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User satisfaction based model for resource allocation in bike-sharing systems

机译:基于用户满意的自行车共享系统资源分配模型

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摘要

Over the past decade, the number of ongoing bike-sharing programs has remarkably risen. In this framework, operators need appropriate methodologies to support them in optimizing the allocation of their resources to globally enhance the bike-sharing program, even without massive and costly interventions on the existing configuration of the system.In this paper, we propose an optimization model able to determine how to employ a given budget to enhancing a bike-sharing system, maximizing the global user satisfaction. During the day, each bicycle station has a certain number of bikes that fluctuates according to the travel demand; it happens, however, that for certain time slots, the station is full or empty. Then, we propose to consider as key performance indicators the zero-vehicle time and the full-port time, that reflected respectively the duration of vehicle shortage and parking stall unavailability in the stations. Both these indicators, together with the lost users of the system, need to be kept to a minimum if the final aim is maximizing the customer satisfaction, i.e. not forcing the user to use other stations or turn/shift to other travel modes. We have analyzed the historical usage patterns of the bike-sharing stations, smoothing their trends (by wavelets), and operated a preliminary spatio-temporal clustering. Our model verifies the necessity of adding or removing racks to each station, setting at the same time the optimal number of bikes to allocate in them, and decide the eventual realization of further stations. Then, an application, both on a small test and a real-size network, is presented, together with a sensitivity analysis.
机译:在过去十年中,正在进行的自行车分享计划的数量显着上升。在此框架中,运营商需要适当的方法来支持它们在优化其资源分配到全球增强自行车共享程序,即使没有对系统现有配置的巨大和昂贵的干预。在本文中,我们提出了一种优化模型能够确定如何使用给定的预算来增强自行车共享系统,最大化全球用户满意度。白天,每个自行车站都有一定数量的自行车,根据旅行需求波动;然而,它发生在某些时隙,站是满或空的。然后,我们建议考虑作为关键绩效指标的零车时间和全港时间,这分别反映了车辆短缺和停车位不可用的持续时间。如果最终目标最大化客户满意度,则这两个指标都与系统的失落用户一起保持最低限度,即不要强迫用户使用其他站或转向其他旅行模式。我们分析了自行车共享站的历史使用模式,平滑其趋势(按小波),并操作了初步的时空聚类。我们的模型验证了向每个站添加或删除机架的必要性,同时设置最佳的自行车数量分配,并决定进一步的站点的最终实现。然后,呈现在小型测试和实尺寸网络上的应用,以及灵敏度分析。

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