Collective Human Mobility Pattern from Taxi Trips in Urban Area

We analyze the passengers' traffic pattern for 1.58 million taxi trips of Shanghai, China. By employing the non-negative matrix factorization and optimization methods, we find that, people travel on workdays mainly for three purposes: commuting between home and workplace, traveling from workplace to workplace, and others such as leisure activities. Therefore, traffic flow in one area or between any pair of locations can be approximated by a linear combination of three basis flows, corresponding to the three purposes respectively. We name the coefficients in the linear combination as traffic powers, each of which indicates the strength of each basis flow. The traffic powers on different days are typically different even for the same location, due to the uncertainty of the human motion. Therefore, we provide a probability distribution function for the relative deviation of the traffic power. This distribution function is in terms of a series of functions for normalized binomial distributions. It can be well explained by statistical theories and is verified by empirical data. These findings are applicable in predicting the road traffic, tracing the traffic pattern and diagnosing the traffic related abnormal events. These results can also be used to infer land uses of urban area quite parsimoniously.本研究基于中国上海市158万次出租车出行数据，分析乘客的交通出行模式。通过采用非负矩阵分解（Non-negative Matrix Factorization）与优化方法，本研究发现：工作日居民的出行主要分为三类目的，即家与工作地间的通勤、工作地之间的移动，以及休闲活动等其他出行。据此，某一区域内或任意两地间的交通流量，可通过三类基础流量的线性组合进行近似拟合，该三类基础流量分别对应上述三类出行目的。我们将该线性组合中的系数命名为交通权重（traffic powers），各系数分别对应各类基础流量的强度。即便针对同一区域，不同日期的交通权重也往往存在差异，这源于人类出行行为的不确定性。据此，本研究针对交通权重的相对偏差给出了概率分布函数，该函数以一系列归一化二项分布函数为基础构建。该分布函数可通过统计学理论得到合理解释，并经实证数据验证有效。本研究的相关发现可应用于道路交通流量预测、交通模式溯源以及交通异常事件诊断等场景。此外，本研究结果还可用于较为简约地推断城市区域的土地利用属性。