In the complex urban road traffic network, a sudden accident leads to rapid congestion in the nearby traffic region, which even makes the local traffic network capacity quickly reduced. Therefore, an efficient monitoring system for abnormal conditions of urban road network plays a crucial role in the tolerance of urban road network. The traditional traffic monitoring system not only costs a lot in construction and maintenance, but also may not cover the road network comprehensively, which could not meet the basic needs of traffic management. Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly so that it provide more effective support for traffic management decisions. The extensive use of positioning equipment makes us to obtain accurate trajectory data. This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data. This model uses deep learning to detect abnormal trajectory on the traffic road network. The method effectively analyzes the abnormal source and potential anomaly to judge the abnormal region, which provides an important reference for the traffic department to take effective traffic control measures. Finally, the paper uses Internet vehicle trajectory data of Chengdu to test and gets an accurate result.
{"title":"Road traffic anomaly monitoring and warning based on DeepWalk algorithm","authors":"Zihe Wang, Junqing Ye, Jinjun Tang","doi":"10.1093/tse/tdac049","DOIUrl":"https://doi.org/10.1093/tse/tdac049","url":null,"abstract":"\u0000 In the complex urban road traffic network, a sudden accident leads to rapid congestion in the nearby traffic region, which even makes the local traffic network capacity quickly reduced. Therefore, an efficient monitoring system for abnormal conditions of urban road network plays a crucial role in the tolerance of urban road network. The traditional traffic monitoring system not only costs a lot in construction and maintenance, but also may not cover the road network comprehensively, which could not meet the basic needs of traffic management. Only a more comprehensive and intelligent monitoring method is able to identify traffic anomalies more effectively and quickly so that it provide more effective support for traffic management decisions. The extensive use of positioning equipment makes us to obtain accurate trajectory data. This paper presents a traffic anomaly monitoring and prediction method based on vehicle trajectory data. This model uses deep learning to detect abnormal trajectory on the traffic road network. The method effectively analyzes the abnormal source and potential anomaly to judge the abnormal region, which provides an important reference for the traffic department to take effective traffic control measures. Finally, the paper uses Internet vehicle trajectory data of Chengdu to test and gets an accurate result.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49312556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Freeway on-ramps suffer high crash risks due to frequent merging behaviors. This study developed hazard-based duration models to investigate the merging time interval on freeway on-ramps based on microscopic trajectory data. Fixed effect, random effect, and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables. The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of individual drivers were captured. Modeling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables. Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane, whereas the influence of surrounding vehicles is marginal for those with an auxiliary lane. The findings of this study identify potential countermeasures for improving safety during the merging process.
{"title":"Hazard-based Duration Modeling of Merging Time Interval on Freeway On-Ramps","authors":"Ye Li, Jichen Zhu, M. Haque, Jaeyoung Lee","doi":"10.1093/tse/tdac040","DOIUrl":"https://doi.org/10.1093/tse/tdac040","url":null,"abstract":"\u0000 Freeway on-ramps suffer high crash risks due to frequent merging behaviors. This study developed hazard-based duration models to investigate the merging time interval on freeway on-ramps based on microscopic trajectory data. Fixed effect, random effect, and random parameters Weibull distributed accelerated failure time models were developed to capture merging time as a function of various dynamic variables. The random parameters model was found to outperform the two counterparts since the unobserved heterogeneity of individual drivers were captured. Modeling estimation results indicate that drivers along the merging section with an auxiliary lane perform a smooth merging process and are easily affected by speed variables. Dynamics of leading and following vehicles on the merging and target lanes are found to influence the merging time interval for merging without an auxiliary lane, whereas the influence of surrounding vehicles is marginal for those with an auxiliary lane. The findings of this study identify potential countermeasures for improving safety during the merging process.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47783885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Liu, Xinmin Pan, Rui Yang, Zhu Duan, Ye Li, Shi Yin, N. Nikitas, Hui Liu
Considering the application of wind forecasting technology along the railway, it becomes an effective means to reduce the risk of train derailment and overturning. Accurate prediction of crosswinds can provide scientific guidance for safe train operation. To obtain more reliable wind speed prediction results, this study proposes an intelligent ensemble forecasting method for strong winds along the high-speed railway. The method consists of three parts, including data preprocessing module, hybrid prediction module, and reinforcement learning ensemble module. First, fast ensemble empirical model decomposition (FEEMD) is used to process the original wind speed data. Then, broyden-fletcher-goldfarb-shanno (BFGS), non-linear autoregressive network with exogenous inputs (NARX), and deep belief network (DBN), three benchmark predictors with different characteristics, are employed to build prediction models for all the sublayers of decomposition. Finally, Q-learning is utilized to iteratively calculate the combined weights of the three models, and the prediction results of each sublayer are superimposed to obtain the model output. The real wind speed data of two Railway stations in Xinjiang are used for experimental comparison. Experiments show that compared with the single benchmark model, the hybrid ensemble model has better accuracy and robustness for wind speed prediction along the railway. The 1-step forecasting results mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of Q-learning-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s, 0.6509%, 0.1146 m/s, and 0.0458 m/s, 0.2709%, 0.0616 m/s. The proposed ensemble model is a promising method for railway wind speed prediction.
