Fast and Memory Efficient Multimodal Journey Planning with Delays

TL;DR

This paper introduces a more memory-efficient, faster, and accurate multimodal journey planning algorithm that extends existing methods like ULTRA, CSA, and RAPTOR, demonstrating significant speedups and better scalability.

Contribution

The work extends state-of-the-art multimodal journey planning algorithms to be more memory-efficient, faster, and more accurate, with improved scalability for larger delay buffers.

Findings

Achieves 1.9-4.2x speedup over existing algorithms in single-objective search.

Demonstrates greater accuracy in bicriteria settings.

Scales better as the delay buffer increases.

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 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.