Fast and Memory Efficient Multimodal Journey Planning with Delays

Abstract

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 x 1.9 -- 4.2 over existing algorithms in the single-criterion search. In the multicriteria setting, we achieve competitive speedup results but greater accurateness. We also found that our method scales much better as the delay increases.