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.

这项研究的重点是使用机器学习算法（MLA）预测纽约市各个地理区域在一天中的不同时段对空中出租车城市空中交通（UAM）服务的需求。几个与乘车相关的因素（例如，一年中的月份，一周中的一天和一天中的时间）和与天气有关的变量（例如，温度，天气条件和能见度）被用作四个流行的MLA的预测指标，即后勤回归，人工神经网络，随机森林和梯度增强。实验结果表明，梯度增强可以始终如一地提供更高的预测性能。特定的位置，特定的时间段和工作日始终是重要的预测指标。