In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operati...In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.展开更多
During railway operations,trains normally run as scheduled,but the occurrence of unexpected events will disrupt traffic flow and cause train deviation from the original timetable.In order to assist dispatchers in resc...During railway operations,trains normally run as scheduled,but the occurrence of unexpected events will disrupt traffic flow and cause train deviation from the original timetable.In order to assist dispatchers in rescheduling trains,this paper introduces an innovative Human-Computer Interaction framework.This framework enables train dispatchers to propose different timetable adjustment instructions to the original or adjusted timetable.These instructions will be processed,stored,analyzed,and digested by computer program,which finally lead to the modification and calculation of the embedded mathematical model,then a new adjusted timetable will be produced and provided to dispatchers for checking and modifying.This framework can iterate for unlimited times based on dispatchers'intentions,until the final results satisfy them.A demonstration system named RTARS(Real-time Timetable Automatic Rescheduling System)is developed based on this framework and it has been applied in Beijing Railway Administration,which shows its effectiveness in reality.展开更多
High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading del...High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.展开更多
Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation.It includes promoting the deep integrat...Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation.It includes promoting the deep integration of cutting-edge technologies with the railway systems,strengthening the research and application of intelligent railway technologies,applying green computing technologies and advancing the collaborative sharing of transportation big data.The high-speed rail system tasks need to process huge amounts of data and heavy workload with the requirement of ultra-fast response.Therefore,it is of great necessity to promote computation efficiency by applying High Performance Computing(HPC)to high-speed rail systems.The HPC technique is a great solution for improving the performance,efficiency,and safety of high-speed rail systems.In this review,we introduce and analyze the application research of high performance computing technology in the field of highspeed railways.These HPC applications are cataloged into four broad categories,namely:fault diagnosis,network and communication,management system,and simulations.Moreover,challenges and issues to be addressed are discussed and further directions are suggested.展开更多
Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteri...Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.展开更多
Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recen...Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recent years,the growing data and computing resources have promoted the development of deep learning,and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods.However,high-speed railroads,as an important part of life,have their unique industry characteristics of image data,which leads to different image fusion techniques with different fusion effects in high-speed railway scenes.This research work first introduces the mainstream technology classification of image fusion,further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes,and introduces the evaluation metrics of image fusion,followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes,and finally provides some possible future image fusion in the field of rail transportation of research.展开更多
Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systemat...Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.展开更多
Organized by： Qinghai Research Institute of Transportation （QRIT）, Department of Transportation （DOT） of the Qinghai Province, China Beijing Jiaotong University, China University of Illinois at Urbana-Champaign...Organized by： Qinghai Research Institute of Transportation （QRIT）, Department of Transportation （DOT） of the Qinghai Province, China Beijing Jiaotong University, China University of Illinois at Urbana-Champaign, USA Moscow State University of Railway Transportation, Moscow, Russia University of Kansas, USA Siberian State University of Railway Engineering, Novo- sibirsk Russia State Key Laboratory of Frozen Soil Engineering, Cold ＆ Arid Regions Environmental and Engineering Re- search Institute, Chinese Academy of Sciences, China Far-east State University of Railway Transportation, Habarovsk, Russia展开更多
Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time...Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time-consuming and resource-intensive but also fails to meet the diverse business needs.Therefore,developing large vision models specifically for the railway industry is of critical importance.This paper examines and explores potential application scenarios for large vision models within the railway sector,proposing a solution for their development.The research builds upon the UPerNet network,utilizing InternImage to replace the original backbone network,thereby enhancing the model's ability to capture details of image targets.To further improve model robustness,Semantic-Aware Normalization(SAN)and Semantic-Aware Whitening(SAW)attention mechanisms are introduced in place of the original pyramid pooling module.Additionally,the integration of spatial attention and channel attention replaces the original decoding part,allowing for dynamic adjustments to attention across various regions.Finally,datasets for railway scenarios were established through semi-automatic annotation.The experimental results indicate that the improved UPerNet_InternImage large vision model proposed for the railway industry has potential to enhance the segmentation accuracy and robustness.The model exhibits faster convergence speeds and improved effectiveness when tackling segmentation tasks in specific railway scenarios.It offers new insights and methodologies for addressing issues prevalent in railway vision scenarios.展开更多
基金the Talent Fund of Beijing Jiaotong University(Grant No.2024XKRC055).
文摘In recent years,railway construction in China has developed vigorously.With continuous improvements in the highspeed railway network,the focus is gradually shifting from large-scale construction to large-scale operations.However,several challenges have emerged within the high-speed railway dispatching and command system,including the heavy workload faced by dispatchers,the difficulty of quantifying subjective expertise,and the need for effective training of professionals.Amid the growing application of artificial intelligence technologies in railway systems,this study leverages Large Language Model(LLM)technology.LLMs bring enhanced intelligence,predictive capabilities,robust memory,and adaptability to diverse real-world scenarios.This study proposes a human-computer interactive intelligent scheduling auxiliary training system built on LLM technology.The system offers capabilities including natural dialogue,knowledge reasoning,and human feedback learning.With broad applicability,the system is suitable for vocational education,guided inquiry,knowledge-based Q&A,and other training scenarios.Validation results demonstrate its effectiveness in auxiliary training,providing substantial support for educators,students,and dispatching personnel in colleges and professional settings.
基金supported by China Railway Research and Development(K2021x001)the Talent Fund of Beijing Jiaotong University(2023JBRC003).
