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Applied Sciences
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Trends and Prospects in Urban Rail Transit
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Trends and Prospects in Urban Rail Transit

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 3969

Special Issue Editors

Dr. Haodong Yin

E-Mail Website
Guest Editor
State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Interests: urban rail transit network modeling; simulation optimization; passenger flow forecasting; operation management
Dr. Yan Xu

E-Mail Website
Guest Editor
Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
Interests: train operation optimization; simulation; passenger flow control
Dr. Guangming Xu

E-Mail Website
Guest Editor
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Interests: urban rail transit network modeling; railway renvenue management; railway line planning; passenger travel choice behavior

Special Issue Information

Dear Colleagues,

Urban rail transit is essential to solving urban traffic congestion and pollution problems. In recent years, with the development of information technology, the urban rail transit industry has also faced rapid changes, and a large number of new technologies and methods have emerged, especially in planning and design, construction, services, policies and operation management and other related aspects. Therefore, this Special Issue aims to present a collection of original research articles and review papers regarding trends and prospects in urban rail transit. Topics of interest include, but are not limited to:

  • Smart urban rail transit;
  • Urban rail transit planning, control, and management;
  • Passenger flow analysis or forecasting under disruption;
  • Fully automatic operation systems;
  • Networked train scheduling or timetabling;
  • Demand-driven train rescheduling;
  • Smart train or station;
  • Simulation optimization of urban rail transit;
  • Intelligent perception technology in urban rail transit;
  • The application of BIM, digital twins, etc., in urban rail transit;
  • The application of big data and artificial intelligence in urban rail transit.

Dr. Haodong Yin
Dr. Yan Xu
Dr. Guangming Xu
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. Applied Sciences 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

  • smart urban rail transit
  • train scheduling, rescheduling
  • passenger flow
  • simulation optimization
  • artifial intelligence

Published Papers (3 papers)

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Research

27 pages, 2934 KiB  
Article
Train Rescheduling for Large Transfer Passenger Flow by Adding Cross-Line Backup Train in Urban Rail Transit
by Jianjun Yuan, Xiaoqun Zhao and Pengzi Chu
Appl. Sci. 2023, 13(20), 11228; https://doi.org/10.3390/app132011228 - 12 Oct 2023
Viewed by 803
Abstract
The cross-line operation mode, based on interoperability technology, is becoming increasingly common in urban rail transits (URTs). Compared to trains running on a single line, cross-line trains can greatly facilitate transfer passengers. Taking the scenario of emergent large transfer passenger flow as an [...] Read more.
The cross-line operation mode, based on interoperability technology, is becoming increasingly common in urban rail transits (URTs). Compared to trains running on a single line, cross-line trains can greatly facilitate transfer passengers. Taking the scenario of emergent large transfer passenger flow as an example, this paper explores the train rescheduling problem for serving transfer passengers by adding a cross-line backup train. To maximize the number of transfer passengers served by the cross-line backup train, a nonlinear optimization model is constructed by taking into account the operation parameters of planned trains on relevant lines, the deviation degree of the planned timetable, the utilization of the cross-line backup train, and the passenger flow calculation as constraints, and some linearization lemmas are proposed to transform it into a mixed integer programming (MIP) model with quadratic terms. A case study is conducted to discuss the impact of parameter changes on the objective function value and the applicability of different solution approaches. The results suggest that the operation trajectory of the cross-line backup train has an effect on the objective function value, which is related to the demand, the deviation tolerance of the planned timetable, and the running efficiency tolerance of the cross-line backup train. The corresponding methods help guide the organization of the cross-line backup train for large transfer passenger flow scenarios. Full article
(This article belongs to the Special Issue Trends and Prospects in Urban Rail Transit)
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16 pages, 794 KiB  
Article
Analysis of the Influence and Propagation Law of Urban Rail Transit Disruptions: A Case Study of Beijing Rail Transit
by Wenhan Zhou, Tongfei Li, Rui Ding, Jie Xiong, Yan Xu and Feiyang Wang
Appl. Sci. 2023, 13(14), 8040; https://doi.org/10.3390/app13148040 - 10 Jul 2023
Cited by 1 | Viewed by 832
Abstract
In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even [...] Read more.
In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers. Full article
(This article belongs to the Special Issue Trends and Prospects in Urban Rail Transit)
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19 pages, 8094 KiB  
Article
Resilience Assessment of Beijing Subway Lines under Extreme Precipitation Weather
by Yun Wei, Jingyu Liang, Yongxin Deng, Fei Dou, Yao Ning, Dong Zhou and Jie Liu
Appl. Sci. 2023, 13(6), 3964; https://doi.org/10.3390/app13063964 - 21 Mar 2023
Cited by 4 | Viewed by 1647
Abstract
Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an [...] Read more.
Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an urgent issue in the construction of subway systems to fully prepare for extreme weather and improve system resilience under external disturbances. The resilience of a complex system generally refers to its ability to adapt to external disturbances and return to a functional state. As one of several key infrastructure systems in large cities, a subway system needs to be highly resilient to cope with various risks, and it needs to recover quickly under uncertain weather conditions and other external damage events. In order to achieve the goal of conducting a real-time resilience assessment of a subway system, this study adopts the Bayesian network and the traditional failure mode and effect analysis (FMEA) method to realize resilience assessment with multiple performance indicators. Combined with the risk matrix method from FMEA, multiple important indicators of a subway system under the influence of extreme weather are obtained. These important indicators are integrated into the resilience assessment of the subway system within a Bayesian method. In this paper, the feasibility and applicability of the proposed method are verified by taking the Changping Line of the Beijing subway under extreme rainfall weather (>10 mm) as a case. Full article
(This article belongs to the Special Issue Trends and Prospects in Urban Rail Transit)
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