# Adapting Dijkstra for Buffers and Unlimited Transfers

arXiv: https://arxiv.org/abs/2603.11729 · HTML: https://mlsanity.com/paper/2603.11729/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Connection Scan Algorithm](https://mlsanity.com/paper/1703.05997/) (2017)
- [Trip-Based Public Transit Routing](https://mlsanity.com/paper/1504.07149/) (2015)
- [Trip-Based Public Transit Routing Using Condensed Search Trees](https://mlsanity.com/paper/1607.01299/) (2016)
- [UnLimited TRAnsfers for Multi-Modal Route Planning: An Efficient Solution](https://mlsanity.com/paper/1906.04832/) (2019)
- [Fast Multimodal Journey Planning for Three Criteria](https://mlsanity.com/paper/2110.12954/) (2021)
- [Timetable Nodes for Public Transport Network](https://mlsanity.com/paper/2410.15715/) (2024)
- [Fast and Delay-Robust Multimodal Journey Planning](https://mlsanity.com/paper/2310.20554/) (2023)

## Built on by (most cited first, 1 of 1)

- [Early Pruning for Public Transport Routing](https://mlsanity.com/paper/2603.12592/) (2026)

## Abstract (arXiv)

In recent years, RAPTOR based algorithms have been considered the state-of-the-art for path-finding with unlimited transfers without preprocessing. However, this status largely stems from the evolution of routing research, where Dijkstra-based solutions were superseded by timetable-based algorithms without a systematic comparison. In this work, we revisit classical Dijkstra-based approaches for public transit routing with unlimited transfers and demonstrate that Time-Dependent Dijkstra (TD-Dijkstra) outperforms MR. However, efficient TD-Dijkstra implementations rely on filtering dominated connections during preprocessing, which assumes passengers can always switch to a faster connection. We show that this filtering is unsound when stops have buffer times, as it cannot distinguish between seated passengers who may continue without waiting and transferring passengers who must respect the buffer. To address this limitation, we introduce Transfer Aware Dijkstra (TAD), a modification that scans entire trip sequences rather than individual edges, correctly handling buffer times while maintaining performance advantages over MR. Our experiments on the London and Switzerland networks show that we can achieve more than a twofold speedup over MR while producing optimal results on both networks, with and without buffer times.