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Application of Big Data in Energy-Efficient Management of Rail Systems

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 10271

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Special Issue Editors

Prof. Dr. Xin Yang

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Guest Editor

State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
Interests: urban rail transit; rail operations optimization; transportation network

Dr. Songpo Yang

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Guest Editor

College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Interests: intelligent transportation; railway scheduling; energy management; heuristics

Dr. Xiaoming Xu

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Guest Editor

School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Interests: urban rail transit; rail operations optimization; network flow model

Special Issue Information

Dear Colleagues,

We are pleased to launch a new Special Issue focusing on recent developments in the field of application data in rail systems in terms of its relationship to energy efficiency.

Advanced rail system plays an increasingly important role for passenger mobility both in intercity communication and urban commuting. For the design of a train control system, the energy efficiency should be borne in mind.

In this respect, introducing eco-driving strategies or energy-saving infrastructures have been promoted for trains running safely and efficiently. The traditional mathematical modeling approach, where the train trajectory and device usage are the results of a theoretical analysis with highly idealized assumptions, has deviated far from its application in actual life. With the application of data mining and algorithms, the mathematical models and computer simulations in rail system could correct parameters, and further verify applicability.

In view of the above concerns, the aim of the Special Issue is to collect the most promising approaches of modeling newly introduced energy-efficient operation in rail system with big data technology supplements. We want to show the complexity of the analysis and present how to solve the problem associated with the application of big data based on highly advanced technologies. The Special Issue will be focused on modeling techniques, quantitative analysis and advanced solution algorithms, resulting in the development of this research area.

Potential topics include but are not limited to the following:

Energy analysis in rail systems;
Energy-efficient scheduling;
Energy-efficient timetable optimization;
Energy-efficient speed profile optimization;
Energy-efficient train control;
Simulations of the power supply system;
Carbon emission evaluation or reduction;
Data analysis on energy-efficient management;
Decision making on energy-efficient management;

Other topics relevant to big data and machine learning in rail transit systems.

Prof. Dr. Xin Yang
Dr. Songpo Yang
Dr. Xiaoming 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. 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
rail operations optimization
transportation network
intelligent transportation
railway scheduling
energy management
network flow model

Published Papers (6 papers)

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Research

20 pages, 2439 KiB

Open AccessArticle

A Meta-Learning-Based Train Dynamic Modeling Method for Accurately Predicting Speed and Position

by Ying Cao, Xi Wang, Li Zhu, Hongwei Wang and Xiaoning Wang

Sustainability 2023, 15(11), 8731; https://doi.org/10.3390/su15118731 - 29 May 2023

Viewed by 992

Abstract

The train dynamics modeling problem is a challenging task due to the complex dynamic characteristics and complicated operating environment. The flexible formations, the heavy carriage load, and the nonlinear feature of air braking further increase the difficulty of modeling the dynamics of heavy haul trains. In this study, a novel data-driven train dynamics modeling method is designed by combining the attention mechanism (AM) with the gated recursive unit (GRU) neural network. The proposed learning network consists of the coding, decoding, attention, and context layers to capture the relationship between the train states with the control command, the line condition, and other influencing factors. To solve the data insufficiency problem for new types of heavy haul trains to be deployed, the model agnostic meta-learning (MAML) framework is adopted to achieve knowledge transferring from tasks supported by large amounts of field data to data-insufficient tasks. Effective knowledge transfer can enhance the efficiency of data resource utilization, reduce data requirements, and lower computational costs, demonstrating considerable potential in the application of sustainable development. The simulation results validate the effectiveness of the proposed MAML-based method in enhancing accuracy. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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21 pages, 8172 KiB

Open AccessArticle

Travel-Energy-Based Timetable Optimization in Urban Subway Systems

by Jian Li, Lu Zhang, Bu Liu, Ningning Shi, Liang Li and Haodong Yin

Sustainability 2023, 15(3), 1930; https://doi.org/10.3390/su15031930 - 19 Jan 2023

Cited by 1 | Viewed by 869

Abstract

Timetable optimization for urban subways is aimed at improving the transportation service. In congested subway systems, the effects of crowding at stations and inside the vehicles have not been properly addressed in timetabling. Moreover, it is difficult to show the time of values in different riding conditions. In this paper, we consider the passenger-travel process as a physical activity expending energy and formulate a travel energy expenditure function for a heavily congested urban subway corridor. A timetable optimization model is proposed to minimize the total energy expenditure, including waiting on the platform and travelling in the vehicle. We develop a heuristic generic algorithm to solve the optimization problem through a special binary coding method. The model is applied to the Yi-zhuang line in the Beijing subway system to obtain a passenger-oriented energy-minimizing timetable. Compared with using the existing timetable, we find a 20% reduction in average energy expenditure per passenger and a RMB 47,500 increase in social profits as the result of the timetable optimization. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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14 pages, 16633 KiB

