城市手机用户移动轨迹时空熵特征分析

利用手机话单数据分析城市个体居民移动活动的时间熵和空间熵特征，一方面探讨了从原始话单记录中进行出行识别的必要性，另一方面提出了一种考虑空间邻近性的轨迹近似熵特征分析方法。其中，出行识别可以克服手机定位数据采样频率较低的缺陷；近似熵分析方法具有强空间鲁棒性，可以减少因手机定位数据空间精度较低带来的影响。实证结果表明，城市居民出行活动既具有强烈的目的地选择倾向，同时也具有强烈的移动路径选择偏好。

An Analysis of Entropy of Human Mobility from Mobile Phone Data

Human mobility patterns have been intensively investigated by scientists from computational social science, statistical physics and complex science. In last decade, mobile phone data provide an unprecedented tool for capturing individuals' travel activities in space and time. However, its nature of sparsity in time and imprecision in space imposes significant bias upon the derived mobility patterns. This research proposes two efficient techniques to cope with this issue. First, we implement an activity-location and travel-OD identification method to reconstruct reliable trajectories from call detailed records for mobile users. Second, we introduce the approximate entropy, which is superior to conditional entropy, for quantifying the regularity of individuals' consecutively visited locations. With a case study in Harbin, the proposed approaches enable us to uncover meaningful patterns of urban mobility in terms of frequently and consecutively visited locations.