State-of-the-art multimodal journey-planning algorithms, such as ULTRA, have recently been adapted to account for delays. In this work, we extend this approach to be more memory-efficient, faster, and accurate. We also adapt this framework to other state-of-the-art algorithms, like CSA and RAPTOR. We demonstrate a speedup of 1.9-4.2x over existing algorithms in the single-objective search (earliest arrival time). In the bicriteria setting, we achieve competitive speedup results but greater accuracy. We also find that our method scales much better as the delay buffer Delta increases.

翻译：近年来，诸如ULTRA等先进的多模式出行规划算法已被改进以处理延迟问题。本研究提出了一种更高效、更快速且更精确的改进方案，同时将该框架适配至CSA和RAPTOR等其他先进算法。实验表明，在单目标搜索（最早到达时间）中，本方法较现有算法实现了1.9-4.2倍的加速；在双目标场景下，虽加速效果与现有算法相当，但显著提升了准确性。此外，我们发现随着延迟缓冲区Delta增大，本方法展现出更优的可扩展性。