{"title":"Forecasting wind speed using a reinforcement learning hybrid ensemble model: a high-speed railways strong wind signal prediction study in Xinjiang, China","authors":"B. Liu, Xinmin Pan, Rui Yang, Zhu Duan, Ye Li, Shi Yin, N. Nikitas, Hui Liu","doi":"10.1093/tse/tdac064","DOIUrl":"https://doi.org/10.1093/tse/tdac064","url":null,"abstract":"\u0000 Considering the application of wind forecasting technology along the railway, it becomes an effective means to reduce the risk of train derailment and overturning. Accurate prediction of crosswinds can provide scientific guidance for safe train operation. To obtain more reliable wind speed prediction results, this study proposes an intelligent ensemble forecasting method for strong winds along the high-speed railway. The method consists of three parts, including data preprocessing module, hybrid prediction module, and reinforcement learning ensemble module. First, fast ensemble empirical model decomposition (FEEMD) is used to process the original wind speed data. Then, broyden-fletcher-goldfarb-shanno (BFGS), non-linear autoregressive network with exogenous inputs (NARX), and deep belief network (DBN), three benchmark predictors with different characteristics, are employed to build prediction models for all the sublayers of decomposition. Finally, Q-learning is utilized to iteratively calculate the combined weights of the three models, and the prediction results of each sublayer are superimposed to obtain the model output. The real wind speed data of two Railway stations in Xinjiang are used for experimental comparison. Experiments show that compared with the single benchmark model, the hybrid ensemble model has better accuracy and robustness for wind speed prediction along the railway. The 1-step forecasting results mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of Q-learning-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s, 0.6509%, 0.1146 m/s, and 0.0458 m/s, 0.2709%, 0.0616 m/s. The proposed ensemble model is a promising method for railway wind speed prediction.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48759383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cargo ship sailing within the ice channel that assisting icebreaker tracks in the compact ice cover is the usual practice of the navigation for the difficult ice conditions in the freezing seas and in the Arctic water areas. When the icebreaker or an ahead vessel stops before the insuperable ice obstacle or because the engine trouble, the danger of an emergency appears, namely, the collision with the icebreaker or the ahead ship, if the interval between them is not sufficient for the effective braking and stop. The paper presents the equation that describes the ship braking process within an ice channel and includes the thrust of the propeller that works under the reverse regime. The specific of this regime is following: the ship continues the motion “forward» and the propeller rotates “backward”. Analytical method for description of the ship propeller work on the reverse regime is absent because the detached flow on its blades. The paper describes the developed empirical method of this regime parameterization on the base of the serial models of propellers testing. The outcomes of this investigation will be applied for the ship braking process simulation and the safe interval between the ship and the icebreaker evaluation in what follows.
{"title":"Parameterization of the Propeller Thrust for Modelling Ship Braking within Ice Channel behind Icebreaker","authors":"V. Goncharov, N. Klementieva","doi":"10.1093/tse/tdac042","DOIUrl":"https://doi.org/10.1093/tse/tdac042","url":null,"abstract":"\u0000 Cargo ship sailing within the ice channel that assisting icebreaker tracks in the compact ice cover is the usual practice of the navigation for the difficult ice conditions in the freezing seas and in the Arctic water areas. When the icebreaker or an ahead vessel stops before the insuperable ice obstacle or because the engine trouble, the danger of an emergency appears, namely, the collision with the icebreaker or the ahead ship, if the interval between them is not sufficient for the effective braking and stop. The paper presents the equation that describes the ship braking process within an ice channel and includes the thrust of the propeller that works under the reverse regime. The specific of this regime is following: the ship continues the motion “forward» and the propeller rotates “backward”. Analytical method for description of the ship propeller work on the reverse regime is absent because the detached flow on its blades. The paper describes the developed empirical method of this regime parameterization on the base of the serial models of propellers testing. The outcomes of this investigation will be applied for the ship braking process simulation and the safe interval between the ship and the icebreaker evaluation in what follows.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42493190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Sun, Guang Chen, Jun Chen, Xiao-bai Li, Ming-zan Tang, Mu Zhong
Environmental wind measurements are essential for ensuring the operational safety of rail vehicles. In our previous work, an anemometer that can be mounted on the top of a train to achieve real-time measurements of wind speed and direction was proposed based on the pressure distributions around the cylindrical anemometer. However, the flow field on the top of the train is significantly influenced by the train; thus, the measured data might differ from the actual environmental wind parameters, particularly when trains are subjected to windbreak walls. In this study, simulations considering flow fields around trains installed with the proposed anemometer were conducted, and an improved delayed detached eddy simulation approach was adopted. Through simulations, the flow field at the top of the train was analysed, and the aerodynamic characteristics of the anemometer were investigated. Accordingly, relationships between the measured wind characteristics and environmental wind characteristics are presented under various situations herein. Field experiments were performed for the proposed anemometer installed on a certain type of high-speed train along the Nanjiang Railway in China. The results obtained from both the numerical and experimental studies show that the proposed method has high accuracy for measuring environmental wind speed and direction when mounted on the top of a train.
