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REPLACE - Infrastructure Investments for Optimal Rail REPLACEment Bus Services

Promote more efficient rail mobility via innovative AI-based methods, by enabling optimized planning of rail replacement services and cost-effective infrastructure investment decisions.

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Managing disruption in railways

When scheduled maintenance or unforeseen disruptions occur, the railway infrastructure managers and railway undertakings take steps to ensure that all passengers comfortably reach their destinations. Trains are rerouted and rescheduled, and buses is deployed to bridge passengers between disconnected locations. This requires snap judgement by experts with little to no decision support across different actors, leading to inefficient solutions. In turn, this drastically diminishes the attractiveness of the rail transport system. As an attractive public transport service, also in times of disruption, impacts the overall satisfaction of citizens on and off travel, it is essential to avoid congestion and provide quality service. Having a properly scaled and located replacement services requires continual, significant investments when determining which infrastructure investments are the most cost-effective for improving rail replacement services.

Photo: Mads Kristiansen

Mathematical models for optimal replacement

Making use of advanced mathematical optimization techniques, REPLACE aims to promote a more efficient replacement service by complementing practical and professional competence in areas such as infrastructure planning, disruption management, and timetabling. REPLACE will provide a comprehensive model for infrastructure investment decisions and rail replacement bus services, along with an optimization-based solution algorithm. The model is intended to be integrated in planning tools, allowing infrastructure managers to better plan the necessary enhancements needed for providing passengers with an effective, sustainable, and future-oriented replacement service.

Key facts

Project duration

2025 - 2028

Co-workers

Marie Lindland

Marie Lindland

Research Scientist/PhD Fellow
Bjørnar Luteberget

Bjørnar Luteberget

Research Scientist
Oddvar Kloster

Oddvar Kloster

Senior Software Developer
Giorgio Sartor

Giorgio Sartor

Senior Research Scientist

Research Group Optimization