Trackintel: An open-source Python library for human mobility analysis
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Author / Producer
Date
2023-04
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility. The library is available open-source at https://github.com/mie-lab/trackintel.
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Publication status
published
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Book title
Journal / series
Volume
101
Pages / Article No.
101938
Publisher
Elsevier
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Human mobility analysis; Open-source software; Transport planning; Data mining; Python; Tracking studies
Organisational unit
03901 - Raubal, Martin / Raubal, Martin