The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influenc...The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.展开更多
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 detecti...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.展开更多
Discomfort caused by long-term sitting decreases the passenger experience and may lead to musculoskeletal diseases, and this hasbecome one of the main problems for passengers of high-speed railways. However, the comfo...Discomfort caused by long-term sitting decreases the passenger experience and may lead to musculoskeletal diseases, and this hasbecome one of the main problems for passengers of high-speed railways. However, the comfort degradation mechanism during longtermsitting in high-speed railways is still unknown. This study aimed to reveal passengers’ sitting comfort degradation mechanismin high-speed railways. By carrying out long-term sitting tests on high-speed trains running on the Shanghai-Kunming line, the dynamicinterface pressure and subjective comfort including overall and regional comfort of seven participants were obtained.Machinelearning models and statistical analysis methods were combined for data analysis to reveal the effect of regional comfort and thecontribution of sitting duration during the process of sitting comfort degradation. The results show that overall comfort is most significantlyinfluenced by the comfort of the shoulders, waist and buttocks. The seats play different roles before and after 20 minutesduring long-term sitting and there is a lag between the fatigue occurring and being offset. Therefore, the structure of seats affectsoverall comfort by affecting important regional comfort, and a long-term sitting test is necessary for accurate seat assessment. Thecomfort degradation mechanism can be used to define standards for long-term sitting comfort or provide guidance for seat evaluation,and the design and evaluation plan mentioned in this article for second-class seats can be applied to other cases with limitedaccommodating space.展开更多
Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid con...Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.展开更多
Building construction has developed from the use of primitive tools to that of machinery,with a tendency toward automation.Automation of processes and robotics can improve efficiency,accuracy and safety in constructio...Building construction has developed from the use of primitive tools to that of machinery,with a tendency toward automation.Automation of processes and robotics can improve efficiency,accuracy and safety in construction.On the other hand,structural prefabrication for construction is increasingly being adopted worldwide to enhance productivity and to alleviate the environmental impact of conventional construction processes.The combination and application of automation and prefabrication technologies may therefore introduce new developments to the construction industry.This paper provides a comprehensive review of the use of automation technology for structural prefabrication and construction,including recent developments,challenges and future trends.Five stages following the sequence of construction are proposed:design,construction management,robotic manufacturing,autonomous transportation and automatic structural assembly.The paper concludes that the widespread use of automation technology is preferable to structural prefabrication for construction,and that the design for robotic construction introduced through connection innovations may be beneficial as a means of avoiding complex operations and thus improving the efficiency of robotic assembly processes.展开更多
In order to study the shield construction under an existing highway,the initial displacement and the excess pore water pressure solution from Biot consolidation equation were used to derive the analytical solutions of...In order to study the shield construction under an existing highway,the initial displacement and the excess pore water pressure solution from Biot consolidation equation were used to derive the analytical solutions of the vertical deformation and pore water pressure of saturated soil caused by the frontal friction and side friction of the cutterhead.In addition,by introducing the layered method and combining it with other theoretical analytical equations,the expressions of total vertical deformation and total pore water pressure caused by tunnel excavation with different overlying materials were obtained.The simulation results on an engineering project showed that the angleαbetween the direction of the road and the propulsion axis of the shield had a significant influence on the surface settlement of the road.When the angleαwas increased,the settlement curve had higher variation;but the variation would not exceed the maximum settlement value above the shield axis.When the road was perpendicular to the shield axis,a critical point of the road bulging and settlement was formed above the incision.Due to the grinding resistance of the cutterhead,the pore water pressure under the roadbed was distributed asymmetrically on both sides of the shield axis.展开更多
Transmission machinery is widely used in railway vehicles and is an important component in driving the operation of trains.Such transmission components are prone to faults under long exposure to harsh environments and...Transmission machinery is widely used in railway vehicles and is an important component in driving the operation of trains.Such transmission components are prone to faults under long exposure to harsh environments and complex working conditions.This affects normal operation and order,and thus it is important to ensure their safe and reliable operation.Electrical signal-based diagnosis technology has advantages of easy signal acquisition,with no need to install additional sensors,nor embedded monitoring of the object components.It has gradually become a research hotspot in the field of rail transportation diagnosis.This paper describes the fault modes of transmission machinery,takes the electrical signal-based diagnosis method as the entry point,collates and compares the existing diagnosis methods and research results in this field.It analyses their advantages and disadvantages,and finally puts forward problems for current and future research and development.展开更多