{"title":"Performance of Vehicle-mounted Anemometer under Crosswind—Simulation and Experiment","authors":"Bo Sun, Guang Chen, Jun Chen, Xiao-bai Li, Ming-zan Tang, Mu Zhong","doi":"10.1093/tse/tdac053","DOIUrl":"https://doi.org/10.1093/tse/tdac053","url":null,"abstract":"\u0000 Environmental wind measurements are essential for ensuring the operational safety of rail vehicles. In our previous work, an anemometer that can be mounted on the top of a train to achieve real-time measurements of wind speed and direction was proposed based on the pressure distributions around the cylindrical anemometer. However, the flow field on the top of the train is significantly influenced by the train; thus, the measured data might differ from the actual environmental wind parameters, particularly when trains are subjected to windbreak walls. In this study, simulations considering flow fields around trains installed with the proposed anemometer were conducted, and an improved delayed detached eddy simulation approach was adopted. Through simulations, the flow field at the top of the train was analysed, and the aerodynamic characteristics of the anemometer were investigated. Accordingly, relationships between the measured wind characteristics and environmental wind characteristics are presented under various situations herein. Field experiments were performed for the proposed anemometer installed on a certain type of high-speed train along the Nanjiang Railway in China. The results obtained from both the numerical and experimental studies show that the proposed method has high accuracy for measuring environmental wind speed and direction when mounted on the top of a train.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46250350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study is carried out to promote the precise supervision of dangerous goods transportation training, improve the efficiency of civil aviation supervision, and further ensure the safety of air transportation. First, from the perspective of behavior interaction and interest demands, evolutionary game theory is used to construct a tripartite evolutionary game model of dangerous goods transportation training institutions, the Civil Aviation Administration of China (CAAC), and the public. Then, the evolutionary game equilibrium of the system is further analyzed under the joint action of the three parties. Finally, the influences of important parameters of the model on the behavioral strategy selection of the three parties are investigated via MATLAB numerical simulation. The conclusions reveal that the system has three evolutionarily stable strategies under different parameters, namely (non-compliant training, supervision, non-participation in supervision), (non- compliant training, supervision, participation in supervision), and (compliant training, supervision, non-participation in supervision). Moreover, the CAAC supervision cost, the fine amount, the supervision cost of public participation, the supervision success rate, and the reporting reward amount are the main parameters that affect the behavioral strategy selection of the tripartite game players. The conclusions and suggestions of this study provide a decision-making basis and guidance for the supervision and management of civil aviation dangerous goods transportation training.
