Anomalous event detection on large-scale GPS data from mobile phones using hidden markov model and cloud platform

Abstract

Anomaly detection is an important issue in various research fields. An uncommon trajectory or gathering of people in a specific area might correspond to a special event such as a festival, traffic accident or natural disaster. In this paper, we aim to develop a system for detecting such anomalous events in grid-based areas. A framework based on a hidden Markov model is proposed to construct a pattern of spatio-temporal movement of people in each grid during each time period. The numbers of GPS points and unique users in each grid were used as features and evaluated. We also introduced the use of local score to improve the accuracy of the event detection. In addition, we utilized Hadoop, a cloud-computing platform, to accelerate the processing speed and allow the handling of large-scale data. We evaluated the system using a dataset of GPS trajectories of 1.5 million individual mobile phone users accumulated over a one-year period, which constitutes approximately 9.2 billion records.