Human mobility is an important characteristic of human behavior, but since tracking personalized position to high temporal and spatial resolution is difficult, most studies on human mobility patterns rely on sparsely sampled position data. In this work, we re-examined human mobility patterns via comprehensive cell-phone position data recorded at a high frequency up to every second. We constructed human mobility networks and found that individuals exhibit origin-dependent, path-preferential patterns in their short time-scale mobility. These behaviors are prominent when the temporal resolution of the data is high, and are thus overlooked in most previous studies. Incorporating measured quantities from our high frequency data into conventional human mobility models shows inconsistent statistical results. We finally revealed that the individual preferential transition mechanism characterized by the first-order Markov process can quantitatively reproduce the observed travel patterns at both individual and population levels at all relevant time-scales.

人的流动性是人类行为的重要特征，但是由于很难将个性化位置跟踪到高的时间和空间分辨率，因此大多数有关人流动性模式的研究都依赖于稀疏采样的位置数据。在这项工作中，我们通过以每秒最高频率记录的全面的手机位置数据，重新检查了人类的出行方式。我们构建了人类出行网络，发现个人在短时间尺度上的出行表现出了与起点相关，路径优先的模式。当数据的时间分辨率很高时，这些行为就很突出，因此在大多数以前的研究中都被忽略了。将根据我们的高频数据测得的量结合到传统的人类移动性模型中，会得出不一致的统计结果。