Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the transfer relaxation phase, especially on dense transfer graphs, when supporting unlimited transfers. This inefficiency arises from iterating over many potential inter-stop connections (walks, bikes, e-scooters, etc.). To maintain acceptable performance, practitioners often limit transfer distances or exclude certain transfer options, which can reduce path optimality and restrict the multimodal options presented to travellers. This paper introduces Early Pruning, a low-overhead technique that accelerates routing algorithms without compromising optimality. By pre-sorting transfer connections by duration and applying a pruning rule within the transfer loop, the method discards longer transfers at a stop once they cannot yield an earlier arrival than the current best solution. Early Pruning can be integrated with minimal changes to existing codebases and requires only a one-time preprocessing step. The technique preserves Pareto-optimality in extended-criteria settings whenever the additional optimization criteria are monotonically non-decreasing in transfer duration. Across multiple state-of-the-art RAPTOR-based solutions, including RAPTOR, ULTRA-RAPTOR, McRAPTOR, BM-RAPTOR, ULTRA-McRAPTOR, and UBM-RAPTOR and tested on the Switzerland and London transit networks, we achieved query time reductions of up to 57\%. This approach provides a generalizable improvement to the efficiency of transit pathfinding algorithms.

翻译：公共交通路由算法，特别是广泛使用的RAPTOR及其变体，在支持无限制换乘时，尤其是在密集换乘图中，常因换乘松弛阶段面临性能瓶颈。这种低效源于需要遍历大量潜在的站点间连接（步行、自行车、电动滑板车等）。为维持可接受的性能，实践者常限制换乘距离或排除某些换乘选项，但这可能降低路径的最优性并限制呈现给出行者的多模式选择。本文提出"早期剪枝"技术——一种低开销方法，可在不牺牲最优性的前提下加速路由算法。通过按持续时间对换乘连接预排序，并在换乘循环中应用剪枝规则，该方法能在某站点上的较长换乘无法比当前最优解更早到达时将其舍弃。该技术只需对现有代码库进行最小修改，且仅需一次性预处理步骤。在扩展多准则场景中，只要额外优化准则关于换乘持续时间单调非递减，本技术即可保持帕累托最优性。在瑞士和伦敦交通网络上，对包括RAPTOR、ULTRA-RAPTOR、McRAPTOR、BM-RAPTOR、ULTRA-McRAPTOR和UBM-RAPTOR在内的多个基于RAPTOR的先进方案进行测试，我们实现了高达57%的查询时间缩减。该方法为交通路径搜索算法的效率提升提供了可泛化的改进方案。