The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determinants. This study applies an explainable-machine-learning-based analytical framework to identify the key factors that shape ridesourcing demand and to explore their nonlinear associations across various spatial contexts (airport, downtown, and neighborhood). We use the ridesourcing-trip data in Chicago for empirical analysis. The results reveal that the importance of built environment varies across spatial contexts, and it collectively contributes the largest importance in predicting ridesourcing demand for airport trips. Additionally, the nonlinear effects of built environment on ridesourcing demand show strong spatial variations. Ridesourcing demand is usually most responsive to the built environment changes for downtown trips, followed by neighborhood trips and airport trips. These findings offer transportation professionals nuanced insights for managing ridesourcing services.
翻译:近年来,乘车客运服务日益重要,这表明有必要审查乘车客运需求的关键决定因素,然而,对于载车客运需求决定因素的非线性效应和空间差异性知之甚少,本研究报告采用了一个可解释的机械学习分析框架,以确定影响乘车客运需求的关键因素,并探索不同空间环境(机场、市中心及周边地区)的非线性协会。我们使用芝加哥的乘车客运数据进行实证分析。结果显示,建筑环境的重要性因空间背景不同而异,在预测乘车客运需求方面,建筑环境的重要性最大。此外,建筑环境对乘车客运需求的非线性影响也显示出巨大的空间差异。 乘车客运需求通常最能应对市区旅行的建筑环境变化,其次是周边旅行和机场旅行。这些结论为管理载车服务提供了运输专业人员的精细洞洞洞察力。