban Air Mobility Based on the Random Coefficient Logit Model: A Case Study of Provincial Capital Cities in China

Abstract

With the technical breakthrough of electric vertical take-off and landing (eVTOL) aircraft, urban air mobility (UAM) has gained significant attention. Accurate demand forecasts for UAM in China are crucial. The random coefficient logit model (referred to as the BLP model) is applied to estimate the demand of heterogeneous consumers. We employ a predictive model that integrates multiple influencing factors to forecast UAM demand. The model reveals the impact of traffic mode characteristics (speed, cost, waiting time, and congestion) on heterogeneous passengers, who differ in gender, education background, value of travel time, and vehicle ownership. Using the nested logit (NL) model as a benchmark, we calculate the sharing rates of different traffic modes. The results indicate that the sharing rates derived from the BLP model are closer to reality, showing an elastic substitution relationship among different traffic modes. Furthermore, considering the characteristics of current eVTOL aircraft, we estimate the sharing rates of UAM in urban settings. By analyzing the cost of UAM, we offer suggestions concerning fares and improvements to the ride experience. Simulations show that when fares are reduced by 25%, the sharing rate increases 50%. Additionally, passenger accessibility can be enhanced by increasing the number of vertiports. The cruising speed of UAM should be limited, as the sharing rate decreases when speeds exceed 120 km/h, a matter that is related to passenger comfort and safety.