论文标题
人类流动性的频谱维度
The spectral dimension of human mobility
论文作者
论文摘要
人类流动性模式令人惊讶地结构化。尽管有许多难以模拟气候,文化和社会经济机会等因素,但总体迁移率遵守了通用,无参数,“辐射”模型。最近的工作进一步表明,这些流的详细频谱分解(定义为访问给定位置的个人数量,该位置具有频率$ f $从远距离$ r $ akay的频率 - 遵守简单的规则,即,在组合中缩放为$ rf $的通用倒数平方法。但是,这种令人惊讶的规律性是基于一般理由得出的,尚未通过微观行为的微观机制来解释。在这里,我们通过分析来自三个不同区域的大型手机数据集来确认这一点,并表明该缩放定律的直接后果是,访问者在特定位置花费的平均“旅行能量”在整个空间之间一直是不变的,这使人们想起了众所周知的人类运动的旅行预算假设。我们通过对该位置的访问总数定义的不同位置的吸引力也接受了非平凡的空间群集结构。观察到的模式与城市地理学中著名的中心位置理论以及空间经济中的韦伯最优性概念一致,这暗示了人类的集体优化重复运动的能力。我们通过提出一个简单的微观人类流动性模型来结束,该模型同时捕获了我们所有的经验发现。我们的结果与运输,城市规划,地理和其他学科有关,其中对人类流动性的更深入了解至关重要。
Human mobility patterns are surprisingly structured. In spite of many hard to model factors, such as climate, culture, and socioeconomic opportunities, aggregate migration rates obey a universal, parameter-free, `radiation' model. Recent work has further shown that the detailed spectral decomposition of these flows -- defined as the number of individuals that visit a given location with frequency $f$ from a distance $r$ away -- also obeys simple rules, namely, scaling as a universal inverse square law in the combination, $rf$. However, this surprising regularity, derived on general grounds, has not been explained through microscopic mechanisms of individual behavior. Here we confirm this by analyzing large-scale cell phone datasets from three distinct regions and show that a direct consequence of this scaling law is that the average `travel energy' spent by visitors to a given location is constant across space, a finding reminiscent of the well-known travel budget hypothesis of human movement. The attractivity of different locations, which we define by the total number of visits to that location, also admits non-trivial, spatially-clustered structure. The observed pattern is consistent with the well-known central place theory in urban geography, as well as with the notion of Weber optimality in spatial economy, hinting to a collective human capacity of optimizing recurrent movements. We close by proposing a simple, microscopic human mobility model which simultaneously captures all our empirical findings. Our results have relevance for transportation, urban planning, geography, and other disciplines in which a deeper understanding of aggregate human mobility is key.