The upper bound limit analysis(UBLA)is one of the key research directions in geotechnical engineering and is widely used in engineering practice.UBLA assumes that the slip surface with the minimum factor of safety(FSm...The upper bound limit analysis(UBLA)is one of the key research directions in geotechnical engineering and is widely used in engineering practice.UBLA assumes that the slip surface with the minimum factor of safety(FSmin)is the critical slip surface,and then applies it to slope stability analysis.However,the hypothesis of UBLA has not been systematically verified,which may be due to the fact that the traditional numerical method is difficult to simulate the large deformation.In this study,in order to systematically verify the assumption of UBLA,material point method(MPM),which is suitable to simulate the large deformation of continuous media,is used to simulate the whole process of the slope failure,including the large-scale transportation and deposition of soil mass after slope failure.And a series of comparative studies are conducted on the stability of cohesive slopes using UBLA and MPM.The proposed study indicated that the slope angle,internal friction angle and cohesion have a remarkable effect on the slip surface of the cohesive slope.Also,for stable slopes,the calculation results of the two are relatively close.However,for unstable slopes,the slider volume determined by the UBLA is much smaller than the slider volume determined by the MPM.In other words,for unstable slopes,the critical slip surface of UBLA is very different from the slip surface when the slope failure occurs,and when the UBLA is applied to the stability analysis of unstable slope,it will lead to extremely unfavorable results.展开更多
This paper examines the energy-absorption characteristics of trains for active-passive safety protection.A one-dimensional collision-simulation model of traditional subway vehicles and active-passive safety vehicles w...This paper examines the energy-absorption characteristics of trains for active-passive safety protection.A one-dimensional collision-simulation model of traditional subway vehicles and active-passive safety vehicles was developed based on the multibody dynamics theory using MATLAB simulation software.The effectiveness of the simulation model was verified by scaled-collision tests.Then,the energy-absorption characteristics of traditional trains and the active-passive safety trains under different marshalling conditions were studied.The results showed that as the number of marshalling vehicles increased from 5 to 8,the energy absorption of interface 1 for the active-passive safety trains during the collision was 681 kJ,775 kJ,840 kJ and 901 kJ,and the physical compression of the interface of the head car of the active-passive safety trains was 619 mm,704 mm,764 mm and 816 mm,which was far below the maximum value of 1773 mm.The head car of the active-passive safety subway vehicles therefore had sufficient energy-absorption capacity.Finally,to find the maximum safe impact velocity of the active-passive safety trains,the energy distribution of the active-passive safety subway vehicles with 8-car marshalling at different impact velocities was studied.It was found that the safe impact velocity of an active-passive safety subway vehicle conforming to the requirements of the EN15227 collision standard reached 32 km/h,far exceeding the safe impact velocity of 25 km/h allowed by traditional trains,and representing an increase in the safe impact velocity of 28%.The total collision-energy absorption of the interface of the head car of the active-passive trains was 89.1%higher than that of the traditional trains at the safe impact velocity.The active-passive energy absorption method was therefore effective at improving the crashworthiness of the subway trains.展开更多
Pavement skid resistance is critical for contributing to traffic safety.In this study,two pavement specimens were fabricated using different aggregate gradations and binder contents.Compaction efforts were made to ens...Pavement skid resistance is critical for contributing to traffic safety.In this study,two pavement specimens were fabricated using different aggregate gradations and binder contents.Compaction efforts were made to ensure the same porosity of the two specimens.Some parameters related to the surface morphology were measured using the sand patching method and a 3D laser.The parameters include mean texture depth(MTD),interface roughness(R_(a)),skewness(R_(sk)),kurtosis(R_(ku)),fractal dimension(D)and parameters related to material ratio curve.Relationships between these parameters and skid resistance were quantitatively developed.Skid resistance increased along with the increase of MTD,R_(a) and D.From the surface A to surface B,MTD increased from 0.23 to 1.10 mm,R_(a) increased from 0.93 to 1.80 mm,D increased from 2.25 to 2.43 and the British Pendulum Number increased from 80 to 97 accordingly.The comprehensive analysis could assist scholars and practitioners to facilitate the understanding of tyre-pavement interaction and further assist pavement design.展开更多
The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecti...The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well investigated.In this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs.We then compared the safety performance for two different heterogeneous traffic systems,i.e.AV and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control strategy.We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV.With random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV.We also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.展开更多
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,fr...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 behaviour 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 analysed under the joint action of the three parties.Finally,the influences of important parameters of the model on the behavioural 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 behavioural 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.展开更多
