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：公共交通路径规划算法，特别是广泛使用的RAPTOR及其变体，在支持无限次换乘时，尤其在密集换乘图上，常因换乘松弛阶段而面临性能瓶颈。这种低效源于需迭代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，这可能导致路径最优性降低，并限制向出行者展示的多模式选项。本文提出早期剪枝（Early Pruning），一种低开销的技术，可在不牺牲最优性的前提下加速路径规划算法。通过按持续时间预排序换乘连接，并在换乘循环内应用剪枝规则，该方法能在某一站点的较长换乘无法产生优于当前最优方案的更早到达时间时将其丢弃。早期剪枝可以最小的改动集成到现有代码库中，并仅需一次预处理步骤。当额外优化准则随换乘持续时间单调非递减时，该技术能在扩展准则设置中保留帕累托最优性。在多种基于RAPTOR的最新解决方案中，包括RAPTOR、ULTRA-RAPTOR、McRAPTOR、BM-RAPTOR、ULTRA-McRAPTOR和UBM-RAPTOR，并在瑞士和伦敦的交通网络上测试，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供了普遍适用的改进。