Railway Dangerous Goods Transportation Research Articles

Mining the accident causes of railway dangerous goods transportation: A Logistics-DT-TFP based approach

Accurately and quickly mining the hidden information in railway dangerous goods transportation (RDGT) accident reports has great significance for its safety management. In this paper, a data mining method Logistics-DT-TFP is proposed for analysing the causes of RDGT accidents. Firstly, analyse the transportation process, extract the cause of the accident, and classify the severity of the accident. Then, using ordered multi-classification Logistic regression for correlation calculation, qualitatively judge and quantitatively analyse the relationship between each cause and the severity of the accident. The feature tags of the Decision Tree (DT) are screened, the C5.0 algorithm is used to obtain the accident coupling rules. Next, the FP-Growth algorithm is used to mine frequent itemsets, and TOP-K is used to improve it and output effective association rules with the degree of lift as the indicator, which avoids repeated traversal of the database, shortens the time complexity, and reduces the impact of the minimum support setting on the calculation results. The degree of lift among the causes in the coupling chain is calculated as a complement to the extraction of coupling rules. Finally, based on the analysis and mining results of case study, the management strategies for railway dangerous goods are proposed.

Using Radial Basis Function and Back Propagation to predicate fault in a railway dangerous goods transportation system considering the Markov Correction

Nowadays, we can obtain a huge amount of information and data of railway dangerous goods transportation system through various technical documents, accident reports, monitoring technologies etc. For the space composed of massive historical information and data, a data-driven fault predication approach that can better grasp some important local information while ignoring the negative impact of other data in the global might be practical. However, we have the following challenges: (i) How to reduce the impact of unimportant information on prediction results in advance? (ii) What kind of local approximation data-driven approach should be applied, and (iii) How to correct the results and improve the reliability and accuracy after fault predication based on the local approximation data-driven approach? In this paper, two data-driven approaches including Back Propagation with Markov Correction and Radial Basis Function with Markov Correction are proposed to predicate the fault in a railway dangerous goods transportation system. In order to solve challenge (i), we decompose the whole transportation process into multiple sub-processes based on Work Breakdown Structure with clear duration boundary on the time axis, and use Risk Breakdown Structure to detect the possible faults in each sub-process. In order to solve challenge (ii), we use and compare Back Propagation/Radial Basis Function with global approximation/local approximation and nonlinear prediction ability to learn and predicate the fault based on the collected historical data. In order to solve challenge (iii), we correct the prediction results with fluctuating deviation by using Markov Correction due to its non-aftereffect. Finally, a case study is conducted based on the collected historical fault data of railway dangerous goods transportation system in China. The results show that, for the prediction results based on Back Propagation, the average error increases from 1.43 to 1.88 and the Mean Square Error increases from 3.27 to 5.60 after Markov Correction. For the prediction results based on Radial Basis Function, the average error decreases from 1.13 to 1.11 and the Mean Square Error decreases from 1.81 to 1.76 after Markov Correction. The non-aftereffect of Markov chain well corresponds to the local approximation of Radial Basis Function. When the prediction value is determined by some data of the series, Radial Basis Function with Markov Correction can be applied. When the prediction value is determined by all the data (small-scale) of the series, Back Propagation with Markov Correction is better.

Optimal Location of Emergency Facility Sites for Railway Dangerous Goods Transportation under Uncertain Conditions

Railroad accidents involving dangerous goods (DG) need to be rescued quickly due to their hazardous nature. This paper proposes an emergency facility location model for the railway dangerous-goods transportation problem (RDGT-EFLP, abbreviated as EFLP). The EFLP model is based on an ellipsoidal robust model that introduces a robust control safety parameter Ω to measure the risk preferences of decision makers and limits the range of uncertain demand, the range of uncertain service and the range of safety parameters to find the solution for siting emergency facilities, when the time and location of emergency events are unknown. The model is solved using a genetic algorithm (GA) and real data after abstraction. Finally, a comprehensive analysis of the solution results under different maximum overcoverages illustrates the feasibility and effectiveness of the model.

Using N-K Model to quantitatively calculate the variability in Functional Resonance Analysis Method

In this paper, we formulate the formation mechanism of variability of Functional Resonance Analysis Method (FRAM) based on the risk pulse theory for the first time. The coupling and interactions among human errors, mechanical failures, terrible environment and organization factors might cause the system state change, and cause the variability during the operation processes of the system. Based on the collected historical occurrence frequency of each risk factor and the determined coupling and interaction relationships among the risk factors, the N-K model is used to calculate the coupling risk intensity, which provides a quantitative method to evaluate the variability of the functional module. The more frequent the coupling times, the greater the probability of the coupling. The greater the coupling intensity, the greater the risk and the greater the occurrence probability of accidents. Compared with previous improvement works, the historical statistical frequency of each risk factor is need during the calculation based on N-K model, the results are not influenced by experts. A case study is conducted by taking a railway dangerous goods transportation accident in 2001 as the background. The results show that N-K model is able to calculate the variability of the functional module, quantitatively and effectively.