In order to ensure the safety of railway transportation,it is necessary to regularly check for faults and defects in the railway system.Visual inspection technology is conducive to improving the low efficiency,poor ec...In order to ensure the safety of railway transportation,it is necessary to regularly check for faults and defects in the railway system.Visual inspection technology is conducive to improving the low efficiency,poor economy and inaccurate detection results of traditional detection methods.This paper introduces the research and contribution of various scholars in the field of visual inspection,summarizes the application and development of visual inspection technology in the railway industry,and finally forecasts the future research direction of visual inspection technology.展开更多
As an important transportation infrastructure and transportation backbone in China,high-speed rail(HSR)plays a critical role in promoting the development of green and low-carbon transportation.Calculating the CO_(2) e...As an important transportation infrastructure and transportation backbone in China,high-speed rail(HSR)plays a critical role in promoting the development of green and low-carbon transportation.Calculating the CO_(2) emissions reduction performance of HSR will be conduci v e to pr omote the CO_(2) emissions r eduction w ork of the r ail w ay.Based on the Dalkic HSR CO_(2) emissions r eduction performance model,by adjusting the HSR CO_(2) emission factor(CEF HSR),the annual times of departures(T)and other parameters,this study develops a Chinese HSR CO_(2) emissions reduction performance model.Taking the Beijing-Shanghai HSR as the resear c h object,this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes;collects data such as passenger v olume,av era ge electricity use and annual times of departures of the Beijing-Shanghai HSR in 2019;and calculates the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR.This study has two main results:(1)It builds a Chinese HSR CO_(2) emissions reduction performance model based on substitution effect and demand effect.(2)In 2019,the CO_(2) emissions of the Beijing-Shanghai HSR are 2898233.62 t,the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR is 17999482.8 t,the annual CO_(2) emissions of the Beijing-Shanghai line in‘No HSR’case are 7.2 times as in the’HSR’case and the PKT of the HSR is 10.2 g/pkm.Based on the research results,this study proposes three CO_(2) emissions reduction policy suggestions.This study would be helpful for further HSR CO_(2) emissions reduction resear c h and departments related to railway transportation management to make CO_(2) emissions reduction policies.展开更多
As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predi...As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers.However,most of their models focus on how to optimize input variables without considering the interaction between output variables.This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety.Concretely,in this work,the coupling effect of lateral and longitudinal movement is considered in the L.C process.Trajectory changes in two directions will be modelled separately,and the information interaction is completed under the multi-task learing framework.In addition,the trajectory fragents are clustered by the driving features,and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task.Finally,the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory(LSTM).The model training and testing are conducted with the data collected by the driving simulator,and the proposed method expresses better performance in LC trjectory prediction compared with several traditional models.The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems(ADASs)and reduce the traffic accidents caused by lane changes.展开更多
Shipping is perhaps themost international of all the world’s great industries and one of the most dangerous.It is essential to study maritime safety and take reasonable measurements to ensure the security of life and...Shipping is perhaps themost international of all the world’s great industries and one of the most dangerous.It is essential to study maritime safety and take reasonable measurements to ensure the security of life and property.With the rapid development of Artificial Intelligence,industrial upgrading is undergoing a change in the maritime industry,where many techniques have been developed to enhance maritime safety and improve the automation and intelligence of ships,equipment,and infrastructure.展开更多
This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed mo...This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed model is composed of two states.In the first state,decision tree,random forest,gradient boosting decision tree(GBDT)and extreme gradient boosting(XGBoost)models are used to investigate the importance of features.The features that have a high influence on RSs are first selected.In the second state,a K-means clustering method is used to uncover the interdependences between RSs and the influencing features,based on the results in the first state.The proposed method can determine the quantitative relationships between RSs and influencing factors.The results clearly show the influences of the factors on RSs,the possibilities of different train operation RSs under different situations,as well as some key time periods and key trains that the controllers should pay more attention to.The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.展开更多
Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e a...Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.展开更多
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 X...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.The 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,the 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-3train 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.展开更多
Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current predi...Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.展开更多
基金supported by The National Natural Science Foundation of China (Grant No.52072214 and Grant No.71871123)Global Road Safety Partnership (GRSP) (Grant No.CHNXX-RD16-1188).
