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Flow-based unit is better: exploring factors affecting mid-term OD demand of station-based one-way electric carsharing

机译:基于流量的单位更好:探索影响基于站的单次OD需求的因素单向电动汽车

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Station-based one-way carsharing (OWC) serves as a flexible method to enjoy the benefits of car travel, while also demonstrating the potential to mitigate environmental challenges and traffic congestion in cities. In the previous studies on OWC, location-based units such as a single station, station cluster, and land parcel were generally used as the basic analysis units for demand; however, these studies failed to consider the association among the operating areas. This led to results that were pertinent to a specific OWC system and cross-section of the development process. The objective of this study is to explore the significant factors related to flow-based demand (i.e., four-weekly bookings from the origin spatial unit to the destination spatial unit (OD bookings)). Four groups of explanatory variables are adopted: carsharing spatial unit attributes, built environment, transportation facilities, and OD trip attributes (such as public transportation and car travel distance between the OD). A combination model integrating machine learning and a generalized linear model is also developed to address the zero-inflation issue of the data. Moreover, an approach of Shapley additive explanations is implemented to determine the considerable effects of the factors. The results show that (1) the OD trip attributes play an important role in estimating the carsharing OD demand; (2) taxi and carsharing demands exhibit a non-significant partial overlap, and carsharing may compete with buses and supplement to the metro; and (3) the travel purpose in carsharing is diverse for any land use over a four-week period.
机译:基于站的单向卡路沙灵(OWC)是一种灵活的方法,可以享受汽车旅行的好处,同时还展示了减轻城市的环境挑战和交通拥堵的潜力。在以前关于OWC的研究中,基于位置的单位单位,例如单站,站集群和陆包裹通常用作需求的基本分析单元;然而,这些研究未能考虑操作区域之间的关联。这导致了与特定OWC系统相关的结果和开发过程的横截面。本研究的目的是探讨与基于流量的需求相关的重要因素(即,从原始空间单元到目的地空间单位(OD预订)的四周预订)。采用四组解释性变量:Carsharing Spatial Unit属性,建筑环境,运输设施和OD行程属性(如公共交通和OD之间的汽车行驶距离)。还开发了一种组合模型集成机器学习和广义线性模型来解决数据的零充气问题。此外,实施了福芙添加剂解释的方法以确定因素的大量影响。结果表明,(1)OD行程属性在估计Carsharing OD需求方面发挥着重要作用; (2)出租车和碳水化合物需求表现出非显着的部分重叠,并且卡雷什可能与公共汽车竞争并补充到地铁; (3)在四周内的任何土地使用的情况下,碳水化合物的旅行目的是多样的。

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