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 However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison.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的算法被认为是无预处理无限制换乘寻路领域的最先进技术。然而，这一地位很大程度上源于路径搜索研究的发展历程——基于时间表的算法取代了迪杰斯特拉类解决方案，且缺乏系统性比较。在本研究中，我们重新审视基于经典迪杰斯特拉方法的无限制换乘公交路径规划，并证明时间相关迪杰斯特拉算法（TD-Dijkstra）的性能优于MR算法。然而，高效的TD-Dijkstra实现依赖于预处理阶段对支配连接的过滤，其前提假设是乘客总能切换到更快的连接。我们证明，当站点存在缓冲区时间时，这种过滤机制并不成立——它无法区分无需等待即可继续行驶的已座位乘客与必须遵守缓冲区时间的换乘乘客。为解决此局限，我们提出换乘感知迪杰斯特拉算法（TAD），通过扫描完整行程序列而非单个边进行改进，在正确处理缓冲区时间的同时保持优于MR的性能优势。在伦敦和瑞士交通网络上的实验表明，无论是否存在缓冲区时间，本算法在两类网络中均能实现比MR快两倍以上的加速效果，同时输出最优结果。