文摘The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.
基金the National Key R&D Program of China(Grant No.2021YFF0501102)the National Natural Science Foundation of China(Grant No.62120106011 and Grant No.U1934219).
文摘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.
基金the National Natural Science Foundation of China(Grant No.52075553)the Hunan Science Foundation for Distinguished Young Scholars of China(Grant No.2021JJ10059)the School Enterprise Cooperation Program of Central South University(Grant No.2021XQLH011).
文摘Discomfort caused by long-term sitting decreases the passenger experience and may lead to musculoskeletal diseases, and this hasbecome one of the main problems for passengers of high-speed railways. However, the comfort degradation mechanism during longtermsitting in high-speed railways is still unknown. This study aimed to reveal passengers’ sitting comfort degradation mechanismin high-speed railways. By carrying out long-term sitting tests on high-speed trains running on the Shanghai-Kunming line, the dynamicinterface pressure and subjective comfort including overall and regional comfort of seven participants were obtained.Machinelearning models and statistical analysis methods were combined for data analysis to reveal the effect of regional comfort and thecontribution of sitting duration during the process of sitting comfort degradation. The results show that overall comfort is most significantlyinfluenced by the comfort of the shoulders, waist and buttocks. The seats play different roles before and after 20 minutesduring long-term sitting and there is a lag between the fatigue occurring and being offset. Therefore, the structure of seats affectsoverall comfort by affecting important regional comfort, and a long-term sitting test is necessary for accurate seat assessment. Thecomfort degradation mechanism can be used to define standards for long-term sitting comfort or provide guidance for seat evaluation,and the design and evaluation plan mentioned in this article for second-class seats can be applied to other cases with limitedaccommodating space.
基金funded by University of Transport and Commu-nications (UTC) (Grant No.T2019-CT-06TD).
文摘Red-light running(RLR)is a crucial violation that causes traffic accidents and injuries.Understanding factors that affect RLR is very significant to reduce the potential of this violation.Current studies have paid considerable attention to the observable factors,but not to unobservable factors.This study aims to examine the effects of observable and unobservable factors on RLR.This study uses a latent class model(LCM)to assign individuals into two classes—red-light-respectful and red-light-disrespectful road users—by surveying 751 respondents who use private transportation modes.This study incorporates psychological determinants into the LCM to account for unobservable factors.The contribution of this study is the in-depth investigation into law-respectful and law-disrespectful behaviours and intentional and unintentional violators.Such a study has not yet been conducted in the existing literature.In addition,a comprehensive comparison of the LCM and a traditional ordered probit model was conducted.Overall,the results suggest that the LCM is superior to the model that does not consider latent classes.Our estimation results are in alignment with previous studies on RLR:males,younger drivers/riders,less educated road users and motorcyclists are more likely to run red lights.An analysis of the latent variables shows that surrounding conditions—the behaviour of other violators,the absence of traffic police,and long waiting times—increase the possibility of violations.Based on these results,we provide suggestions to policymakers and traffic engineers:the implementation of enforcement cameras and penalties for violators are critical countermeasures to minimize the potential of RLR.
基金support from the Australian Research Council through the Discovery project(DP180102208).
文摘Building construction has developed from the use of primitive tools to that of machinery,with a tendency toward automation.Automation of processes and robotics can improve efficiency,accuracy and safety in construction.On the other hand,structural prefabrication for construction is increasingly being adopted worldwide to enhance productivity and to alleviate the environmental impact of conventional construction processes.The combination and application of automation and prefabrication technologies may therefore introduce new developments to the construction industry.This paper provides a comprehensive review of the use of automation technology for structural prefabrication and construction,including recent developments,challenges and future trends.Five stages following the sequence of construction are proposed:design,construction management,robotic manufacturing,autonomous transportation and automatic structural assembly.The paper concludes that the widespread use of automation technology is preferable to structural prefabrication for construction,and that the design for robotic construction introduced through connection innovations may be beneficial as a means of avoiding complex operations and thus improving the efficiency of robotic assembly processes.
