Fast and Memory Efficient Multimodal Journey Planning with Delays

2026-04-17

AI Summary

This work addresses the limitations of existing delay-aware multimodal route planning algorithms, which often compromise among memory efficiency, computational speed, and solution accuracy. By introducing memory-efficient data structures and a novel delay propagation mechanism, the authors optimize the search strategy to achieve, for the first time, high-speed, low-memory, and highly accurate delay-sensitive routing within mainstream frameworks such as ULTRA, CSA, and RAPTOR. Experimental results demonstrate that the proposed approach accelerates single-destination queries by 1.9–4.2× while significantly improving accuracy in multi-destination scenarios. Furthermore, the method maintains strong scalability even under large-scale delay conditions, offering a robust solution for real-world multimodal transit systems subject to disruptions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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-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.

Problem

• multimodal journey planning
• delays
• memory efficiency
• computational speed
• accuracy

Innovation

• multimodal journey planning
• delay handling
• memory efficiency
• algorithm acceleration
• scalability