Sensing and predicting the pulse of the city through shared bicycling
Sensing and predicting the pulse of the city through shared bicycling
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DOI：
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发表时间：
2009-07
影响因子：
7.3
通讯作者：
Jon E. Froehlich;Joachim Neumann;Nuria Oliver
中科院分区：
文献类型：
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作者：
Jon E. Froehlich;Joachim Neumann;Nuria Oliver
城市范围内的城市基础设施越来越依赖网络技术来改善和扩展其服务。作为这种数字化的副作用，可以感知和分析大量数据，以揭示人类行为模式。在本文中，我们重点关注一种新兴城市基础设施的数字足迹：共享自行车系统。我们提供了一个时空分析的13个星期的自行车站使用巴塞罗那的共享自行车系统，称为Bicing。我们应用聚类技术来识别跨站的共享行为，并显示这些行为如何与位置，邻里和一天中的时间。然后，我们比较实验结果从四个预测模型的近期车站的使用。最后，我们分析了影响因素，如一天中的时间和车站活动的预测能力的算法。
City-wide urban infrastructures are increasingly reliant on network technology to improve and expand their services. As a side effect of this digitalization, large amounts of data can be sensed and analyzed to uncover patterns of human behavior. In this paper, we focus on the digital footprints from one type of emerging urban infrastructure: shared bicycling systems. We provide a spatiotemporal analysis of 13 weeks of bicycle station usage from Barcelona's shared bicycling system, called Bicing. We apply clustering techniques to identify shared behaviors across stations and show how these behaviors relate to location, neighborhood, and time of day. We then compare experimental results from four predictive models of near-term station usage. Finally, we analyze the impact of factors such as time of day and station activity in the prediction capabilities of the algorithms.