Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms
This work addresses demand forecasting for urban air mobility operators, but it is incremental as it applies existing methods to a new dataset.
The study tackled predicting demand for air taxi services in New York City by using machine learning algorithms with ride and weather factors, finding that gradient boosting consistently provided higher performance.
This research focuses on predicting the demand for air taxi urban air mobility (UAM) services during different times of the day in various geographic regions of New York City using machine learning algorithms (MLAs). Several ride-related factors (such as month of the year, day of the week and time of the day) and weather-related variables (such as temperature, weather conditions and visibility) are used as predictors for four popular MLAs, namely, logistic regression, artificial neural networks, random forests, and gradient boosting. Experimental results suggest gradient boosting to consistently provide higher prediction performance. Specific locations, certain time periods and weekdays consistently emerged as critical predictors.