arXiv subjects

Patrick Steil

Publications and source records attributed to Patrick Steil.

5 recordsLinked to original sources

arXiv2026

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

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.

cs.SI↗

arXiv2025

Parallel PLL on DAGs

We present a parallel variant of Pruned Landmark Labelling (PLL) that is optimised for the preprocessing of hub labels on directed acyclic graphs (DAGs). This method was developed during a seminar at the Karlsruhe Institute of Technology (KIT), focusing on time-expanded graphs that model public transport networks. The approach leverages the topological properties of DAGs to enable a novel parallel construction of hub labels.

cs.DS↗

arXiv2023

FLASH-TB: Integrating Arc-Flags and Trip-Based Public Transit Routing

We present FLASH-TB, a journey planning algorithm for public transit networks that combines Trip-Based Public Transit Routing (TB) with the Arc-Flags speedup technique. The basic idea is simple: The network is partitioned into a configurable number of cells. For each cell and each possible transfer between two vehicles, the algorithm precomputes a flag that indicates whether the transfer is required to reach the cell. During a query, only flagged transfers are explored. Our algorithm improves upon previous attempts to apply Arc-Flags to public transit networks, which saw limited success due to conflicting rules for pruning the search space. We show that these rules can be reconciled while still producing correct results. Because the number of cells is configurable, FLASH-TB offers a tradeoff between query time and memory consumption. It is significantly more space-efficient than existing techniques with a comparable preprocessing time, which store generalized shortest-path trees: to match their query performance, it requires up to two orders of magnitude less memory. The fastest configuration of FLASH-TB achieves a speedup of more than two orders of magnitude over TB, offering sub-millisecond query times even on large countrywide networks.

cs.DS↗

arXiv2023

Optimal FIFO grouping in public transit networks

This technical report is about grouping vehicles in public transport into routes so that two vehicles of a route do not overtake each other. We say that such a set of routes satisfies the FIFO property. A natural question is: Given a set of trips, find a minimal FIFO grouping into routes. This question is especially interesting for route planning algorithms since a better route grouping leads to a better runtime.

cs.DS↗

arXiv2023

Arc-Flags Meet Trip-Based Public Transit Routing

We present Arc-Flag TB, a journey planning algorithm for public transit networks which combines Trip-Based Public Transit Routing (TB) with the Arc-Flags speedup technique. Compared to previous attempts to apply Arc-Flags to public transit networks, which saw limited success, our approach uses stronger pruning rules to reduce the search space. Our experiments show that Arc-Flag TB achieves a speedup of up to two orders of magnitude over TB, offering query times of less than a millisecond even on large countrywide networks. Compared to the state-of-the-art speedup technique Trip-Based Public Transit Routing Using Condensed Search Trees (TB-CST), our algorithm achieves similar query times but requires significantly less additional memory. Other state-of-the-art algorithms which achieve even faster query times, e.g., Public Transit Labeling, require enormous memory usage. In contrast, Arc-Flag TB offers a tradeoff between query performance and memory usage due to the fact that the number of regions in the network partition required by our algorithm is a configurable parameter. We also identify an issue in the transfer precomputation of TB that affects both TB-CST and Arc-Flag TB, leading to incorrect answers for some queries. This has not been previously recognized by the author of TB-CST. We provide discussion on how to resolve this issue in the future. Currently, Arc-Flag TB answers 1-6% of queries incorrectly, compared to over 20% for TB-CST on some networks.

cs.DS↗