In recent years, RAPTOR based algorithms have been considered the state-of-the-art for path-finding with unlimited transfers without preprocessing. However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison. In this work, we revisit classical Dijkstra-based approaches for public transit routing with unlimited transfers and demonstrate that Time-Dependent Dijkstra (TD-Dijkstra) outperforms MR. However, efficient TD-Dijkstra implementations rely on filtering dominated connections during preprocessing, which assumes passengers can always switch to a faster connection. We show that this filtering is unsound when stops have buffer times, as it cannot distinguish between seated passengers who may continue without waiting and transferring passengers who must respect the buffer. To address this limitation, we introduce Transfer Aware Dijkstra (TAD), a modification that scans entire trip sequences rather than individual edges, correctly handling buffer times while maintaining performance advantages over MR. Our experiments on London and Switzerland networks show that we can achieve a greater than two time speed-up over MR while producing optimal results on both networks with and without buffer times.

翻译：近年来，基于RAPTOR的算法被视为无需预处理的无限换乘路径规划领域的最新技术。然而，这一地位主要源于路径搜索研究的发展历程——基于Dijkstra的解决方案被基于时刻表的算法取代时，并未经过系统性对比。本研究重新审视了面向无限换乘的公共交通路径规划中经典Dijkstra方法，并证明时变Dijkstra算法优于MR算法。但高效实现时变Dijkstra依赖于预处理阶段对支配性连接的过滤，该操作假设乘客始终能切换至更快班次。我们证明当站点存在缓存时间时，该过滤方法并不完备——因为它无法区分无需等待即可继续乘车的坐席乘客与必须遵守缓存时间的换乘乘客。为解决这一局限，我们提出换乘感知Dijkstra算法（TAD），通过遍历完整行程序列而非单条边，在正确处理缓存时间的同时保持优于MR的性能优势。在伦敦与瑞士交通网络的实验表明，无论是否存在缓存时间，本算法在产生最优解的前提下，相较MR可实现超过两倍的加速效果。