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Cited by 58Human Mobility and Location-Based AnalysisData Management and AlgorithmsUrban Transport and Accessibility

Trackintel: An open-source Python library for human mobility analysis

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Martin, Henry, Hong, Ye, Wiedemann, Nina, Bucher, Dominik, Raubal, Martin·2023·Computers, Environment and Urban Systems
DOI 10.1016/j.compenvurbsys.2023.101938
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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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  • scikit-mobility and Trackintel focus on large-scale mobility modeling rather than detailed motion quantification.

    “scikit-mobility and Trackintel focus on large-scale mobility modeling rather than detailed motion quantification [5,7]”

    From PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos (2025) · cites this paper as [5]

    The Related Work section (p. 3) describes scikit-mobility as targeting high-level analysis and Trackintel as using semantic aggregation levels for human mobility. The Introduction (p. 2) also describes Trackintel as a library for processing and analyzing movement data. These passages support a contrast with low-level trajectory manipulation, but they do not establish that Trackintel focuses on large-scale mobility modeling rather than detailed motion quantification. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Most studies on motion analysis focus on qualitative aspects, while standardized and accessible libraries that enable precise movement quantification in measurement units remain a challenge.

    “Although research on motion analysis is expanding, most studies focus on qualitative aspects, while standardized and accessible libraries that enable precise movement quantification in measurement units remain a challenge [3-5].”

    From PyBodyTrack: A python library for multi-algorithm motion quantification and tracking in videos (2025) · cites this paper as [5]

    The Discussion and Conclusion (p. 14) says quantitative human-mobility analysis lacks a common data model and standardized preprocessing; Related Work (pp. 2–3) describes gaps in accessible, comprehensive libraries. The paper does not say that most motion-analysis studies focus on qualitative aspects or that precise quantification in measurement units is the particular challenge. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 2

  1. [33][33] Quannan Li, Yu Zheng, Xing Xie, Yukun Chen, Wenyu Liu, and Wei-Ying Ma. Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems , pages 1-10, 2008. -
  2. [40][40] The pandas development team. pandas-dev/pandas: Pandas, February 2020. URL https://doi.org/10.5281/ zenodo.3509134 . -

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Authors
Martin, H., Hong, Y., Wiedemann, N., Bucher, D., & Raubal, M.
Affiliations
ETH Zurich (CH)
Corresponding author
Martin, Henry
ORCID
Venue
Computers, Environment and Urban Systems
Publisher
Elsevier BV
Published
2023-04
Volume
101
Pages
101938
ISSN
0198-9715 (ISSN-L)
DOI
10.1016/j.compenvurbsys.2023.101938
OpenAlex
W4319336084
Cited by
58
References
61
Citations by year
2026: 15 · 2025: 20 · 2024: 12 · 2023: 7 · 2022: 4
FWCI
11.09
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers Environment and Urban Systems (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
  • ETH Zürich Research Collection
Funders
  • Eidgenössische Technische Hochschule Zürich
  • ETH Zürich Foundation
Topics
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
  • Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
  • Social Sciences › Transportation › Urban Transport and Accessibility
Keywords
tracking data, data pipelines, reproducibility, data quality assessment, open-source Python package, transportation planning

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