{"title":"Research on the evolutionary game of the supervision of civil aviation dangerous goods transportation training","authors":"Shen Hai-bin, Zhao Sheng-nan","doi":"10.1093/tse/tdac074","DOIUrl":"https://doi.org/10.1093/tse/tdac074","url":null,"abstract":"\u0000 This study is carried out to promote the precise supervision of dangerous goods transportation training, improve the efficiency of civil aviation supervision, and further ensure the safety of air transportation. First, from the perspective of behavior interaction and interest demands, evolutionary game theory is used to construct a tripartite evolutionary game model of dangerous goods transportation training institutions, the Civil Aviation Administration of China (CAAC), and the public. Then, the evolutionary game equilibrium of the system is further analyzed under the joint action of the three parties. Finally, the influences of important parameters of the model on the behavioral strategy selection of the three parties are investigated via MATLAB numerical simulation. The conclusions reveal that the system has three evolutionarily stable strategies under different parameters, namely (non-compliant training, supervision, non-participation in supervision), (non- compliant training, supervision, participation in supervision), and (compliant training, supervision, non-participation in supervision). Moreover, the CAAC supervision cost, the fine amount, the supervision cost of public participation, the supervision success rate, and the reporting reward amount are the main parameters that affect the behavioral strategy selection of the tripartite game players. The conclusions and suggestions of this study provide a decision-making basis and guidance for the supervision and management of civil aviation dangerous goods transportation training.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46293037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To assess the operational safety risk of long-term evolution for metro (LTE-M) communication system more accurately, guide maintenance strategy, the improved evidence theory and multi-attribute ideal reality comparative analysis (MAIRCA) approaches are proposed respectively. According to the features of the LTE-M system, the risk evaluation system is established. The enhanced structural entropy weight method is used to count the weight. Furthermore, combined with nine-element fuzzy mathematics to transform the degree of membership. Modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory. Then get the system risk grade assessment result. For the purpose of forming the ranking of indicator importance, the MAIRCA is introduced and the sort is based on three-dimensional. The operational state of the metro line is used as the data source in various ways based on the test and calculation. The results show that the method is effective, compared with the others, the confidence degree in the obtained risk grade increased by 7.12%. It is verified that MAIRCA can be applied to the field of urban rail transit because excellent stability and the ranking result of risk factors is reasonable. The influencing indicator with the highest importance is’ the equipment failure rate’.
{"title":"Study on Risk Assessment and Factors Ranking of LTE-M Communication System","authors":"Xiaochun Wu, Yu Gao, Weichao Zheng","doi":"10.1093/tse/tdac067","DOIUrl":"https://doi.org/10.1093/tse/tdac067","url":null,"abstract":"To assess the operational safety risk of long-term evolution for metro (LTE-M) communication system more accurately, guide maintenance strategy, the improved evidence theory and multi-attribute ideal reality comparative analysis (MAIRCA) approaches are proposed respectively. According to the features of the LTE-M system, the risk evaluation system is established. The enhanced structural entropy weight method is used to count the weight. Furthermore, combined with nine-element fuzzy mathematics to transform the degree of membership. Modifying the conflict and fusion rules to solve the confidence degree clashed problem of evidence theory. Then get the system risk grade assessment result. For the purpose of forming the ranking of indicator importance, the MAIRCA is introduced and the sort is based on three-dimensional. The operational state of the metro line is used as the data source in various ways based on the test and calculation. The results show that the method is effective, compared with the others, the confidence degree in the obtained risk grade increased by 7.12%. It is verified that MAIRCA can be applied to the field of urban rail transit because excellent stability and the ranking result of risk factors is reasonable. The influencing indicator with the highest importance is’ the equipment failure rate’.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46321581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to study the influence of square-cone energy-absorbing structures on the mechanical behavior of the collision performance of the leading vehicle, a parameterization method for rapidly changing the performance of energy-absorbing structures was proposed. Firstly, a finite element simulation model of the collision of the leading vehicle with a square-cone energy-absorbing structure was constructed. Then, the platform force, the slope of the platform force and the initial peak force of the force-displacement curve derived from the energy-absorbing structure were studied for the collision performance of the leading vehicle. Finally, the correlation model of the square cone energy-absorbing structure and the mechanical behavior of the collision performance of the leading vehicle was established by the response surface method. The results showed that the increase of the platform force of the energy-absorbing structure can effectively buffer the longitudinal impact of the train and reduce the nodding attitude of the train. The increase of the platform force slope can not only effectively buffer the longitudinal impact and vertical nodding of the train, but also reduce the lateral swing of the train. An increase in the initial peak force to a certain extent may lead to a change in the deformation mode, thereby reducing the energy absorption efficiency. The correlation model can guide the design of the square-cone energy-absorbing structure and predict the deformation attitude of the leading vehicle.