文摘In order to study the shield construction under an existing highway,the initial displacement and the excess pore water pressure solution from Biot consolidation equation were used to derive the analytical solutions of the vertical deformation and pore water pressure of saturated soil caused by the frontal friction and side friction of the cutterhead.In addition,by introducing the layered method and combining it with other theoretical analytical equations,the expressions of total vertical deformation and total pore water pressure caused by tunnel excavation with different overlying materials were obtained.The simulation results on an engineering project showed that the angleαbetween the direction of the road and the propulsion axis of the shield had a significant influence on the surface settlement of the road.When the angleαwas increased,the settlement curve had higher variation;but the variation would not exceed the maximum settlement value above the shield axis.When the road was perpendicular to the shield axis,a critical point of the road bulging and settlement was formed above the incision.Due to the grinding resistance of the cutterhead,the pore water pressure under the roadbed was distributed asymmetrically on both sides of the shield axis.
文摘Transmission machinery is widely used in railway vehicles and is an important component in driving the operation of trains.Such transmission components are prone to faults under long exposure to harsh environments and complex working conditions.This affects normal operation and order,and thus it is important to ensure their safe and reliable operation.Electrical signal-based diagnosis technology has advantages of easy signal acquisition,with no need to install additional sensors,nor embedded monitoring of the object components.It has gradually become a research hotspot in the field of rail transportation diagnosis.This paper describes the fault modes of transmission machinery,takes the electrical signal-based diagnosis method as the entry point,collates and compares the existing diagnosis methods and research results in this field.It analyses their advantages and disadvantages,and finally puts forward problems for current and future research and development.
基金financially supported by the National Natural Science Foundation of China(No.51878668)the Guizhou Provincial Department of Transportation Foundation(Nos.2017-123-033,2018-123-040)+1 种基金the Innovation-Driven Project of Central South University(No.2016CX012)the Science and Technology Project Plan for Key Projects of Jiangxi Transportation Department(No.2019C0011)
文摘The upper bound limit analysis(UBLA)is one of the key research directions in geotechnical engineering and is widely used in engineering practice.UBLA assumes that the slip surface with the minimum factor of safety(FSmin)is the critical slip surface,and then applies it to slope stability analysis.However,the hypothesis of UBLA has not been systematically verified,which may be due to the fact that the traditional numerical method is difficult to simulate the large deformation.In this study,in order to systematically verify the assumption of UBLA,material point method(MPM),which is suitable to simulate the large deformation of continuous media,is used to simulate the whole process of the slope failure,including the large-scale transportation and deposition of soil mass after slope failure.And a series of comparative studies are conducted on the stability of cohesive slopes using UBLA and MPM.The proposed study indicated that the slope angle,internal friction angle and cohesion have a remarkable effect on the slip surface of the cohesive slope.Also,for stable slopes,the calculation results of the two are relatively close.However,for unstable slopes,the slider volume determined by the UBLA is much smaller than the slider volume determined by the MPM.In other words,for unstable slopes,the critical slip surface of UBLA is very different from the slip surface when the slope failure occurs,and when the UBLA is applied to the stability analysis of unstable slope,it will lead to extremely unfavorable results.
文摘This paper examines the energy-absorption characteristics of trains for active-passive safety protection.A one-dimensional collision-simulation model of traditional subway vehicles and active-passive safety vehicles was developed based on the multibody dynamics theory using MATLAB simulation software.The effectiveness of the simulation model was verified by scaled-collision tests.Then,the energy-absorption characteristics of traditional trains and the active-passive safety trains under different marshalling conditions were studied.The results showed that as the number of marshalling vehicles increased from 5 to 8,the energy absorption of interface 1 for the active-passive safety trains during the collision was 681 kJ,775 kJ,840 kJ and 901 kJ,and the physical compression of the interface of the head car of the active-passive safety trains was 619 mm,704 mm,764 mm and 816 mm,which was far below the maximum value of 1773 mm.The head car of the active-passive safety subway vehicles therefore had sufficient energy-absorption capacity.Finally,to find the maximum safe impact velocity of the active-passive safety trains,the energy distribution of the active-passive safety subway vehicles with 8-car marshalling at different impact velocities was studied.It was found that the safe impact velocity of an active-passive safety subway vehicle conforming to the requirements of the EN15227 collision standard reached 32 km/h,far exceeding the safe impact velocity of 25 km/h allowed by traditional trains,and representing an increase in the safe impact velocity of 28%.The total collision-energy absorption of the interface of the head car of the active-passive trains was 89.1%higher than that of the traditional trains at the safe impact velocity.The active-passive energy absorption method was therefore effective at improving the crashworthiness of the subway trains.
