Spatio-Temporal Routine Mining on Mobile Phone Data

Author:

Qin Tian1ORCID,Shangguan Wufan1,Song Guojie1,Tang Jie2

Affiliation:

1. Peking University, Beijing, China

2. Tsinghua University, Beijing, China PR

Abstract

Mining human behaviors has always been an important subarea of Data Mining. While it provides empirical evidences to psychological/behavioral studies, it also builds the foundation of various big-data systems, which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily commute behaviors of mobile subscribers and further conduct routine mining on them. In this article, we propose to model mobile subscribers’ daily commute behaviors by three levels: location trajectory, one-day pattern, and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain two aspects of information: (1) How people’s typical commute patterns are. (2) How much their commute behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learn model parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human commute behaviors and yields higher accuracy results in trajectory prediction task.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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