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.

翻译：这项研究的重点是利用机器学习算法预测纽约市不同地理区域每天不同时间对航空出租车城市空中流动服务的需求,若干与载运有关的因素(例如当月、当日的一天和当日的时间)和与天气有关的变数(例如温度、天气条件和能见度)被用来预测四种受欢迎的司法协助,即后勤回归、人工神经网络、随机森林和梯度增强。 实验结果显示梯度加速,以不断提供更高的预测性能。 特定地点、某些时段和工作日一直作为关键预测器出现。