Li Ruiqi, Gao Shuai, Luo Ankang, Yao Qing, Chen Bingsheng, Shang Fan, Jiang Rui, Stanley H Eugene
UrbanNet Lab, College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
School of Systems Science, Beijing Normal University, Beijing 100875, China.
Phys Rev E. 2021 Jan;103(1-1):012312. doi: 10.1103/PhysRevE.103.012312.
Due to previous technical challenges with the collection of data on riding behaviors, there have only been a few studies focusing on patterns and regularities of biking traffic, which are crucial to understand to help achieve a greener and more sustainable future urban development. Recently, with the booming of the sharing economy, and the development of the Internet of Things (IoT) and mobile payment technology, dockless bike-sharing systems that record information for every trip provide us with a unique opportunity to study the patterns of biking traffic within cities. We first reveal a spatial scaling relation between the cumulative volume of riding activities and the corresponding distance to the city center, and a power law distribution on the volume of biking flows between fine-grained locations in both Beijing and Shanghai. We validate the effectiveness of the general gravity model on predicting biking traffic at fine spatial resolutions, where population-related parameters are less than unity, indicating that smaller populations are relatively more important per capita in generating biking traffic. We then further study the impacts of spatial scale on the gravity model and reveal that the distance-related parameter grows in a similar way as population-related parameters when the spatial scale of the locations increases. In addition, the flow patterns of some special locations (sources and sinks) that cannot be fully explained by the gravity model are studied.
由于之前在收集骑行行为数据方面存在技术挑战，仅有少数研究关注自行车交通的模式和规律，而了解这些对于实现更绿色、更可持续的未来城市发展至关重要。最近，随着共享经济的蓬勃发展以及物联网（IoT）和移动支付技术的进步，记录每次行程信息的无桩共享单车系统为我们提供了一个研究城市内自行车交通模式的独特机会。我们首先揭示了骑行活动累积量与到市中心相应距离之间的空间缩放关系，以及北京和上海细粒度位置之间自行车流量的幂律分布。我们验证了一般引力模型在预测精细空间分辨率下自行车交通方面的有效性，其中与人口相关的参数小于1，这表明在产生自行车交通方面，较小的人口人均相对更重要。然后我们进一步研究空间尺度对引力模型的影响，并揭示当位置的空间尺度增加时，与距离相关的参数与与人口相关的参数以类似方式增长。此外，还研究了一些无法用引力模型完全解释的特殊位置（源和汇）的流量模式。
