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首页> 外文期刊>Journal of geovisualization and spatial analysis >Visualizing the Spatiotemporal Characteristics of Dockless Bike Sharing Usage in Shenzhen, China
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Visualizing the Spatiotemporal Characteristics of Dockless Bike Sharing Usage in Shenzhen, China

机译：可视化的时空特征在中国深圳,Dockless自行车共享使用

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摘要

A comprehensive understanding of the spatiotemporal characteristics and patterns of dockless bike sharing usage is crucial in developing bike management and scheduling strategies. Recently, bike sharing-related topics have become a popular research subject. Existing studies have mainly analyzed the temporal and spatial characteristics of dockless bike sharing usage separately and have not explored how temporal patterns vary for different spatial units, even though this information is key to developing a straightforward profile of bike usage and implementing spatiotemporal scheduling strategies. To address this research gap, the space-time cube model and an emerging hot spot analysis were integrated into this study to identify the spatiotemporal patterns and hot/cold spot trends of dockless bike sharing usage in Shenzhen, China. The main goal of this study is to understand and visualize the spatiotemporal characteristics and patterns of dockless bike sharing usage with over 6.21 million GPS data processed, and to provide an analysis with integrated application of the space-time cube model and emerging hot spot analysis. We visualized the usage behavior characteristics, including riding distance, duration, and frequency, explored the spatiotemporal heterogeneity of riding origins and destinations, and identified spatiotemporal hot/cold spots for scheduling strategies. These results provide a valuable guide for developing bike spatiotemporal scheduling strategies.
机译：一个全面的了解时空特征和模式dockless自行车共享使用是至关重要的发展自行车管理和调度策略。已经成为一个热门研究课题。主要分析了时间和研究dockless自行车共享的空间特征分开使用,并没有探讨如何时间模式改变为不同的空间单位,即使这些信息是关键开发一个简单的自行车使用和实现时空调度策略。时空多维数据集模型和一个新兴的热点分析被纳入本研究确定时空模式和热/冷趋势dockless自行车共享使用深圳,中国。理解和想象的时空dockless自行车的特点和模式与超过621万个GPS数据分享使用处理,并提供一个分析集成的应用程序的时空多维数据集模型和新兴热点分析。可视化的使用行为特征,包括骑的距离,时间,频率,探讨了时空异质性的来源和目的地,和确定时空热点或冷点调度策略。宝贵的指南开发自行车时空调度策略。

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