# Predicting Demand for Air Taxi Urban Aviation Services using Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2103.14604 · HTML: https://mlsanity.com/paper/2103.14604/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 83 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Accelerated Gradient Boosting](https://mlsanity.com/paper/1803.02042/) (2018)
- [Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities](https://mlsanity.com/paper/2103.01768/) (2021)
- [Recommendations for Emerging Air Taxi Network Operations based on Online Review Analysis of Helicopter Services](https://mlsanity.com/paper/2006.10898/) (2020)

## Built on by (most cited first, 3 of 3)

- [Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks](https://mlsanity.com/paper/2502.01680/) (2025)
- [STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction](https://mlsanity.com/paper/2306.14476/) (2023)
- [Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2601.21316/) (2026)

## Abstract (arXiv)

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.