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Exploring patterns of demand in bike sharing systems via replicated point process models

机译:通过复制点过程模型探索自行车共享系统的需求模式

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Understanding patterns of demand is fundamental for fleet management of bike sharing systems. We analyse data from the Divvy system of the city of Chicago. We show that the demand for bicycles can be modelled as a multivariate temporal point process, with each dimension corresponding to a bike station in the network. The availability of daily replications of the process enables non-parametric estimation of the intensity functions, even for stations with low daily counts, and straightforward estimation of pairwise correlations between stations. These correlations are then used for clustering, revealing different patterns of bike usage.
机译:了解需求模式对于自行车共享系统的车队管理至关重要。我们分析了来自芝加哥市Divvy系统的数据。我们表明,可以将自行车需求建模为多元时间点过程,每个维度都对应于网络中的自行车站。该过程的每日复制的可用性使强度函数可以进行非参数估计,即使对于每日计数较低的站点,也可以直接估计站点之间的成对相关性。然后将这些相关性用于聚类,揭示自行车使用的不同模式。

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