T-REX: Fast and Dynamic Journey Planning for Continental-Scale Public Transit Networks

TL;DR

T-REX is a novel, highly efficient algorithm for real-time journey planning in large-scale public transit networks, significantly outperforming previous methods in speed and adaptability.

Contribution

The paper introduces T-REX, a new multi-level overlay algorithm that enables fast, memory-efficient, and real-time journey planning across continental-scale transit networks.

Findings

Queries answered in less than 10ms on European networks

20x speedup over Trip-Based Public Transit Routing (TB)

Preprocessing takes only two minutes with quick schedule updates

Abstract

We present T-REX (Transfer-Ranked EXploration), a new algorithm for journey planning in public transit networks on the country and continental scale. Our algorithm applies the principles of multi-level overlays to Trip-Based Public Transit Routing (TB). Using a multi-level partition of the network, T-REX identifies transfers between trips that are relevant for long-distance travel in a short precomputation phase. This information is then used to prune irrelevant local transfers during a query. Like other state-of-the-art algorithms, T-REX Pareto-optimizes arrival time and the number of used trips. T-REX dramatically outperforms previous overlay-based algorithms for three key reasons: (1) a better partition, (2) reducing the search space by focusing on transfers rather than trips, and (3) a redesigned query algorithm with improved memory efficiency and throughput. As a result, T-REX answers queries in less than 10ms on a network of Europe, including local and long-distance transit. This constitutes a speedup of 20 compared to TB and 80 compared to algorithms without preprocessing. The memory footprint is moderate and the precomputation takes only two minutes, while real-time schedule updates can be incorporated in a few seconds. These properties make T-REX the first public transit journey planning algorithm that fulfills the requirements of interactive real-time applications on the continental scale.