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":null,"pages":null},"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}
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":null,"pages":null},"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":null,"pages":null},"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}
This paper investigates the scale and expression of passengers’ anger from the disputes between drivers and passengers, as well as among passengers on the bus by surveying a sample of 757 undergraduate students. The bus passengers’ anger scale and expression inventory (BPASX), a newly-designed questionnaire battery, is used to analyze anger levels and resulted behavior expression of passengers in 6-item anger-provoking situations. The analysis shows that a positive correlation exists between the frequency or duration of bus rides and anger levels/ external anger expression, while self-adaptive expression is only correlated with the frequency. Gender differs only in the violent external expression where males display a greater preference. Meanwhile, the correlations of anger levels among anger-provoking situations are significantly positive. Moreover, anger expression patterns gradually shift from self-adaptation to external expression as anger levels grow in general, but the variation rules of expression frequency are different. Overall, this study provides a newly effective tool to explore characteristics of anger expression of bus passengers under different anger-provoking scenarios and demonstrates its variation features when anger levels change.
{"title":"Bus passengers’ anger scale and expression in anger-provoking situations","authors":"Zhili Yuan, Qun Chen, Shi Ye","doi":"10.1093/tse/tdac038","DOIUrl":"https://doi.org/10.1093/tse/tdac038","url":null,"abstract":"\u0000 This paper investigates the scale and expression of passengers’ anger from the disputes between drivers and passengers, as well as among passengers on the bus by surveying a sample of 757 undergraduate students. The bus passengers’ anger scale and expression inventory (BPASX), a newly-designed questionnaire battery, is used to analyze anger levels and resulted behavior expression of passengers in 6-item anger-provoking situations. The analysis shows that a positive correlation exists between the frequency or duration of bus rides and anger levels/ external anger expression, while self-adaptive expression is only correlated with the frequency. Gender differs only in the violent external expression where males display a greater preference. Meanwhile, the correlations of anger levels among anger-provoking situations are significantly positive. Moreover, anger expression patterns gradually shift from self-adaptation to external expression as anger levels grow in general, but the variation rules of expression frequency are different. Overall, this study provides a newly effective tool to explore characteristics of anger expression of bus passengers under different anger-provoking scenarios and demonstrates its variation features when anger levels change.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44791954","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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
Eliseé Enselme Tuekam Bopda, J. Tamba, Armand Fopah-Lele
The objective of this article is to show the influence of mobile phone use while driving on the performance of petroleum product drivers in Cameroon. The topical questioning centered on the series of accidents and near misses in general and specifically in the petroleum products sector called for the need to look for other causes. To do so, we conceptually proposed a model derived from the literature review and adjusted by practical research, which shows that our approach of is the hypothetico-deductive type. Methodologically, the observations from the experience of 90 questionnaires were operationalized using multivariate logistic regression analyses. The results indicate that making or receiving calls while driving significantly influences the occurrence of reckless driving situations. Reading and sending Short Message Service (SMS or text messages) while driving negatively influences drivers' performance by increasing delivery times. The type of phone used has an effect on the risk of accidents or near misses. Indeed, drivers who use smartphones are 2.14 times more likely to experience crash-like events compared to those who use conventional mobile phones. In addition, with a view to reducing near-misses and accidents, it is important to organize regular appropriate road safety campaigns, to install on-board cameras in all trucks, to provide rest areas and encourage drivers to stop at these areas to check their phones and catch up on missed calls. Additionally to introduce training for drivers in defensive driving with a renewable license after a maximum of 2 years.
{"title":"Impacts of mobile phone use while driving on the performance of petroleum product drivers in Cameroon","authors":"Eliseé Enselme Tuekam Bopda, J. Tamba, Armand Fopah-Lele","doi":"10.1093/tse/tdac044","DOIUrl":"https://doi.org/10.1093/tse/tdac044","url":null,"abstract":"\u0000 The objective of this article is to show the influence of mobile phone use while driving on the performance of petroleum product drivers in Cameroon. The topical questioning centered on the series of accidents and near misses in general and specifically in the petroleum products sector called for the need to look for other causes. To do so, we conceptually proposed a model derived from the literature review and adjusted by practical research, which shows that our approach of is the hypothetico-deductive type. Methodologically, the observations from the experience of 90 questionnaires were operationalized using multivariate logistic regression analyses. The results indicate that making or receiving calls while driving significantly influences the occurrence of reckless driving situations. Reading and sending Short Message Service (SMS or text messages) while driving negatively influences drivers' performance by increasing delivery times. The type of phone used has an effect on the risk of accidents or near misses. Indeed, drivers who use smartphones are 2.14 times more likely to experience crash-like events compared to those who use conventional mobile phones.\u0000 In addition, with a view to reducing near-misses and accidents, it is important to organize regular appropriate road safety campaigns, to install on-board cameras in all trucks, to provide rest areas and encourage drivers to stop at these areas to check their phones and catch up on missed calls. Additionally to introduce training for drivers in defensive driving with a renewable license after a maximum of 2 years.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44700913","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}
Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.
{"title":"Data-driven technology of fault diagnosis in railway point machines: review and challenges","authors":"Xiaoxi Hu, Yuan Cao, T. Tang, Yongkui Sun","doi":"10.1093/tse/tdac036","DOIUrl":"https://doi.org/10.1093/tse/tdac036","url":null,"abstract":"\u0000 Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45485090","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}
{"title":"Introduction to special issue on maritime safety and smart shipping","authors":"Xiaojian Xu, Xinping Yan, Di Zhang","doi":"10.1093/tse/tdac056","DOIUrl":"https://doi.org/10.1093/tse/tdac056","url":null,"abstract":"","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46472164","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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