# Trip-Based Public Transit Routing Using Condensed Search Trees

arXiv: https://arxiv.org/abs/1607.01299 · HTML: https://mlsanity.com/paper/1607.01299/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2016.

## Built on by (most cited first, 3 of 3)

- [Adapting Dijkstra for Buffers and Unlimited Transfers](https://mlsanity.com/paper/2603.11729/) (2026)
- [Do Math Reasoning LLMs Help Predict the Impact of Public Transit Events?](https://mlsanity.com/paper/2511.00808/) (2025)
- [Early Pruning for Public Transport Routing](https://mlsanity.com/paper/2603.12592/) (2026)

## Abstract (arXiv)

We study the problem of planning Pareto-optimal journeys in public transit networks. Most existing algorithms and speed-up techniques work by computing subjourneys to intermediary stops until the destination is reached. In contrast, the trip-based model focuses on trips and transfers between them, constructing journeys as a sequence of trips. In this paper, we develop a speed-up technique for this model inspired by principles behind existing state-of-the-art speed-up techniques, Transfer Pattern and Hub Labelling. The resulting algorithm allows us to compute Pareto-optimal (with respect to arrival time and number of transfers) 24-hour profiles on very large real-world networks in less than half a millisecond. Compared to the current state of the art for bicriteria queries on public transit networks, this is up to two orders of magnitude faster, while increasing preprocessing overhead by at most one order of magnitude.