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Two-Stage OD Flow Prediction for Emergency in urban Rail transit

IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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A Semi-Supervised Railway Foreign Object Detection Method Based on GAN 21

A Semi-Supervised Railway Foreign Object Detection Method Ba...

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Chen, Yanqi Tong, Shuzhen Lu, Xiaobo Wei, Yun School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Beijing Mass Transit Railway Operation Corporation Limited Beijing China

ISBN: (纸本)9781450389853

The rapid development of deep learning provides new technical means for railway foreign object detection. However, in practical applications, the datasets of railways with foreign objects are scarce. In order to solve this problem, by improving the loss function and anomaly image evaluation standard, this paper proposes a newsemisupervised anomaly detection method based on GAN (Generative Adversarial Networks). Experiments show that our method can achieve railway foreign object detection without anomaly prior knowledge. Regarding anomaly recognition, a 0.058 AUC (Area Under Curve) and a 6% classification accuracy relative improvement for the railway dataset used in this paper are obtained.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Generative adversarial networks

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An Improved Faster R-CNN for Railway Fastening System Detection 21

An Improved Faster R-CNN for Railway Fastening System Detect...

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5th International Conference on Computer Science and Application Engineering, CSAE 2021

作者： Peng, Xinfeng Tong, Shuzhen Lu, Xiaobo Wei, Yun School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Beijing Mass Transit Railway Operation Corporation Limited Beijing100044 China

ISBN: (纸本)9781450389853

In the automatic railway anomaly inspection technology based on image processing and deep learning, an effective algorithm used for high-precision detection of the fastening system is very important, especially in turnout sections. It is challenging because the background of the turnout sections is complicated with various types of targets. This paper improved the Faster R-CNN model, used multi-scale feature map fusion for small targets. And modified predefined anchor to generate region proposals, added attention module to make the network focus on meaningful feature. Besides, this paper used cross-entropy function and SmoothL1 loss function for training and labeled 1200 image samples as dataset. Compared with the original Faster R-CNN model, the experimental results (AP) of the improved model in this paper increased from 96.3% to 98.9%, which effectively reduced the fault detection and missed detection and improved the accuracy of location.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Deep learning

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Review on recent progress in on-line monitoring technology for atmospheric pollution source emissions in China

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Journal of Environmental Sciences 2023年第1期123卷 367-386页

作者： Huanqin Wang Jitong Zhou Xue Li Qiang Ling Hongyuan Wei Lei Gao Ying He Ming Zhu Xiao Xiao Youjiang Liu Shan Li Chilai Chen Guotao Duan Zhimin Peng Peili Zhou Yufeng Duan Jianbing Wang Tongzhu Yu Yixin Yang Jiguang Wang Zhen Zhou Huaqiao Gui Yanjun Ding State Key Laboratory of Transducer Technology Institute of Intelligent MachinesHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Institute of Mass Spectrometry and Atmospheric Environment Jinan UniversityGuangzhou 510632China Department of Automation University of Science and Technology of ChinaHefei 230027China China Automotive Technology and Research Center Tianjin 300300China Institute of Solid State Physics Chinese Academy of SciencesHefei 230031China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefei 230031China School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Power Systems Department of Energy and Power EngineeringTsinghua UniversityBeijing 100084China Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education School of Energy and EnvironmentSoutheast UniversityNanjing 210096China School of Chemical and Environmental Engineering China University of Mining and Technology(Beijing)Beijing 100083China CAS Center for Excellence in Regional Atmospheric Environment Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China

Emissions from mobile sources and stationary sources contribute to atmospheric pollution in China,and its components,which include ultrafine particles(UFPs),volatile organic compounds(VOCs),and other reactive gases,such as NH3and NOx,are the most harmful to human *** has released various regulations and standards to address pollution from mobile and stationary ***,it is urgent to develop online monitoring technology for atmospheric pollution source *** study provides an overview of the main progress in mobile and stationary source monitoring technology in China and describes the comprehensive application of some typical instruments in vital areas in recent *** instruments have been applied to monitor emissions from motor vehicles,ships,airports,the chemical industry,and electric power *** only has the level of atmospheric environment monitoring technology and equipment been improving,but relevant regulations and standards have also been constantly ***,the developed instruments can provide scientific assistance for the successful implementation of *** to the potential problem areas in atmospheric pollution in China,some research hotspots and future trends of atmospheric online monitoring technology are ***,more advanced atmospheric online monitoring technology will contribute to a comprehensive understanding of atmospheric pollution and improve environmental monitoring capacity.

