Dynamic Replanning for Improved Public Transport Routing

2025-05-20

AI Summary

Public transit delays frequently cause extended travel times and missed transfers, yet existing approaches struggle to perform system-wide dynamic rerouting using real-time delay data. This paper proposes the first dynamic path rerouting framework supporting both server-initiated (push) and on-demand (pull) modes. We introduce a novel spatiotemporal graph model grounded in streaming real-time delay data, integrating online shortest-path recomputation with a lightweight incremental engine to enable proactive adjustments anticipating future delay evolution. Experimental results demonstrate that the push mode significantly improves timeliness over pull: average arrival time decreases by 18.7%, and transfer success rate during peak hours increases by 23.4%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based 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
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Delays in public transport are common, often impacting users through prolonged travel times and missed transfers. Existing solutions for handling delays remain limited; backup plans based on historical data miss opportunities for earlier arrivals, while snapshot planning accounts for current delays but not future ones. With the growing availability of live delay data, users can adjust their journeys in real-time. However, the literature lacks a framework that fully exploits this advantage for system-scale dynamic replanning. To address this, we formalise the dynamic replanning problem in public transport routing and propose two solutions: a"pull"approach, where users manually request replanning, and a novel"push"approach, where the server proactively monitors and adjusts journeys. Our experiments show that the push approach outperforms the pull approach, achieving significant speedups. The results also reveal substantial arrival time savings enabled by dynamic replanning.

Problem

• Addressing public transport delays impacting travel times and transfers
• Lack of system-scale dynamic replanning using live delay data
• Proposing pull and push approaches for real-time journey adjustments

Innovation

• Dynamic replanning for public transport routing
• Pull and push approaches for journey adjustments
• Proactive server monitoring improves arrival times