基金funded by the National Natural Science Foun-dation of China (Grant Nos.52008405 and 51778638),to which the authors are very grateful.
文摘Pavement skid resistance is critical for contributing to traffic safety.In this study,two pavement specimens were fabricated using different aggregate gradations and binder contents.Compaction efforts were made to ensure the same porosity of the two specimens.Some parameters related to the surface morphology were measured using the sand patching method and a 3D laser.The parameters include mean texture depth(MTD),interface roughness(R_(a)),skewness(R_(sk)),kurtosis(R_(ku)),fractal dimension(D)and parameters related to material ratio curve.Relationships between these parameters and skid resistance were quantitatively developed.Skid resistance increased along with the increase of MTD,R_(a) and D.From the surface A to surface B,MTD increased from 0.23 to 1.10 mm,R_(a) increased from 0.93 to 1.80 mm,D increased from 2.25 to 2.43 and the British Pendulum Number increased from 80 to 97 accordingly.The comprehensive analysis could assist scholars and practitioners to facilitate the understanding of tyre-pavement interaction and further assist pavement design.
基金the Collaboration Project between China and Sweden regarding Research,Development and Innovation within Life Science and Road Traffic Safety(Grant No.2018YFE0102800)in part by the Key Program of National Natural Science Foundation of China(Grant No.U21B2089)+1 种基金in part by the National Natural Science Foundation of China(Grant No.71671100)in part by the Swedish Innovation Agency Vinnova(Grant No.2018-02891).
文摘The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well investigated.In this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs.We then compared the safety performance for two different heterogeneous traffic systems,i.e.AV and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control strategy.We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV.With random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV.We also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.
基金This study was supported by the Central University Basic Scientific Research Fund Program of Civil Aviation University of China(Grant No.3122022037).
文摘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 behaviour 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 analysed under the joint action of the three parties.Finally,the influences of important parameters of the model on the behavioural 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 behavioural 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.
文摘In order to ensure the safety of railway transportation,it is necessary to regularly check for faults and defects in the railway system.Visual inspection technology is conducive to improving the low efficiency,poor economy and inaccurate detection results of traditional detection methods.This paper introduces the research and contribution of various scholars in the field of visual inspection,summarizes the application and development of visual inspection technology in the railway industry,and finally forecasts the future research direction of visual inspection technology.
基金This study was supported by the Fundamental Research Funds for the Central Universities(Grant No.2022YJS053).
文摘As an important transportation infrastructure and transportation backbone in China,high-speed rail(HSR)plays a critical role in promoting the development of green and low-carbon transportation.Calculating the CO_(2) emissions reduction performance of HSR will be conduci v e to pr omote the CO_(2) emissions r eduction w ork of the r ail w ay.Based on the Dalkic HSR CO_(2) emissions r eduction performance model,by adjusting the HSR CO_(2) emission factor(CEF HSR),the annual times of departures(T)and other parameters,this study develops a Chinese HSR CO_(2) emissions reduction performance model.Taking the Beijing-Shanghai HSR as the resear c h object,this study conducts a questionnaire survey to explore the substitution effect and demand effect of HSR on different transportation modes;collects data such as passenger v olume,av era ge electricity use and annual times of departures of the Beijing-Shanghai HSR in 2019;and calculates the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR.This study has two main results:(1)It builds a Chinese HSR CO_(2) emissions reduction performance model based on substitution effect and demand effect.(2)In 2019,the CO_(2) emissions of the Beijing-Shanghai HSR are 2898233.62 t,the CO_(2) emissions reduction performance of the Beijing-Shanghai HSR is 17999482.8 t,the annual CO_(2) emissions of the Beijing-Shanghai line in‘No HSR’case are 7.2 times as in the’HSR’case and the PKT of the HSR is 10.2 g/pkm.Based on the research results,this study proposes three CO_(2) emissions reduction policy suggestions.This study would be helpful for further HSR CO_(2) emissions reduction resear c h and departments related to railway transportation management to make CO_(2) emissions reduction policies.
基金funded in part by the National Natural Science Foundation of China(Grant No.52172310)the Humanities and Social Sciences Foundation of the Ministry of Education(Grant No.21YJCZH147)the Innovation-Driven Project of Central South Univ ersity(Grant No.2020CX041).