文摘During railway operations,trains normally run as scheduled,but the occurrence of unexpected events will disrupt traffic flow and cause train deviation from the original timetable.In order to assist dispatchers in rescheduling trains,this paper introduces an innovative Human-Computer Interaction framework.This framework enables train dispatchers to propose different timetable adjustment instructions to the original or adjusted timetable.These instructions will be processed,stored,analyzed,and digested by computer program,which finally lead to the modification and calculation of the embedded mathematical model,then a new adjusted timetable will be produced and provided to dispatchers for checking and modifying.This framework can iterate for unlimited times based on dispatchers'intentions,until the final results satisfy them.A demonstration system named RTARS(Real-time Timetable Automatic Rescheduling System)is developed based on this framework and it has been applied in Beijing Railway Administration,which shows its effectiveness in reality.
基金supported by the National Natural Science Foundation of China (U1834211, 61925302, 62103033)the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems (20210104)。
文摘High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.
基金supported in part by the Talent Fund of Beijing Jiaotong University(2023XKRC017)in part by Research and Development Project of China State Railway Group Co.,Ltd.(P2022Z003).
文摘Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation.It includes promoting the deep integration of cutting-edge technologies with the railway systems,strengthening the research and application of intelligent railway technologies,applying green computing technologies and advancing the collaborative sharing of transportation big data.The high-speed rail system tasks need to process huge amounts of data and heavy workload with the requirement of ultra-fast response.Therefore,it is of great necessity to promote computation efficiency by applying High Performance Computing(HPC)to high-speed rail systems.The HPC technique is a great solution for improving the performance,efficiency,and safety of high-speed rail systems.In this review,we introduce and analyze the application research of high performance computing technology in the field of highspeed railways.These HPC applications are cataloged into four broad categories,namely:fault diagnosis,network and communication,management system,and simulations.Moreover,challenges and issues to be addressed are discussed and further directions are suggested.
基金supported by the National Natural Science Foundation of China(No.61903023)the Natural Science Foundation of Bejing Municipality(No.4204110)+1 种基金State Key Laboratory of Rail Traffic Control and Safety(No.RCS2020ZT006,RCS2021ZT006)the Fundamental Research Funds for the Central Universities(No.2020JBM087).
文摘Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.
基金supported in part by the National Key Research and Development Program of China,under Grant 2020YFB2103800.
文摘Image fusion refers to extracting meaningful information from images of different sources or modalities,and then fusing them to generate more informative images that are beneficial for subsequent applications.In recent years,the growing data and computing resources have promoted the development of deep learning,and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods.However,high-speed railroads,as an important part of life,have their unique industry characteristics of image data,which leads to different image fusion techniques with different fusion effects in high-speed railway scenes.This research work first introduces the mainstream technology classification of image fusion,further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes,and introduces the evaluation metrics of image fusion,followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes,and finally provides some possible future image fusion in the field of rail transportation of research.
文摘Digital Twin (DT) technology is revolutionizing the railway sector by providing a virtual replica of physical systems, enabling real-time monitoring, predictive maintenance, and enhanced decision-making. This systematic literature review examines the status, enabling technologies, case studies, and frameworks for DT applications in railway systems with 91 selected papers from Scopus, Web of Science, IEEE, and the Snowballing Technique. The review focuses on four primary subsystems: tracks, civil structures, vehicles, and overhead contact line structures. Key findings reveal that DT has successfully optimized maintenance strategies, improved operational efficiency, and enhanced system safety. Internet of Things (IoT) devices, Artificial Intelligence (AI), machine learning, and cloud computing are critical in implementing DT models. However, challenges like data integration, high implementation costs, and cybersecurity risks remain, necessitating the discussed implications. Future research should focus on improving data interoperability, reducing costs through scalable cloud-based solutions, and addressing cybersecurity vulnerabilities. DT technology has the potential to revolutionize railway infrastructure management, ensuring greater efficiency, safety, and sustainability.
文摘Organized by： Qinghai Research Institute of Transportation （QRIT）, Department of Transportation （DOT） of the Qinghai Province, China Beijing Jiaotong University, China University of Illinois at Urbana-Champaign, USA Moscow State University of Railway Transportation, Moscow, Russia University of Kansas, USA Siberian State University of Railway Engineering, Novo- sibirsk Russia State Key Laboratory of Frozen Soil Engineering, Cold ＆ Arid Regions Environmental and Engineering Re- search Institute, Chinese Academy of Sciences, China Far-east State University of Railway Transportation, Habarovsk, Russia
文摘Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time-consuming and resource-intensive but also fails to meet the diverse business needs.Therefore,developing large vision models specifically for the railway industry is of critical importance.This paper examines and explores potential application scenarios for large vision models within the railway sector,proposing a solution for their development.The research builds upon the UPerNet network,utilizing InternImage to replace the original backbone network,thereby enhancing the model's ability to capture details of image targets.To further improve model robustness,Semantic-Aware Normalization(SAN)and Semantic-Aware Whitening(SAW)attention mechanisms are introduced in place of the original pyramid pooling module.Additionally,the integration of spatial attention and channel attention replaces the original decoding part,allowing for dynamic adjustments to attention across various regions.Finally,datasets for railway scenarios were established through semi-automatic annotation.The experimental results indicate that the improved UPerNet_InternImage large vision model proposed for the railway industry has potential to enhance the segmentation accuracy and robustness.The model exhibits faster convergence speeds and improved effectiveness when tackling segmentation tasks in specific railway scenarios.It offers new insights and methodologies for addressing issues prevalent in railway vision scenarios.
