Early Pruning for Public Transport Routing

2026-03-12

AI Summary

This work addresses the computational inefficiency in multimodal public transit routing with unrestricted transfers, where dense transfer connections—such as walking or cycling—often force a trade-off between solution optimality and performance. To overcome this, the authors propose an Early Pruning method that enhances the RAPTOR algorithm by preprocessing transfer links sorted by duration and applying dynamic runtime pruning rules during the transfer relaxation phase to eliminate paths incapable of improving the current best arrival time. This approach incurs minimal overhead, preserves strict optimality, and seamlessly integrates with various RAPTOR variants. Empirical evaluations on real-world networks in Switzerland and London demonstrate up to a 57% reduction in query time, enabling efficient support for larger transfer radii and richer sets of travel modes.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Application Domains: Transportation

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
• Security and Privacy: Data transparency and provenance

Abstract

Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the transfer relaxation phase, especially on dense transfer graphs, when supporting unlimited transfers. This inefficiency arises from iterating over many potential inter-stop connections (walks, bikes, e-scooters, etc.). To maintain acceptable performance, practitioners often limit transfer distances or exclude certain transfer options, which can reduce path optimality and restrict the multimodal options presented to travellers. This paper introduces Early Pruning, a low-overhead technique that accelerates routing algorithms without compromising optimality. By pre-sorting transfer connections by duration and applying a pruning rule within the transfer loop, the method discards longer transfers at a stop once they cannot yield an earlier arrival than the current best solution. Early Pruning can be integrated with minimal changes to existing codebases and requires only a one-time preprocessing step. Across multiple state-of-the-art RAPTOR-based solutions, including RAPTOR, ULTRA-RAPTOR, McRAPTOR, BM-RAPTOR, ULTRA-McRAPTOR, and UBM-RAPTOR and tested on the Switzerland and London transit networks, we achieved query time reductions of up to 57%. This approach provides a generalizable improvement to the efficiency of transit pathfinding algorithms. Beyond algorithmic performance, Early Pruning has practical implications for transport planning. By reducing computational costs, it enables transit agencies to expand transfer radii and incorporate additional mobility modes into journey planners without requiring extra server infrastructure. This is particularly relevant for passengers in areas with sparse direct transit coverage, such as outer suburbs and smaller towns, where richer multimodal routing can reveal viable alternatives to private car use.

Problem

• public transport routing
• transfer relaxation
• performance bottleneck
• multimodal options
• path optimality

Innovation

• Early Pruning
• RAPTOR
• public transport routing
• transfer optimization
• multimodal journey planning