标题： 基于美国航班位置信息的人类移动模式探索 显示英文标题

标题： Exploring Human Mobility Patterns Based on Location Information of US Flights

摘要： 一系列早期研究已使用不同的跟踪数据（如美元纸币、手机和出租车）来说明人类移动模式。 在这里，我们基于美国航班的大量跟踪数据探索人类移动模式。 详细检查了拓扑和几何属性。 我们发现，诸如交通量（机场之间）和连通性程度（单个机场）等拓扑属性，包括入度和出度，遵循幂律分布，而不是像旅行长度这样的几何属性。 旅行长度表现出指数分布，而不是之前研究中所表明的具有指数截断的幂律分布。 我们进一步在机场的既定拓扑结构上模拟了各种移动行为的人类移动，并发现移动模式主要归因于机场的底层二元拓扑结构，与其他因素（如移动行为和几何距离）关系不大。 除了上述发现外，本研究采用了头/尾划分规则，该规则是任何重尾分布背后的规律，用于提取个体机场。 在数据处理中采用这一规则构成了本文的另一大主要贡献。 关键词：地理空间的尺度，头/尾划分规则，幂律，地理信息，基于代理的模拟 显示英文摘要

摘要： A range of early studies have been conducted to illustrate human mobility patterns using different tracking data, such as dollar notes, cell phones and taxicabs. Here, we explore human mobility patterns based on massive tracking data of US flights. Both topological and geometric properties are examined in detail. We found that topological properties, such as traffic volume (between airports) and degree of connectivity (of individual airports), including both in- and outdegrees, follow a power law distribution but not a geometric property like travel lengths. The travel lengths exhibit an exponential distribution rather than a power law with an exponential cutoff as previous studies illustrated. We further simulated human mobility on the established topologies of airports with various moving behaviors and found that the mobility patterns are mainly attributed to the underlying binary topology of airports and have little to do with other factors, such as moving behaviors and geometric distances. Apart from the above findings, this study adopts the head/tail division rule, which is regularity behind any heavy-tailed distribution for extracting individual airports. The adoption of this rule for data processing constitutes another major contribution of this paper. Keywords: scaling of geographic space, head/tail division rule, power law, geographic information, agent-based simulations