An unsupervised approach for semantic place annotation of trajectories based on the prior probability

TL;DR

This paper introduces UPAPP, an unsupervised Bayesian method for semantic place annotation of trajectories that leverages spatiotemporal data and prior probabilities, eliminating the need for external or annotated data.

Contribution

The paper presents a novel unsupervised approach for semantic place annotation that does not require retraining or external data, enabling large-scale applications.

Findings

Achieved an overall accuracy of 0.712 in place annotation.

Validated on a dataset of 709 volunteers in Beijing.

Effective without external data or retraining.

Abstract

Semantic place annotation can provide individual semantics, which can be of great help in the field of trajectory data mining. Most existing methods rely on annotated or external data and require retraining following a change of region, thus preventing their large-scale applications. Herein, we propose an unsupervised method denoted as UPAPP for the semantic place annotation of trajectories using spatiotemporal information. The Bayesian Criterion is specifically employed to decompose the spatiotemporal probability of the candidate place into spatial probability, duration probability, and visiting time probability. Spatial information in ROI and POI data is subsequently adopted to calculate the spatial probability. In terms of the temporal probabilities, the Term Frequency Inverse Document Frequency weighting algorithm is used to count the potential visits to different place types in the trajectories, and generates the prior probabilities of the visiting time and duration. The spatiotemporal probability of the candidate place is then combined with the importance of the place category to annotate the visited places. Validation with a trajectory dataset collected by 709 volunteers in Beijing showed that our method achieved an overall and average accuracy of 0.712 and 0.720, respectively, indicating that the visited places can be annotated accurately without any external data.