{"title":"Correlation study between the square cone energy-absorbing structure and the frontal collision behavior of leading vehicles","authors":"Ping Xu, Ying Gao, Chong Huang, Chengxing Yang, Shuguang Yao, Quanwei Che","doi":"10.1093/tse/tdac054","DOIUrl":"https://doi.org/10.1093/tse/tdac054","url":null,"abstract":"\u0000 In order to study the influence of square-cone energy-absorbing structures on the mechanical behavior of the collision performance of the leading vehicle, a parameterization method for rapidly changing the performance of energy-absorbing structures was proposed. Firstly, a finite element simulation model of the collision of the leading vehicle with a square-cone energy-absorbing structure was constructed. Then, the platform force, the slope of the platform force and the initial peak force of the force-displacement curve derived from the energy-absorbing structure were studied for the collision performance of the leading vehicle. Finally, the correlation model of the square cone energy-absorbing structure and the mechanical behavior of the collision performance of the leading vehicle was established by the response surface method. The results showed that the increase of the platform force of the energy-absorbing structure can effectively buffer the longitudinal impact of the train and reduce the nodding attitude of the train. The increase of the platform force slope can not only effectively buffer the longitudinal impact and vertical nodding of the train, but also reduce the lateral swing of the train. An increase in the initial peak force to a certain extent may lead to a change in the deformation mode, thereby reducing the energy absorption efficiency. The correlation model can guide the design of the square-cone energy-absorbing structure and predict the deformation attitude of the leading vehicle.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45850337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the current problems of high failure rate and low diagnostic efficiency of Railway Point Machines (RPMs) in railway industry, a short-time method of fault diagnosis is proposed. Considering the effect of noise on power signals in the data acquisition process of railway Centralized Signaling Monitoring (CSM) System, this study utilizes wavelet threshold denoising to eliminate the interference of it. The consequences show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals. Then in order to attain lightweight and shorten running time of diagnosis model, Mallat wavelet decomposition and artificial immune algorithm are applied to RPMs fault diagnosis. Finally, voluminous experiments using veritable power signals collected from CSM are introduced, which manifest that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time. It substantiates the validity and feasibility of the presented approach.
{"title":"Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm","authors":"Xiaochun Wu, Weikang Yang, Jianrong Cao","doi":"10.1093/tse/tdac072","DOIUrl":"https://doi.org/10.1093/tse/tdac072","url":null,"abstract":"\u0000 Aiming at the current problems of high failure rate and low diagnostic efficiency of Railway Point Machines (RPMs) in railway industry, a short-time method of fault diagnosis is proposed. Considering the effect of noise on power signals in the data acquisition process of railway Centralized Signaling Monitoring (CSM) System, this study utilizes wavelet threshold denoising to eliminate the interference of it. The consequences show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals. Then in order to attain lightweight and shorten running time of diagnosis model, Mallat wavelet decomposition and artificial immune algorithm are applied to RPMs fault diagnosis. Finally, voluminous experiments using veritable power signals collected from CSM are introduced, which manifest that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time. It substantiates the validity and feasibility of the presented approach.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49179134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Li Yue, Luyue Liu, Maoqing Li, Baodi Xiao, Xiaochun Wu
The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly. XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms. To begin, the text features were extracted using the improved TF-IDF (Term Frequency–Inverse Document Frequency) approach, and 24 fault feature words were chosen and converted into weight word vectors. Secondly, considering the imbalanced fault categories in the data set, ADASYN (Adaptive Synthetic sampling) adaptive synthetically oversampling technique was used to synthesize a few category fault samples. Finally, the data samples were split into training and test sets based on the fault text data of CTCS-3 train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel. The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search. Compared with other methods, the evaluation index of the XGBoost model was significantly improved. The diagnostic accuracy reached 95.43%, which verifies the effectiveness of the method in text fault diagnosis.
{"title":"Research on Text Fault Recognition for On-board Equipment of C3 Train Control System Based on Integrated XGBoost Algorithm","authors":"Li Yue, Luyue Liu, Maoqing Li, Baodi Xiao, Xiaochun Wu","doi":"10.1093/tse/tdac066","DOIUrl":"https://doi.org/10.1093/tse/tdac066","url":null,"abstract":"\u0000 The robust guarantee of train control on-board equipment is inextricably linked to the safe functioning of a high-speed train. A fault diagnostic model of on-board equipment is built utilizing the integrated learning XGBoost (eXtreme Gradient Boosting) algorithm to help technicians assess the malfunction category of high-speed train control on-board equipment accurately and rapidly. XGBoost algorithm iterates multiple decision tree models to improve the accuracy of fault diagnosis by lifting the predicted residual and adding regular terms. To begin, the text features were extracted using the improved TF-IDF (Term Frequency–Inverse Document Frequency) approach, and 24 fault feature words were chosen and converted into weight word vectors. Secondly, considering the imbalanced fault categories in the data set, ADASYN (Adaptive Synthetic sampling) adaptive synthetically oversampling technique was used to synthesize a few category fault samples. Finally, the data samples were split into training and test sets based on the fault text data of CTCS-3 train control on-board equipment recorded by Guangzhou Railway Group maintenance personnel. The XGBoost model was utilized to realize the automatic fault location of the test set after optimized parameter tuning through grid search. Compared with other methods, the evaluation index of the XGBoost model was significantly improved. The diagnostic accuracy reached 95.43%, which verifies the effectiveness of the method in text fault diagnosis.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47673213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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