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

TL;DR

This paper presents a scalable framework that reconstructs empirical bus timetables from real-time vehicle location data, enabling more accurate accessibility analysis by incorporating actual travel time variability.

Contribution

The study introduces an automated, scalable method to derive empirical timetables from high-frequency data, improving the realism of accessibility assessments.

Findings

Framework successfully reconstructs empirical timetables at national scale.

Enables detailed analysis of travel time variability across large regions.

Supports more accurate public transport accessibility evaluations.

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.