Predicting demand for air taxi urban aviation services using machine learning algorithms |
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| Institution: | 1. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Ecole Polytechnique Fédérale de Lausanne Engineering, CH-1015 Lausanne, Switzerland;2. Georgia Institute of Technology, School of Civil and Environmental Engineering, 790 Atlantic Drive, Atlanta, GA 30332-0355, United States;3. Georgia Institute of Technology, School of Aerospace Engineering, Daniel Guggenheim School of Aerospace Engineering, Atlanta, GA 30332-0355, United States;4. Georgia Institute of Technology, Daniel Guggenheim School of Aerospace Engineering, Atlanta, GA 30332-0355, United States;5. Transport and Mobility Laboratory, School of Architecture, Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland;1. National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China;2. National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191 Beijing, China;3. Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, 100083 Beijing, China;4. Institut für Luft- und Raumfahrtsysteme, RWTH Aachen, 52062 Aachen, Germany |
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| 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. |
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| Keywords: | Air taxi Demand prediction Machine learning algorithms Ride- and weather-related factors Urban air mobility (UAM) |
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