Dynamic Replanning for Improved Public Transport Routing

TL;DR

This paper introduces a formal framework for dynamic replanning in public transport routing, leveraging real-time delay data to improve journey adjustments and reduce travel times through proactive server monitoring.

Contribution

It proposes a novel 'push' approach for system-scale dynamic replanning, outperforming manual 'pull' methods and enhancing public transport efficiency.

Findings

Push approach achieves significant speedups.

Dynamic replanning enables substantial arrival time savings.

Proactive adjustments outperform manual requests.

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.