From Raw GPS to GTFS: A Real-World Open Dataset for Bus Travel Time Prediction

Aigerim Mansurova (), Aigerim Mussina (), Sanzhar Aubakirov, Aliya Nugumanova () and Didar Yedilkhan ()
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Aigerim Mansurova: Big Data and Blockchain Technologies Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan
Aigerim Mussina: Department of Computer Science, Al-Farabi Kazakh National University, 71 al-Farabi Avenue, 050040 Almaty, Kazakhstan
Sanzhar Aubakirov: Department of Computer Science, Al-Farabi Kazakh National University, 71 al-Farabi Avenue, 050040 Almaty, Kazakhstan
Aliya Nugumanova: Big Data and Blockchain Technologies Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan
Didar Yedilkhan: Smart City Research and Innovation Center, Astana IT University, 020000 Astana, Kazakhstan

Data, 2025, vol. 10, issue 8, 1-16

Abstract: The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts.

Keywords: GPS; GTFS; public transportation; open data; smart city; bus travel time prediction; bus operations; transit analytics (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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