Open AccessArticle

Automatic Obstacle Detection Method for the Train Based on Deep Learning

by Qiang Zhang, Fei Yan, Weina Song, Rui Wang and Gen Li

Sustainability 2023, 15(2), 1184; https://doi.org/10.3390/su15021184 - 09 Jan 2023

Cited by 6 | Viewed by 2955

Abstract

Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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19 pages, 1670 KiB

Open AccessArticle

A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers

by Fei Peng, Xian Fan, Puxin Wang and Mingan Sheng

Sustainability 2022, 14(21), 14431; https://doi.org/10.3390/su142114431 - 03 Nov 2022

Cited by 1 | Viewed by 1068

Abstract

The driver scheduling problem at Chinese electric multiple-unit train depots becomes more and more difficult in practice and is studied in very little research. This paper focuses on defining, modeling, and solving the depot driver scheduling problem which can determine driver size and driver schedule simultaneously. To solve this problem, we first construct a time-space network based on which we formulate the problem as a minimum-cost multi-commodity network flow problem. We then develop a Lagrangian relaxation heuristic to solve this network flow problem, where the upper bound heuristic is a two-phase method consisting of a greedy heuristic and a local search method. We conduct a computational study to test the effectiveness of our Lagrangian relaxation heuristic. The computational results also report the significance of the ratio of driver size to task size in the depot. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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13 pages, 2717 KiB

Open AccessArticle

An Optimization Method of Urban Rail Train Operation Scheme Based on the Control of Load Factor

by Fei Dou, Huiru Zhang, Haodong Yin, Yun Wei and Yao Ning

Sustainability 2022, 14(21), 14235; https://doi.org/10.3390/su142114235 - 31 Oct 2022

Cited by 4 | Viewed by 1512

Abstract

The train operation scheme of urban rail transit is a transportation plan formulated to fully meet the needs of passenger travel under the constraints of signal system capacity, turn-back capacity, and so on. Facing an unexpected epidemic, it was particularly important for passengers to travel safely and in an orderly manner. With an ever-increasing passenger flow due to work resumption, this paper proposes an optimization method for the urban rail train operation scheme based on the control of the target load factor according to the preparation process of the train operation scheme. The proposed method obtained the optimal train running interval and routing scheme based on analyzing the spatiotemporal distribution of passenger flow. The north section of Beijing Subway Line 8 was taken as an example. After optimization, for trains in the morning peak hour in the downward direction, the maximum load factor for the collinear section of the full-length routing and short-turn routing was reduced by 21%, and the matching effect of the transportation capacity and volume in the non-collinear was improved. In general, the maximum load factor in the downward direction after optimization was 80%, which met the target control requirements. The results show that the optimization method plays an important role in balancing the load factor in each cross-section and realizing the optimal coupling of passenger flow and train flow. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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11 pages, 363 KiB

Open AccessArticle

Evaluating the Safety Control Scheme of Railway Centralized Traffic Control (CTC) System with Coloured Petri Nets

by Tao Zhang, Xieting Li, Daohua Wu, Hongwei Wang, Jintao Liu and Dalin Zhang

Sustainability 2022, 14(18), 11669; https://doi.org/10.3390/su141811669 - 16 Sep 2022

Cited by 1 | Viewed by 1542

Abstract

The Centralized Traffic Control (CTC) system plays an important role in ensuring safe and efficient rail transportation operations. It is mainly responsible for the implementation and adjustment of the train operation schedule through the automatic control of the station signalling equipment. The major task of the CTC system is to achieve a high rail transportation operation efficiency under the precondition of safety. For this purpose, it is necessary to select appropriate safety control schemes for the CTC system. In this paper, a formal approach is proposed to quantitatively evaluate the operation efficiencies of the CTC system with respect to different safety control schemes. The proposed approach adopts stochastic coloured Petri nets as the means of description for the system model, and evaluates the operation efficiency of the CTC system based on the data collected during the simulation of the system model. To exemplify the proposed approach, the safety control scheme of prohibiting a passenger train from passing a freight train through adjacent rail tracks between two adjacent stations is studied. The results of the case study show the feasibility of the proposed approach. Full article

(This article belongs to the Special Issue Application of Big Data in Energy-Efficient Management of Rail Systems)

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