A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data

TL;DR

This paper introduces a probabilistic, non-parametric Bayesian method using Infinite Gaussian Mixture Models and Kullback-Leibler divergence to automatically discover daily human mobility patterns from GPS data, outperforming traditional GMM approaches.

Contribution

It presents a novel, automatic clustering algorithm that does not require predefining the number of clusters, improving mobility pattern discovery from GPS data.

Findings

IGMM outperforms GMM in pattern detection.

Effective with varying data lengths.

Automates cluster number determination.

Abstract

Discovering human mobility patterns with geo-location data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem from a probabilistic perspective in order to explore more information from the original GPS data compared to other conventional methods. A non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, is used to estimate the probability density for the daily mobility. Then, we use Kullback-Leibler divergence as the metrics to measure the similarity of different probability distributions. And combining Infinite Gaussian Mixture Model and Kullback-Leibler divergence, we derived an automatic clustering algorithm to discover mobility patterns for each individual user without setting the number of clusters in advance. In the experiments, the effectiveness of our method is validated on the real user data collected from different users. The results show that the IGMM-based algorithm outperforms the GMM-based algorithm. We also test our methods on the dataset with different lengths to discover the minimum data length for discovering mobility patterns.