Unveiling Activity-Travel Patterns through Topological Data Analysis
Nuoxian Huang, Yulin Wu

TL;DR
This paper introduces a novel application of Topological Data Analysis to urban mobility data, revealing complex activity patterns and heterogeneity among residents, which can improve transportation planning and predictive models.
Contribution
It applies persistent homology to high-dimensional urban mobility data, enabling the extraction of complex activity patterns without data reduction, a novel approach in this context.
Findings
Identified five main activity-travel patterns among residents
Revealed behavioral heterogeneity linked to socio-economic attributes
Provided insights for urban planning and mobility prediction
Abstract
In the context of rapid urbanization, understanding the patterns of urban residents' activities and mobility is crucial for optimizing transportation systems and enhancing urban management efficiency. This study addresses the limitations of traditional travel analysis methods in handling high-dimensional and large-scale spatiotemporal data by incorporating Topological Data Analysis (TDA) techniques, specifically using persistent homology. This method allows for the extraction of information from the topological structure of data, enabling the effective identification and analysis of complex spatiotemporal behavior patterns without reducing the data's dimensionality. We utilized mobile signaling data from a community in Shenzhen, which includes detailed geographic and temporal information, providing an ideal sample for analyzing urban residents' behavior patterns. Using our pattern…
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Taxonomy
TopicsTopological and Geometric Data Analysis · Data Management and Algorithms
