Special Issue "Sustainability and Digital Transformations in Railway Systems: Modelling, Planning, and Management"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Dr. Yang Song
E-Mail Website
Guest Editor
Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, 7491 Norway
Interests: railway pantograph-catenary interaction; the wind-induced vibration of long-span structures of railway transportation; the coupling dynamics in railway engineering
Dr. Hongrui Wang
E-Mail Website
Guest Editor
Department of Engineering Structures, Delft University of Technology, 2628CN Delft, The Netherlands
Interests: signal processing; artificial intelligence; applications of signal processing and artificial intelligence in the structural health monitoring and digital modelling and design of railway infrastructures
Dr. Yongqiu Zhu
E-Mail Website
Guest Editor
Institute for Transport Planning and Systems, ETH Zurich, 8093 Zurich, Switzerland
Interests: optimization, reinforcement learning, applications of optimization and reinforcement learning in railway traffic management; disruption management; passenger assignment; passenger-oriented decision making; passenger information provision

Special Issue Information

Dear Colleagues,

Rail has already demonstrated that it can satisfy massive transport demands as the cleanest and most sustainable transport mode. The first two decades of the 21st century have witnessed a substantial expansion of rail transport across the world. In the foreseeable future, rail will become the backbone of sustainable mobility in our society. However, the rail sector is still facing major challenges in fulfiling such a critical role. We must accelerate research and innovation in the rail sector to meet the ever-growing mobility demand, to become more flexible and attractive to passengers and goods, to interrelate with other transport modes for multimodal mobility, and to offer greener solutions in planning, management, and operation. This transformation should rely on emerging concepts and technologies including artificial intelligence, digital twins, automated/autonomous trains, renewable energies, and more.

In this context, we are pleased to announce this Special Issue with the general theme of “Sustainability and Digital Transformations in Railway Systems: Modelling, Planning, and Management”. Possible topics include, but are not limited to, the following:

  • Physical modelling and digitalization of railway systems
  • Digital twins and simulations of railway systems
  • Smart and sustainable asset management in rail transportation
  • Resilient line planning and timetabling
  • Smart structural health monitoring solutions
  • Predictive maintenance decision making
  • Railway traffic management and disruption management
  • Passenger-friendly information systems
  • Energy-efficient automated/autonomous train control
  • Renewable energies for trains
  • Emerging technologies for future railways

We invite researchers and experts to submit original research and review articles that can describe the state of the art and further stimulate innovations in the field.

Dr. Yang Song
Dr. Hongrui Wang
Dr. Yongqiu Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sustainable railway
  • Digital transformation
  • Renewable energy
  • Railway operation
  • Planning and (re)scheduling
  • Smart monitoring
  • Predictive maintenance
  • Artificial intelligence
  • Digital twins
  • Automated/autonomous trains

Published Papers (2 papers)

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Research

Article
Mechanism of Sleeper–Ballast Dynamic Impact and Residual Settlements Accumulation in Zones with Unsupported Sleepers
Sustainability 2021, 13(14), 7740; https://doi.org/10.3390/su13147740 - 11 Jul 2021
Viewed by 554
Abstract
Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities such as subgrade [...] Read more.
Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities such as subgrade damages. The reason for the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occurs due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted to studying the mechanism of the sleeper–ballast dynamic impact in the void zone. The results of experimental in situ measurements of rail deflections showed the significant impact accelerations in the zone even for lightweight slow vehicles. A simple three-beam numerical model of track and rolling stock interaction has shown dynamic interaction similar to the experimental measurements. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appears before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised by high ballast pre-stressed conditions. The analysis of void size influence demonstrates that the maximal impact loadings and maximal wheel and sleeper accelerations appear at a certain void depth, after which the values decrease. The ballast quasistatic loading analysis indicates an increase of more than 2 times in the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short-length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void is performed. Full article
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Article
Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model
Sustainability 2021, 13(8), 4379; https://doi.org/10.3390/su13084379 - 14 Apr 2021
Viewed by 347
Abstract
In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm [...] Read more.
In rail transit traction, due to the remarkable energy-saving and low-cost characteristics, synchronous reluctance motors (SynRM) may be a potential substitute for traditional AC motors. However, in the parameter extraction of SynRM nonlinear magnetic model, the accuracy and robustness of the metaheuristic algorithm is restricted by the excessive dependence on fitness evaluation. In this paper, a novel probability-driven smart collaborative performance (SCP) is defined to quantify the comprehensive contribution of candidate solution in current population. With the quantitative results of SCP as feedback in-formation, an algorithm updating mechanism with improved evolutionary quality is established. The allocation of computing resources induced by SCP achieves a good balance between exploration and exploitation. Comprehensive experiment results demonstrate better effectiveness of SCP-induced algorithms to the proposed synchronous reluctance machine magnetic model. Accuracy and robustness of the proposed algorithms are ranked first in the comparison result statistics with other well-known algorithms. Full article
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