Virtual Coupling as an operational concept for Dutch mainline railway corridors

Master Thesis (2024)
Authors

J. Plasmeijer (TU Delft - Civil Engineering & Geosciences)

Supervisors

Rob Goverde (TU Delft - Transport, Mobility and Logistics)

Faculty
Civil Engineering & Geosciences
More Info
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Publication Year
2024
Language
English
Graduation Date
05-12-2024
Awarding Institution
Delft University of Technology
Programme
Transport, Infrastructure and Logistics
Faculty
Civil Engineering & Geosciences
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Abstract

Virtual Coupling (VC) has the potential to significantly enhance railway infrastructure capacity and enable more flexible scheduling by introducing vehicle-to-vehicle (V2V) communication between consecutive trains. Inspired by platooning implementations in truck and bus platooning, VC could reduce operational costs and improve passenger satisfaction. However, its effective implementation in real-time railway operations remains uncertain, particularly regarding its impact on passengers, infrastructure managers, and railway undertakings. This research explores how VC can be implemented as an operational concept for mainline railway services, leveraging its potential to enhance flexibility and frequency to benefit passengers, railway operators, and infrastructure managers., identifying four operational concepts based on similar transport applications. Four operational concepts were identified, inspired by similar implementations in other transport modes. A SWOT analysis was conducted to qualitatively select the most promising options for further evaluation. For these concepts, running times and headways were calculated and tested within a real-world case study, focusing on key performance metrics such as generalized travel times, capacity utilization, and maximum frequency. The analysis revealed that a skip-stop pattern with synchronized arrivals at major stations is the most effective concept, facilitating efficient transfers and enhancing operational flexibility. Key recommendations include integrating real passenger demand data to better assess the absolute impact of operational variations and refining strategies for effective train coupling to advance VC implementation.

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