Literature Review Toward Decentralized Railway Traffic Management

Abstract

This paper analyses the literature to identify ideas which may be applied to decentralized real-time railway traffic management. This system represents a new way for dealing with railway traffic perturbations in absence of a central decision maker. Specifically, we are interested in identifying techniques that may constitute suitable automatic mechanisms for the emergence of an effective system behaviour. In this literature review, we discuss the possibility of exploiting the existing research works on other transport modes. The analysis of these works makes it clear that real-time railway traffic management is very peculiar. Hence, we consider different approaches: hierarchical self organization, task allocation, reinforcement learning, consensus, auction and coopetition techniques. Some promising possibilities emerge, which we analyse proposing ideas for modelling decentralized real-time railway traffic management.

A review of principles and methods to decompose large-scale railway scheduling problems

“…All problems which are at the same level in the decomposition can be coordinated in a direct exchange of information with each other (vertical communication). A recent, more extensive review on the topic of decentralized railway scheduling can be found in Marcelli and Pellegrini (2021). Such review includes also applications beyond railways, where decentralized solution methods have shown potential.…”

A logic-based Benders decomposition for microscopic railway timetable planning

“…Some examples of decentralized decomposition are Brännlund, Lindberg, Nõu, & Nilsson (1998) ; Corman, D'Ariano, Pacciarelli, & Pranzo (2010) ; Luan, De Schutter, Meng, & Corman (2020) . A recent review can be found in Marcelli & Pellegrini (2021) .…”

Are Smart Contracts and Blockchains Suitable for Decentralized Railway Control?

“…Non-DLT solutions for WORM data storage include dedicated hardware 20 and software/cloud 21 designs, modifications of existing file systems 22 , or data archival 23 . Some database products support replication and "hot standby" / "cold standby" modes 24 , usually with a cluster-based distribution.…”

“…There are different approaches to build railway IT infrastructure in a more networked/peer-to-peer way. 38 However, none of them covers M2M payments and train-to-train economy.…”