  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.
