A Python package for analyzing public transport networks using GTFS data, enabling accessibility analysis and network statistics.
gtfspy is a Python package for analyzing public transport networks using timetable data in the GTFS format. It enables importing, filtering, and querying transit data to compute statistics, perform accessibility analyses, and generate network extracts. The tool solves the problem of efficiently processing and deriving insights from complex public transport schedules.
Researchers, urban planners, data scientists, and developers working on transportation analytics, accessibility studies, or public transport network optimization.
Developers choose gtfspy for its integrated approach combining GTFS data management with advanced routing algorithms, open-source flexibility, and support for reproducible research workflows in transportation science.
Public transport network analysis using Python 🚊🚇🚃🚌🛳️🚡🚠🚞
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Leverages SQLite databases for fast querying and filtering of GTFS data, enabling scalable analysis as highlighted in the import and export examples.
Implements an adapted Connection Scan Algorithm for computing Pareto-optimal journeys, providing robust accessibility analysis backed by academic research.
Enhances transit data with real walking distances from OpenStreetMap, improving routing realism for urban planning and research workflows.
Designed with research in mind, supported by published papers and examples for reproducible transportation studies, as noted in the acknowledgments.
The README explicitly states Windows is less tested and may require manual steps like installing Shapely wheels and Visual C++ Build Tools, indicating poor out-of-box experience.
Admits in the versioning section that code organization and interfaces may change rapidly, making it risky for production deployments or long-term projects.
Requires additional libraries like Shapely and potential system tools, especially on Windows, leading to a steeper initial configuration compared to simpler GTFS tools.