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.

翻译：出行时间是可达性测量的基本组成部分，然而大多数可达性分析依赖静态时刻表数据，假设公共交通服务完全按计划运行。这种表示忽略了因运营条件和服务中断导致的出行时间显著变异性。本文提出rt2gtfs，一个用于从高频车辆定位数据重建经验性公共交通时刻表的开源Python包。该包提供了一个可配置且可扩展的工作流程，用于从英国公交开放数据服务（BODS）收集GTFS-Realtime车辆位置数据流，将观测到的车辆位置与计划中的GTFS行程和站点进行匹配，推断站点级别的到达和出发时间，并导出修正后的GTFS格式时刻表包。利用BODS提供的全国规模实时公交数据流，我们展示了如何使用rt2gtfs生成适用于路径规划、起终点出行时间计算以及可达性分析的经验性时刻表。通过将该框架封装为可复用的软件，这项工作支持更具可复现性和现实性的可达性分析，并为寻求将观测到的公共交通运行性能纳入交通规划的研究者和实践者提供了一个实用工具。