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.

翻译：旅行时间是可达性度量的基本要素，然而多数可达性分析依赖静态时刻表数据，其假设公共交通服务完全按计划运行。此类表征忽视了由运营条件与服务中断引起的旅行时间显著波动。本研究开发了一个可扩展框架，利用高频车辆定位数据重建经验性公交时刻表。通过使用英国公交开放数据服务（BODS）的全域实时数据流，我们构建了自动化数据采集管道，持续归档车辆位置与每日时刻表数据。随后将观测到的车辆位置与预定线路进行匹配，以推断站点级别的到达与出发时间，从而构建校正后的经验时刻表。所得数据集支持在精细时间分辨率与广阔地理范围内分析旅行时间变异性（TTV）。该框架的计算效率与可扩展性使得融入实际服务表现的全域可达性分析成为可能，为评估公共交通服务与支持交通规划提供了更现实的证据基础。