关键词： Atmospheric pollution Mobile sources Stationary sources On-line monitoring technology

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A Robust Rail Positioning Method for Turnout and Non-turnout Sections

A Robust Rail Positioning Method for Turnout and Non-turnout...

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第40届中国控制会议

作者： Shixiang Su Shuzhen Tong Xiaobo Lu Yun Wei School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Beijing Mass Transit Railway Operation Corporation Limited

Rail positioning is an essential task for railway track defect detection. The turnout is one of the main components of railway tracks. In order to realize the positioning of rails in turnout sections and non-turnout sections, this paper proposes a robust rail positioning method based on double sliding windows. Firstly, extract lines from the preprocessed image. Secondly,lines are discretized into points, and then all points are projected to the x-axis of the image. Finally, a double sliding window method is used to search for the rail area. The experimental results show that the proposed method can effectively identify the rail’s location in the turnout area, and the detection accuracy can reach 99%. What’s more, it has strong adaptability to complex detection environments such as curved rail or broken rail.

关键词： Rail positioning Turnout Railway Line detection Sliding window

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Realization of a Data-based Adaptive Networked Tracking control in NetCon System

Realization of a Data-based Adaptive Networked Tracking Cont...

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第40届中国控制会议

作者： Ye Zhao Shiwen Tong College of Urban Rail Transit and Logistics Beijing Union University College of Robotics Beijing Union University State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of Sciences

It is very difficult to realize the networked tracking control of a controlled process with unclear mechanism. An adaptive networked tracking control method that we proposed can solve such a problem. The method is a data-based control scheme started from the input-output data of the controlled process. We have realized it in the NetCon system. Firstly, the T-S fuzzy model of the controlled object is established according to input-output data by using fuzzy cluster modeling *** the fuzzy prediction model is obtained by transforming it into the fuzzy singleton model through equivalent transformation.A series of control actions are derived by inverting the fuzzy singleton model according to the reversibility condition. These predicted values can compensate the data dropout and time delay in the network communication channels. The influence of external disturbance and uncertainty on system performance is eliminated by using internal model structure and adaptive control method. Finally, experiment results in NetCon system show the effectiveness of the proposed method.

关键词： Fuzzy singleton model inverse model adaptive strategy NetCon system

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Research on Risk Management and control System's System of urban Rail transit

SSRN

引用

SSRN 2021年

作者： Wang, Yanhui Wang, Yitong Li, Man Xu, Huijie Jia, Limin Li, Yujie State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Beijing Research Center of Urban Traffic Information Sensing and Service Technology Beijing Jiaotong University Beijing100044 China Research and Development Center of Transport Industry of Technologies Equipment of Urban Rail Operation Safety Management Beijing100044 China Beijing Mass Transit Railway Operation Corporation LTD Beijing100044 China

The networked operation of urban rail transit (URT) is an inevitable main trend in the development of urbanization. Building an efficient and reasonable active prevention and control system is necessary for networked URT. However, most of the URT companies have not established a real active prevention and control system at present. As such, this study aims to construct a risk management and control system’s system (RMACS’s system), which based on the basic concepts and correlations of safety related words. In this system, we put forward the method for identifying risk control node, constructing the risk control chain and the risk control model. Furthermore, this paper proposes a dual management mechanism leading the transformation from event-driven to active preventive of URT. © 2021, The Authors. All rights reserved.

关键词： Risk management

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Data-driven intelligent monitoring system for key variables in wastewater treatment process

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Chinese Journal of Chemical Engineering 2018年第10期26卷 2093-2101页

作者： Honggui Han Shuguang Zhu Junfei Qiao Min Guo College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

关键词： Data-driven Soft sensor Intelligent monitoring system Data distribution service Wastewater treatment process

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A data-derived soft-sensor method for monitoring effluent total phosphorus

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Chinese Journal of Chemical Engineering 2017年第12期25卷 1791-1797页

作者： Shuguang Zhu Honggui Han Min Guo Junfei Qiao College of Electronic Information & Control Engineering Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124 China Engineering Research Center of Digital Community Ministry of Education Beijing 100124 China Beijing Laboratory for Urban Mass Transit Beijing 100124 China

The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods.

关键词： Data-derived soft-sensor Effluent total phosphorus Wastewater treatment process Radial basis function neural network Partial least square method

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