Detecting Suspects by Large-Scale Trajectory Patterns in the City

A massive amount of spatial-temporal records generated by sensors across the city help describe our day-to-day activities. Since the lifestyle represented by moving data varies from one individual to another, data analysts could facilitate the suspect-detection task by analyzing and classifying related trajectories of a given target. However, there are still some challenges that need to be overcome in real-life cases; for instance, the positive instances are limited, the trajectories are too diverse, and the transit behavior features are both too broader and costly to define. Moreover, people living in different areas of the city may have different life habits which can result in incorrect conclusions due to data-sensitive factors. In this paper, we describe the particular characteristics of movement behaviors regarding trajectory features. We also propose two models to improve the identification performance, namely, the trajectory pattern model (TPM) and neural network-based model. The trajectory pattern model (TPM) offers a novel view to describe users' movement behaviors and generates more effective and universal features other than location and timestamp dimensions. The end-to-end neural network-based model aims to avoid picking human features. Statistical analysis and insightful explanations are provided to help understand the behavior of a given target. The effectiveness of our proposed solutions compared to peer solutions is demonstrated and proved via extensive evaluation.