Machine Learning and Optimization Methodologies in Railway Transportation
Railway optimization problems are inherently complex due to various factors, such as safety requirements, capacity limitations, and large-scale problem sizes. This complexity makes precise modeling and obtaining accurate solutions highly challenging, often necessitating a trade-off between computational performance and solution quality. In recent years, the rapid development of Machine Learning (ML) has driven innovative research in railway transportation, bringing many advantages and solutions. The application of ML enables in-depth analysis and exploration of the massive data generated within railway systems, uncovering hidden patterns and trends. Moreover, ML can also overcome the limitations of traditional methods. For example, when combined with optimization methodologies, ML may optimize the decision-making process, provide an innovative and effective means to significantly enhance the accuracy, timeliness, and scientific nature of railway management decisions. Therefore, the comprehensive application of ML and optimization methodologies holds enormous potential for addressing challenges in railway transportation.
Guest editors:
Prof. Maged Dessouky
University of Southern California, Los Angeles, CA
maged@usc.edu
Prof. Lixing Yang
Beijing Jiaotong University, Beijing, China
lxyang@bjtu.edu.cn
Dr. Xiaoming Xu
Hefei University of Technology, Hefei, China
xmxu@hfut.edu.cn
Special issue information:
This Special Issue on “Machine Learning and Optimization Methodologies in Railway Transportation” aims to provide a unique platform for researchers and practitioners from academia and industry in railway transportation to share their latest developments, methods, and practices in using ML and optimization technologies to address challenges in railways. With a focus on the integration of ML and OR techniques into Railway Transportation, this SI will encompass a wide range of topics, including but not limited to:
Train scheduling, rescheduling
Train marshalling, shunting
Rolling stock scheduling
Line planning
Railway maintenance planning
Railway infrastructure management
Railway construction project scheduling
Railway traffic simulation
Crew scheduling, rostering
Intelligent railway transportation systems
Green and low-carbon railway transportation
Passenger demand prediction in railways
Big data analytics in transportation
Traffic safety and risk management
Manuscript submission information:
The Journal’s submission system is open for submissions to our Special Issue. When submitting your manuscript please select the article type “VSI:Methodologies in Railway Transportation” so that the article will be considered for the special issue. Please submit your manuscript by 31st Dec 2025.
The submission link is: https://www.editorialmanager.com/caie/default.aspx
Please see an example here: https://www.sciencedirect.com/journal/computers-and-industrial-engineering
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and link to submit your manuscript is available on the Journal’s homepage at https://www.sciencedirect.com/journal/computers-and-industrial-engineering/publish/guide-for-authors
Keywords:
Machine Learning; Optimization Methodology; Transportation; Railways
Computers & Industrial Engineering的CAR指数
2023年3月份科睿唯安官方一次性踢除35本SCI期刊，多数涉及学术诚信问题，让我们意识到学术期刊的“被踢”指数，也很重要。目前，对于期刊的“被踢”指数，这里介绍一下：CAR指数（关于CAR的详细介绍，请关注：www.jcarindex.com），这是一种评价期刊学术诚信风险的指数，指数越高代表可能的风险越大。从数据看，Computers & Industrial Engineering不管是2022年度，还是2023年度的CAR指数，都是比较低的。当然，CAR指数仅供参考，期刊风险情况，需以科睿唯安或中科院预警等官方为准！
让推送更美好~
