Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Future-Proofing Study in Sustainable Railway Transportation Systems
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Future-Proofing Study in Sustainable Railway Transportation Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 3218

Special Issue Editor

Dr. Marco Guerrieri

E-Mail Website
Guest Editor
Department of Civil, Environmental and Mechanical Engineering (DICAM), University of Trento, Via Mesiano 77, 3812 Trento, Italy
Interests: traffic engineering; highway engineering; railway engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, innovative technology systems have been proposed, aiming to create faster and more efficient transportation systems. In railway engineering, these systems include, for example, the Transrapid, the Hyperloop and the HeliRail, which provide several potential benefits over traditional railway systems.

Around the world, remarkable advances have also been made in urban railway transportation systems and related policies, guidelines, and technical regulations.

This Special Issue aims to present a collection of original research articles and review papers on urban railway transportation systems.

Topics of interest include, but are not limited to:

  • Innovative railway systems;
  • Advanced driver-assistance systems;
  • Artificial intelligence applications in railway engineering;
  • Urban rail transit planning, control, and management;
  • Modern tramway systems;
  • Light rails;
  • Fully automated metros;
  • Intelligent and automated transport system technology;
  • Traffic flow theory and simulation;
  • User safety;
  • Vehicle–track system dynamics;
  • Maintenance of railway tracks;
  • Electrification;
  • Vehicle engineering;
  • Environmental impacts (vibration, noise, pollution, etc.);
  • Structural monitoring;
  • Cost–benefit analysis (CBA) of railway transportation systems.

Prof. Dr. Marco Guerrieri
Guest Editor

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 submissions that pass pre-check are 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 2400 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

  • railway engineering
  • innovative railway transportation systems
  • intelligent and automated transport

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 2508 KiB  
Article
Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks
by Chengguang Liu, Jiaqi Zhang, Xixi Luo, Yulin Yang and Chao Hu
Sustainability 2023, 15(12), 9652; https://doi.org/10.3390/su15129652 - 16 Jun 2023
Cited by 1 | Viewed by 1501
Abstract
The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding [...] Read more.
The construction of high-speed rail lines in China has drastically improved the freight capacity of conventional railways. However, due to recent national energy policy adjustments, rail freight volumes, consisting mostly of coal, ore, and other minerals, have declined. As a result, the corresponding changes in the supply and demand of goods and transportation have led to a gradual transformation of the railway freight market from a seller’s market to a buyer’s market. It is important to carry out a systematic analysis and a precise forecast of the demand for rail freight transport. However, traditional time series forecasting models often lack precision during drastic fluctuations in demand, while deep learning-based forecasting models may lack interpretability. This study combines grey relational analysis (GRA) and deep neural networks (DNN) to offer a more interpretable approach to predicting rail freight demand. GRA is used to obtain explanatory variables associated with railway freight demand, which improves the intelligibility of the DNN prediction. However, the high-dimension predictor variable can make training on DNN challenging. Inspired by deep autoencoders (DAE), we add a layer of an encoder to the GRA-DNN model to compress and aggregate the high-dimension input. Case studies conducted on Chinese railway freight from 2000 to 2018 show that the proven GRA-DAE-NN model is precise and easy to interpret. Comparative experiments with conventional prediction models ARIMA, SVR, FC-LSTM, DNN, FNN, and GRNN further validate the performance of the GRA-DAE-NN model. The prediction accuracy of the GRA-DAE-NN model is 97.79%, higher than that of other models. Among the main explanatory variables, coal, oil, grain production, railway locomotives, and vehicles have a significant impact on the railway freight demand trend. The ablation experiment verified that GRA has a significant effect on the selection of explanatory variables and on improving the accuracy of predictions. The method proposed in this study not only accurately predicts railway freight demand but also helps railway transportation companies to better understand the key factors influencing demand changes. Full article
(This article belongs to the Special Issue Future-Proofing Study in Sustainable Railway Transportation Systems)
►▼ Show Figures

Figure 1

20 pages, 3211 KiB  
Article
Daily Line Planning Optimization for High-Speed Railway Lines
by Jinfei Wu, Xinghua Shan, Jingxia Sun, Shengyuan Weng and Shuo Zhao
Sustainability 2023, 15(4), 3263; https://doi.org/10.3390/su15043263 - 10 Feb 2023
Cited by 1 | Viewed by 1092
Abstract
Daily line planning in the operation stage should satisfy the fluctuating travel demand on different days and ensure the operation stability. In this paper, we propose an approach of daily line planning optimization for high-speed railway (HSR) lines to trade off the system [...] Read more.
Daily line planning in the operation stage should satisfy the fluctuating travel demand on different days and ensure the operation stability. In this paper, we propose an approach of daily line planning optimization for high-speed railway (HSR) lines to trade off the system costs and operation stability. The line plan is optimized by adjusting the reference line plan based on the baseline plan. A bi-level programming model is constructed based on Stackelberg game theory to describe the interaction and conflicts between railway companies and passengers. We propose the thought of “trigger decision, space-time coupling and joint iteration” to solve the model under the framework of the Simulated Annealing Algorithm (SAA). The case study on the Beijing–Shanghai HSR Line demonstrates that the adjusted line plan can not only optimize the system costs but also ensure the operation stability. It can provide sufficient transit capacity to satisfy the travel requirements of passengers and present the obvious advantage of operation cost reduction. Full article
(This article belongs to the Special Issue Future-Proofing Study in Sustainable Railway Transportation Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop