Data-Driven Rail Transit Operation, Timetable Scheduling, and Dispatching

Dear Colleagues,

Rail transit has developed rapidly all over the world. Actual train operation performance, which is the main manifestation of railway transportation production performance, can be documented and saved effectively. If legislation regarding train operations can be extracted based on operation performance and if the train operation process model is established, train dispatchers can predict and estimate developing trends related to train delays and make relevant dispatch decisions based on the degree to which a train may be delayed. Thus, a more accurate operational adjustment plan and the implementation of predictive scheduling can be expected.

Based on global data regarding train operations and considering the mutual influence of adjacent trains, it would be beneficial to analyze the interactive relationship between trains and the comprehensive formulation of decisions and schedules. The benefits from the development of big data technology, artificial intelligence, and data-driven methods include advantages in theoretical research and operational practices in many fields. Under the conditions of sufficient data and a permitted method, the data-driven model enables the examination of the more complicated process involving trains and an analysis of the delay propagation and recovery process. The data-driven approach does not require a priori knowledge; rather, it facilitates the discovery of laws from the data and construction of models to approximate real-world rail transport production. Although there may be some deviation between the data-driven model and the real situation, it is sufficient for guiding practices and overcoming the problem inherent in existing mathematical models. Data science provides a new solution to the problem of train operation, timetable scheduling, and dispatching. The trend in using data-driven methods to study train operation adjustments and automation will provide effective support for train dispatching decisions.

The applications of big data in railway operations, maintenance, and safety have attracted the attention of researchers and practitioners. Data-driven train dispatching has aroused huge interest in the railway industry considering using AI and data science. However, there is still lots of work needed in the future. In this Special Issue, original research articles and reviews are welcome, research areas may include, but are not limited to the following:

1. Train operation visualization;
2. Train timetable scheduling;
3. Train timetable rescheduling;
4. Train operation adjustment;
5. Data-driven rail transit capacity utilization optimization;
6. Data-driven train operation conflicts detection and resolution;
7. Data-driven delay propagation models;
8. Data-driven rail transit maintenance;
9. Data-driven train dispatching decision-making;
10. Intelligent rail transit dispatching system;

We look forward to receiving your contributions.

Dr. Chao Wen
Prof. Dr. Xinyue Xu
Dr. Pengling Wang
Dr. Wenxin Li
Prof. Dr. Shuguang Zhan
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 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.

• data-driven
• train timetable
• train operation adjustment
• train delays
• railway maintenance
• dispatching decision-making
• intelligent train dispatching