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 However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison.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 the London and Switzerland networks show that we can achieve more than a twofold speedup 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次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发展历程——基于Dijkstra的解决方案被基于时刻表的算法取代，而两者之间缺乏系统性比较。在本研究中，我们重新审视了面向公共交通无限次换乘的经典Dijkstra方法，证明时间依赖型Dijkstra（TD-Dijkstra）优于MR算法。然而，高效的TD-Dijkstra实现依赖于预处理阶段对受支配连接的过滤，这假设乘客总能切换至更快的连接。我们发现，当站点存在缓冲时间时，该过滤方法不成立，因其无法区分可能无需等待即可继续行程的座位乘客与必须遵守缓冲时间的换乘乘客。为解决此局限，我们提出换乘感知Dijkstra算法（TAD），该改进方法通过扫描完整行程序列而非单条边，在保持优于MR性能优势的同时正确应对缓冲时间。在伦敦和瑞士路网上的实验表明，无论是否存在缓冲时间，我们的算法均能获得最优结果，且速度比MR提升两倍以上。