Predicting demand for air taxi urban aviation services using machine learning algorithms

Suchithra Rajendran, Sharan Srinivas and Trenton Grimshaw

Journal of Air Transport Management, 2021, vol. 92, issue C

Abstract: 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.

Keywords: Air taxi; Demand prediction; Machine learning algorithms; Ride- and weather-related factors; Urban air mobility (UAM) (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:92:y:2021:i:c:s0969699721000260

DOI: 10.1016/j.jairtraman.2021.102043

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