Gaussian Mixture Models for Parking Demand Data
Gaussian Mixture Models for Parking Demand Data
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DOI：
10.1109/tits.2019.2939499
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发表时间：
2020-08
影响因子：
8.5
通讯作者：
Tanner Fiez;L. Ratliff
中科院分区：
文献类型：
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作者：
Tanner Fiez;L. Ratliff
为了缓解驾驶员巡航寻找停车位所造成的拥堵，基于性能的定价方案受到了极大的关注。然而，最近的几项研究表明，位置、时间和政策意识是影响停车决策的主要因素。利用由西雅图交通部提供的数据，并考虑到上述决策因素，我们分析了路边停车需求的空间和时间属性，并提出了方法，可以改善传统的政策，直接修改，通过推进了解在哪里和何时管理定价政策。具体来说，我们开发了一个高斯混合模型为基础的技术，以确定类似的停车需求的空间自相关量化区。为了支持这种技术，我们引入了一个度量的基础上，我们的高斯混合模型的可重复性调查时间的一致性。
To mitigate congestion caused by drivers cruising in search of parking, performance-based pricing schemes have received a significant amount of attention. However, several recent studies suggest location, time-of-day, and awareness of policies are the primary factors that drive parking decisions. Harnessing data provided by the Seattle Department of Transportation and considering the aforementioned decision-making factors, we analyze the spatial and temporal properties of curbside parking demand and propose methods that can improve traditional policies with straightforward modifications by advancing the understanding of where and when to administer pricing policies. Specifically, we develop a Gaussian mixture model based technique to identify zones with similar parking demand as quantified by spatial autocorrelation. In support of this technique, we introduce a metric based on the repeatability of our Gaussian mixture model to investigate temporal consistency.