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 However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison.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搜索领域的最新技术。然而，这一地位很大程度上源于路径规划研究的演进过程——基于Dijkstra的解决方案被基于时刻表的算法取代，而两者缺乏系统性的比较。本研究重新审视了面向无限换乘公交路径规划的经典Dijkstra方法，并证明时变Dijkstra（TD-Dijkstra）算法优于MR算法。但高效的TD-Dijkstra实现依赖预处理阶段对受支配连接的过滤，其前提假设是乘客总能切换至更快的连接。我们证明，当站点存在缓冲区时间时，该过滤方法不成立，因为它无法区分可能无需等待即可继续行程的坐席乘客与必须遵守缓冲区时间的换乘乘客。为解决这一局限，我们提出换乘感知Dijkstra（TAD）算法——通过扫描完整行程序列而非单条边进行改进，能在维持优于MR性能的同时正确处理缓冲区时间。在伦敦与瑞士交通网络上的实验表明，无论有无缓冲区时间，该算法均能在两个网络上取得最优结果，且速度较MR提升两倍以上。