Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning

TL;DR

This paper presents a semi-supervised transfer learning model that reconstructs human mobility patterns from trajectory data, effectively adapting across diverse datasets and capturing semantic activity chains for better urban planning insights.

Contribution

The study introduces a novel semi-supervised transfer learning approach that reconstructs semantic activity chains and adapts to different geographical contexts, addressing data scarcity and incompleteness.

Findings

Achieved a low Jensen-Shannon Divergence of 0.001 on US datasets, indicating high similarity between real and synthetic data.

Successfully transferred mobility patterns from the US to Egypt, reducing JSD from 0.09 to 0.03, a 64% improvement.

Demonstrated the model's effectiveness in reconstructing activity chains and generating high-quality synthetic mobility data.

Abstract

Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the reliance on trajectory data, which often fails to capture the semantic interdependencies of activities, and the inherent incompleteness of real-world trajectory data. We have developed a model that reconstructs and learns human mobility patterns by focusing on semantic activity chains. We introduce a semi-supervised iterative transfer learning algorithm to adapt models to diverse geographical contexts and address data scarcity. Our model is validated using comprehensive datasets from the United States, where it effectively reconstructs activity chains and generates high-quality synthetic mobility data, achieving a low Jensen-Shannon Divergence (JSD) value of 0.001, indicating a close similarity between synthetic and real data. Additionally, sparse GPS data from Egypt is used to evaluate the transfer learning algorithm, demonstrating successful adaptation of US mobility patterns to Egyptian contexts, achieving a 64\% of increase in similarity, i.e., a JSD reduction from 0.09 to 0.03. This mobility reconstruction model and the associated transfer learning algorithm show significant potential for global human mobility modeling studies, enabling policymakers and researchers to design more effective and culturally tailored transportation solutions.