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Innovative Strategies for Sustainable Urban Rail Transit
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Innovative Strategies for Sustainable Urban Rail Transit

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

Deadline for manuscript submissions: 25 March 2026 | Viewed by 1007

Special Issue Editor

Dr. Haodong Yin

E-Mail Website
Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: urban rail transit network modeling; simulation optimization; passenger flow forcasting; operation management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on innovative strategies that can enhance the sustainability of urban rail transit. It has broad coverage, including, but not restricted to, the following:

(1) Innovative organizational models and technologies for passenger–freight mixed transportation. This aims to boost sustainability through resource optimization and new revenue-generating methods.

(2) Intelligent dispatching, including passenger flow and train operation rescheduling under disruption. By leveraging advanced algorithms and real-time data, it seeks to enhance dispatching efficiency and safety.

(3) Energy-efficient train operation. Through methods such as optimized speed control and regenerative braking, it promotes the environmental sustainability of urban rail transit.

(4) Other contributions regarding any emerging and relevant innovative strategies and technologies in the field of urban rail sustainability are encouraged.

The purpose is to gather cutting-edge research that provides practical solutions for making urban rail transit more sustainable, considering environmental, social, and economic aspects.​

I hope this Special Issue will contribute to providing new methods for measuring and monitoring the environmental and economic impacts of innovative strategies. By incorporating a diverse range of innovative elements related to urban rail sustainability, this Special Issue will enrich the body of knowledge in this crucial field.

Dr. Haodong Yin
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

  • urban rail transit
  • sustainability
  • passenger–freight mixed transportation
  • intelligent dispatching
  • energy-efficient train operation

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Published Papers (2 papers)

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Research

21 pages, 2777 KB  
Article
Identifying the Passenger Transport Corridors in an Urban Rail Transit Network Based on OD Clustering
by Fangyi Zhou, Jing Yao and Haodong Yin
Sustainability 2025, 17(20), 9127; https://doi.org/10.3390/su17209127 - 15 Oct 2025
Viewed by 112
Abstract
Traditional passenger transport corridor identification methods fail to effectively capture the spatiotemporal dynamic characteristics of passenger flows in complex urban rail transit networks. This study proposes a novel passenger transport corridor identification method based on Origin–Destination (OD) clustering. The method enables more accurate [...] Read more.
Traditional passenger transport corridor identification methods fail to effectively capture the spatiotemporal dynamic characteristics of passenger flows in complex urban rail transit networks. This study proposes a novel passenger transport corridor identification method based on Origin–Destination (OD) clustering. The method enables more accurate identification of passenger groups with similar travel patterns and distributions through a customized clustering similarity function; simultaneously, it can obtain OD pairs with actual physical significance through OD clustering as the source of basic units for identifying passenger transport corridors. By analyzing the spatial distribution of passenger transport corridor constituent units (clustered ODs), the method determines whether the passenger transport corridor is a cross-line corridor. The method is validated using Beijing’s urban rail transit system as a case study, employing the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm with optimal parameters (eps = 0.46, minpts = 980), identifying 21 clusters and ultimately determining six passenger transport corridors, including four cross-line and two non-cross-line types. Furthermore, this study conducted sensitivity analysis on the eps parameter using 80 test configurations to examine its impact on clustering effectiveness metrics, validating the method’s stability. The results demonstrate that the identified corridors exhibit high passenger flow concentration characteristics and accurately reflect passengers’ transfer demands between different lines. This research provides a theoretical foundation for integrated public transportation connectivity and supports sustainable urban development through improved operational efficiency and reduced operational costs. Full article
(This article belongs to the Special Issue Innovative Strategies for Sustainable Urban Rail Transit)
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22 pages, 3776 KB  
Article
Passenger-Centric Integrated Timetable Rescheduling for High-Speed Railways Under Multiple Disruptions
by Letian Fan, Ke Qiao, Yongsheng Chen, Meiling Hui, Tiqiang Shen and Pengcheng Wen
Sustainability 2025, 17(12), 5624; https://doi.org/10.3390/su17125624 - 18 Jun 2025
Viewed by 646
Abstract
In high-speed railway networks, multiple spatiotemporal correlated disruptions often cause passenger trip failures and delay propagation. Conventional single-disruption rescheduling strategies struggle to resolve such cross-line conflicts, necessitating an integrated, passenger-centric rescheduling framework for multiple correlated disruptions. This paper proposes a mixed-integer linear programming [...] Read more.
In high-speed railway networks, multiple spatiotemporal correlated disruptions often cause passenger trip failures and delay propagation. Conventional single-disruption rescheduling strategies struggle to resolve such cross-line conflicts, necessitating an integrated, passenger-centric rescheduling framework for multiple correlated disruptions. This paper proposes a mixed-integer linear programming (MILP) model to minimize total passenger delay time and trip failures under scenarios involving disruptions that are geographically dispersed but operationally interconnected. Two rescheduling mechanisms are introduced: a stepwise rescheduling method, which iteratively applies single-disruption models to optimize local problems, and an integrated rescheduling method, which simultaneously considers the global impact of all disruptions. Case studies on a real-world China’s high-speed railway network (29 stations, 42 trains, and 36,193 passenger trips) demonstrate that the proposed integrated rescheduling method reduces total passenger delays by 13% and trip failures by 67% within a 300 s computational threshold. By systematically coordinating spatiotemporal interdependencies among disruptions, this approach enhances network accessibility and service quality while ensuring operational safety, providing theoretical foundations for intelligent railway rescheduling. Full article
(This article belongs to the Special Issue Innovative Strategies for Sustainable Urban Rail Transit)
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