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arXiv · 2603.11477

rt2gtfs: A scalable framework for correcting public transport timetables using real-time data for accessibility analysis

Zihao Chen·Federico Botta
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Abstract

Travel time is a fundamental component of accessibility measurement, yet most accessibility analyses rely on static timetable data that assume public transport services operate exactly as scheduled. Such representations overlook the substantial variability in travel times arising from operational conditions and service disruptions. In this paper, we present rt2gtfs, an open-source Python package for reconstructing empirical public transport timetables from high-frequency vehicle location data. The package provides a configurable and scalable workflow for collecting GTFS-Realtime vehicle position feeds from the UK Bus Open Data Service (BODS), matching observed vehicle locations to scheduled GTFS trips and stops, inferring stop-level arrival and departure times, and exporting corrected GTFS format timetable bundles. Using national-scale real-time bus data feeds from BODS, we demonstrate how rt2gtfs can be used to generate observed timetables suitable for routing and origin-destination travel time calculation, as well as accessibility analysis. By packaging the framework as reusable software, this work supports more reproducible and realistic accessibility analysis and provides a practical tool for researchers and practitioners seeking to incorporate observed public transport performance into transport planning.

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Zihao Chen, Federico Botta. 2026-03-12. rt2gtfs: A scalable framework for correcting public transport timetables using real-time data for accessibility analysis. https://arxiv.org/abs/2603.11477

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