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Eye on AI Weekly Research Watch

A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

3 min15 juni 2026
Railway networks are extraordinarily complex — trains of different gauges share limited track, single-track sections require precise coordination, and unexpected disruptions cascade through entire timetables. Most optimization research stops at high-level scheduling, leaving the messy operational details — track switching, gauge compatibility, disruption response — to human operators under pressure. This framework models the entire problem using PDDL 2.1 temporal planning, generating timestamped, conflict-free operational plans that account for gauge constraints and stochastic disruptions like blocked tracks or engine failures. Tested on 200 benchmark instances with up to 1,000 track points and 120 trains, it demonstrates practical viability for real-world railway systems seeking to reduce reliance on manual intervention during disruptions. Authors: Pollob Chandra Ray, Sabah Binte Noor, Fazlul Hasan Siddiqui Paper: https://arxiv.org/abs/2606.14582v1

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