Public transit schedules contain recurring service structures: many dates share the same passenger-facing timetable, while others differ because of weekends, holidays, seasons, or special events. GTFS does not encode these recurring schedules directly, so downstream scheduling, assignment, and mismatch models lack an explicit recurrent supply object. This paper formalizes recurring schedules as DayTypes, defined by the complete set of Route Pattern trips operating on a date, and develops a stable GTFS-based extraction method using H3 route-pattern keys. We then define a schedule-comparison framework with exact, time-tolerant, and structural-comparability metrics that distinguish strict timetable differences from small timing shifts and larger service changes. Validation on Japanese and Canadian GTFS feeds shows substantial schedule compression, a median of four nonempty DayTypes per agency in the pairwise-analysis sample, hierarchical nesting between service classes, and country-level differences in the persistence of exact disjointness. The resulting DayTypes provide compact Route-Pattern-time schedule sets for timetable synchronization, vehicle scheduling, demand assignment, and schedule-consolidation analysis.

翻译：公共交通时刻表包含周期性的服务结构：许多日期共享相同的乘客时刻表，而其他日期因周末、节假日、季节性调整或特殊活动而有所不同。GTFS格式并未直接编码这些周期性时刻表，因此下游的排班、分配和不匹配模型缺乏明确的周期性供给对象。本文形式化了以日期服务类型（DayTypes）为单位的周期性时刻表概念，该概念由某个日期运营的完整路线模式（Route Pattern）班次集合定义，并开发了一种基于GTFS的稳定提取方法，采用H3路线模式键（Route-Pattern keys）。随后，我们定义了一个时刻表比较框架，包括精确匹配、时间容忍匹配和结构可比较性指标，用以区分严格时刻表差异、细微时间偏移及大规模服务变更。在日本与加拿大的GTFS数据验证显示，时刻表压缩效果显著：成对分析样本中每个运营机构的中位非空日期服务类型数为四个，服务类别间存在层级嵌套关系，且国家间在严格不相交性的持续性上存在差异。生成的日期服务类型提供了紧凑的路线模式-时间时刻表集合，可用于时刻表同步、车辆调度、需求分配及时刻表整合分析。