文摘As a complex driving behaviour,lane-changing(LC)behaviour has a great influence on traffic flow.Improper lane-changing behaviour often leads to traffic accidents.Numerous studies are currently being conducted to predict lane-change trajectories to minimize dangers.However,most of their models focus on how to optimize input variables without considering the interaction between output variables.This study proposes an LC trajectory prediction model based on a multi-task deep learning framework to improve driving safety.Concretely,in this work,the coupling effect of lateral and longitudinal movement is considered in the L.C process.Trajectory changes in two directions will be modelled separately,and the information interaction is completed under the multi-task learing framework.In addition,the trajectory fragents are clustered by the driving features,and trajectory type recognition is added to the trajectory prediction framework as an auxiliary task.Finally,the prediction process of lateral and longitudinal trajectory and LC style is completed by long short-term memory(LSTM).The model training and testing are conducted with the data collected by the driving simulator,and the proposed method expresses better performance in LC trjectory prediction compared with several traditional models.The results of this study can enhance the trajectory prediction accuracy of advanced driving assistance systems(ADASs)and reduce the traffic accidents caused by lane changes.
文摘Shipping is perhaps themost international of all the world’s great industries and one of the most dangerous.It is essential to study maritime safety and take reasonable measurements to ensure the security of life and property.With the rapid development of Artificial Intelligence,industrial upgrading is undergoing a change in the maritime industry,where many techniques have been developed to enhance maritime safety and improve the automation and intelligence of ships,equipment,and infrastructure.
基金This work was supported by the National Natural Science Foundation of China(Grant No.71871188)The authors also acknowledge the Open Fund of Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and the support of the State Key Laboratory of Rail Traffic Control(Grant No.RCS2019K007).Finally,the authors are grateful for the useful contributions made by their project partners.
文摘This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed model is composed of two states.In the first state,decision tree,random forest,gradient boosting decision tree(GBDT)and extreme gradient boosting(XGBoost)models are used to investigate the importance of features.The features that have a high influence on RSs are first selected.In the second state,a K-means clustering method is used to uncover the interdependences between RSs and the influencing features,based on the results in the first state.The proposed method can determine the quantitative relationships between RSs and influencing factors.The results clearly show the influences of the factors on RSs,the possibilities of different train operation RSs under different situations,as well as some key time periods and key trains that the controllers should pay more attention to.The research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.
基金supported by the Natural Science Foundation of China (Grants No.U1934219,62173137 and 52272347)the Hunan Pr ovincial Natur al Science Foundation of China (Grant No.2021JJ50001).
文摘Convolutional neur al netw orks(CNNs)ar e widel y used in the field of fault diagnosis due to their strong feature-extraction capability.How ever,in eac h timeste p,CNNs onl y consider the curr ent input and ignor e any cyclicity in time,ther efor e pr oducing difficulties in mining temporal features from the data.In this w ork,the third-gener ation neur al netw ork-the spiking neur al netw ork(SNN)-is utilized in bearing fault diagnosis.SNNs incorpor ate tempor al concepts and utilize discrete spike sequences in communication,making them more biolo gically e xplanatory.Inspired by the classic CNN LeNet-5 fr amew ork,a bearing fault diagnosis method based on a convolutional SNN is proposed.In this method,the spiking convolutional network and the spiking classifier network are constructed by using the inte gr ate-and-fire(IF)and leaky-inte gr ate-and-fire(LIF)model,respectively,and end-to-end training is conducted on the overall model using a surrogate gradient method.The signals are adaptively encoded into spikes in the spiking neuron layer.In addition,the network utilizes max-pooling,which is consistent with the spatial-temporal characteristics of SNNs.Combined with the spiking con volutional la y ers,the netw ork fully extracts the spatial-temporal featur es fr om the bearing vibration signals.Experimental validations and comparisons are conducted on bearings.The results show that the proposed method achieves high accuracy and takes fewer time steps.
基金supported by the Science and Tec hnology Research and Development Plan Contract of China National Railway Group Co.,Ltd(Grant No.N2022G012)the Railway Science and Technology Research and Development Center Project(Project No.SYF2022SJ004).
文摘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.The 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,the 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-3train 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.
基金supported by the National Natural Science Foundation of China(Grant No.U22A2053)Major Science and Technology Project of Guangxi Province of China(Grant No.Guike AB23075209)+1 种基金Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund(Grant No.21-050-44-S015)Innovation Project of Guangxi Graduate Education(Grant No.YCSW2023086).
文摘Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.
