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Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System

机译:基于网格的出行需求模式聚类的加权动态时间规整:以北京自行车共享系统为例

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Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.
机译:运输系统(例如用户订单系统或自动票价收集(AFC)系统)收集的多种时空数据可以离散化并转换为时间序列数据。使用时序数据挖掘技术,可以检测到城市中不同区域的某些旅行需求模式。这项研究提出了一个数据挖掘模型,用于从时空数据集中了解城市地区人类活动的模式和规律。该模型使用基于网格的方法将时空点数据集转换为离散的时间序列。然后,使用时间序列分析技术动态时间规整(DTW)来描述旅行需求序列之间的相似性,而基于聚类算法的基于噪声的应用程序基于密度的空间聚类(DBSCAN)基于改进的DTW进行检测聚集在旅行需求样本中。找到了四种典型的模式,包括平衡和不平衡的情况。这些发现有助于了解城市的土地利用结构和通勤活动。结果表明,本研究开发的基于网格的模型和时间序列分析模型可以有效地从公共交通系统的使用数据中揭示出旅行需求的时空特征。

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