Self-Organizing Railway Traffic Management

TL;DR

This paper introduces a decentralized self-organizing approach for railway traffic management, enabling trains to collaboratively optimize traffic flow and outperform centralized methods in simulations.

Contribution

The paper proposes a novel modular self-organization process for trains to collaboratively manage traffic, contrasting with traditional centralized decision-making.

Findings

Self-organization outperforms centralized algorithms in simulations.

Instance decomposition enhances traffic management effectiveness.

The approach is validated on a real-world Italian railway network.

Abstract

Improving traffic management in case of perturbation is one of the main challenges in today's railway research. The great majority of the existing literature proposes approaches to make centralized decisions to minimize delay propagation. In this paper, we propose a new paradigm to the same aim: we design and implement a modular process to allow trains to self-organize. This process consists in having trains identifying their neighbors, formulating traffic management hypotheses, checking their compatibility and selecting the best ones through a consensus mechanism. Finally, these hypotheses are merged into a directly applicable traffic plan. In a thorough experimental analysis on a portion of the Italian network, we compare the results of self-organization with those of a state-of-the-art centralized approach. In particular, we make this comparison mimicking a realistic deployment thanks to a closed-loop framework including a microscopic railway simulator. The results indicate that self-organization achieves better results than the centralized algorithm, specifically thanks to the definition and exploitation of the instance decomposition allowed by the proposed approach.