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

Zihao Chen, Federico Botta

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 study, we develop a scalable framework for reconstructing empirical bus timetables from high-frequency vehicle location data. Using national-scale real-time feeds from the UK Bus Open Data Service (BODS), we implement an automated data collection pipeline that continuously archives vehicle positions and daily timetable data. Observed vehicle locations are then matched to scheduled routes to infer stop-level arrival and departure times, enabling the construction of corrected empirical timetables. The resulting dataset allows travel time variability (TTV) to be analysed at fine temporal resolution and across large geographic areas. The computational efficiency and scalability of the framework enable national-scale accessibility analyses that incorporate observed service performance, providing a more realistic evidence base for evaluating public transport services and supporting transport planning.

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

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 study, we develop a scalable framework for reconstructing empirical bus timetables from high-frequency vehicle location data. Using national-scale real-time feeds from the UK Bus Open Data Service (BODS), we implement an automated data collection pipeline that continuously archives vehicle positions and daily timetable data. Observed vehicle locations are then matched to scheduled routes to infer stop-level arrival and departure times, enabling the construction of corrected empirical timetables. The resulting dataset allows travel time variability (TTV) to be analysed at fine temporal resolution and across large geographic areas. The computational efficiency and scalability of the framework enable national-scale accessibility analyses that incorporate observed service performance, providing a more realistic evidence base for evaluating public transport services and supporting transport planning.
Paper Structure (14 sections, 4 equations, 3 figures, 3 tables)

This paper contains 14 sections, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. The definition of travel time variability
  4. Incorporating real-time data in travel time and accessibility calculation
  5. Methodology
  6. Data collection
  7. Data preparation (gtfsrt_to_csv.py)
  8. Matching vehicle locations to stops (gtfsrt_to_gtfs.py)
  9. Inferring empirical stop times
  10. Interpolating missing stop times
  11. Finalisation and optimisation
  12. Case Studies
  13. Conclusion
  14. Acknowledgement

Figures (3)

  • Figure 1: Map showing the matching procedure. Candidate Stops A, B, and C were identified during the initial coarse search within a rough 300-metre radius of the recorded vehicle position at 17:23:07. The exact distance between the vehicle position and the three candidate stops were then calculated, with Stop B being identified as the closest match at the shortest distance of 60 metres. Basemap tiles © OpenStreetMap contributors.
  • Figure 2: Maps of the gaps between scheduled and observed average travel time and travel time variability (TTV) for journeys from each LSOA in England to the nearest hospital. Red indicates observed travel time and TTV are higher than scheduled while blue indicates the opposite. LSOAs shown in grey are unable to reach a hospital by bus within two hours.
  • Figure 3: Scatterplot of travel time variability (TTV) versus average travel time for trips from each LSOA in England to the nearby town centre, with points color-coded by settlement type (urban/rural). Minimum, median, and 85th percentile travel times were calculated for travel times between 8:00 and 10:00 on each weekday from May to October 2025.