Railway dangerous goods transportation system risk identification: Comparisons among SVM, PSO-SVM, GA-SVM and GS-SVM

In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search). The three improved SVM approaches are applied to identify the risk of railway dangerous goods transportation system (RDGTS). The statistical occurrence frequency of each sub-risk indicator of the happened RDGTS accidents is used as the basis of experts’ scores, the experts’ scores are presented in interval numbers form, which are used as the inputs of the four approaches. The accuracy rate, optimization time consuming, Mean Square Error (MSE), Receiver Operating Characteristic Curve (ROC) and Area Under Curve (AUC) are used as the evaluation indexes of the identification results. The comparison studies are conducted by using SVM with linear kernel (SVM-L) and SVM with polynomial kernel (SVM-P), respectively. By using such new methodology, the risk identification problem (evaluation problem) is transferred into a classification problem with faster identification speed, higher identification efficiency and higher accuracy. The identification results show that: GS-SVM is the optimal approach to identify the risk factors of Human; SVM is the optimal approach to identify the risk factors of Machine, Materials, Environment and Management. SVM has the shortest optimization time consuming, GA-SVM has the highest accuracy, hence, SVM and GA-SVM are better to applied to identify the risk of RDGTS. The optimization time consuming of all models is no more than 5 s, which means the RDGTS risk identification results could be obtained fast and high-efficiently and the mental strength for researchers can be reduced by using the SVM and improved SVM models. For the risk identification results considering MSE and AUC, GS-SVM is the most accurate and best classification algorithm, and the results based on SVM-P is better than the results based on SVM-L. The results based on PSO-SVM, GA-SVM and GS-SVM have better accuracy and reliability than that based on SVM-L or SVM-P, which means the PSO-SVM, GA-SVM and GS-SVM based risk identification approaches are practicable.

Using a FMEA–TIFIAD Approach to Identify the Risk of Railway Dangerous Goods Transportation System

In this paper a systemic approach that combines Failure Mode and Effect Analysis (FMEA) and trapezoidal intuitionistic fuzzy information axiomatic design (TIFIAD) is proposed to identify the risk of railway dangerous goods transportation system (RDNGTS), which has seven steps: (1) identify potential risk factors and risk sub-indicators of RDNGTS; (2) define the failure criterion of severity of failures (S), portability of occurrence (O) and possibility of detection (D) of FMEA based on trapezoidal intuitionistic fuzzy numbers (TrIFNs); (3) invite experts to score the TrIFNs of S, O and D for each risk sub-indicator; (4) aggregate the TrIFNs of S, O and D for each risk sub-indicator; (5) calculate the TIFIAD of each risk sub-indicator; (6) calculate the total TIFIAD of each risk sub-indicator; (7) calculate the total TIFIAD of each risk factor. Compared with FMEA, FMEA–TIFIAD has the following improvements: (1) The TrIFNs are applied to score the S, O and D of each risk sub-indicator of RDNGTS; (2) The TIFIAD of the S, O and D are calculated for each risk sub-indicator, the product of the S, O and D could replace the Risk Priority Number (RPN) of FMEA; (3) The Entropy Weight Method is used to calculate the weight of each risk sub-indicator. The calculation results show that the potential human risk should be paid more attentions; the FMEA–TIFIAD is more reliable and accurate than FMEA. Finally the improvement suggestions for managers and operators of RDNGTS are given.

Historical data-driven risk assessment of railway dangerous goods transportation system: Comparisons between Entropy Weight Method and Scatter Degree Method

In this paper, two historical data-driven weight calculation approaches including Entropy Weight Method (EWM) and Scatter Degree Method (SDM), are applied and compared to solve the risk assessment of railway dangerous goods transportation system (RDGTS). The risk is defined as product of occurrence probabilities and their corresponding weights, the occurrence probability and weight of each risk sub-indicator is obtained by using the historical statistical data as the inputs. EWM aims at reflecting relative intensities among the evaluation indicators, however, SDM reflects the projection factor that maximizes the overall difference among the evaluated objects. In order to compare the performance of EWM and SDM, the stability analysis is carried out, which focuses on studying and analyzing the reverse degree order of the weight value when the initial matrix changed. In addition, correlation coefficient between occurrence probability and final assessment results based on the two approaches are conducted. The results of case study by using China data show that: the SDM is more stable than EWM; sub-risk indicators with highest ranks belong to risk factors of Human category, which means the Human unsafe actions and behaviors are the most dangerous factors that influence the normal and safe operations of RDGTS.

Railway dangerous goods transportation system risk analysis: An Interpretive Structural Modeling and Bayesian Network combining approach

In this paper, an Interpretive Structural Modeling (ISM) and Bayesian Network (BN) combining approach is applied to analyze the relationships and interaction strengths among the risk factors or accident causes of railway dangerous goods transportation system (RDGTS), quantitatively. According to the statistical data, 17 sub-indicators are concluded and applied as the nodes in BN. Based on the 17 sub-indicators and with the help of experts, the combined ISM and BN approach is used to analyze the system risk with seven steps: establish the relation matrix, calculate the reachability matrix, divide the reachability matrix into different levels, form the directed graph, form the Bayesian Network, establish the prior marginal and conditional probabilities of the sub-indicators in the BN, causal reasoning for the BN and obtain the final probabilities of occurrence for all sub-indicators. The conditional probability tables (CPTs) are used to express the relationships and interaction strengths among the nodes and the parent node variables. The final analysis results show that the sub-indicator, staffs are lack of technical and knowledge during transportation processes, has the highest impact to the RDGTS with probability 0.74, and the sudden natural disaster has the weakest impact to the RDGTS with probability 0.05.

Risk state changes analysis of railway dangerous goods transportation system: Based on the cusp catastrophe model

In this paper, the cusp catastrophe model is applied to analyze the railway dangerous goods transportation system risk state changes. Firstly, a Risk-Accident Catastrophe Tree is proposed to formulate the process that how the risk factors cause the accident. Secondly, the whole risk factors are classified into five categories including failure of human behavior, machine failure, transported materials, environment problems and management problems. Next, the cusp catastrophe model of railway dangerous goods transportation system is established, the Split Coefficient is defined to evaluate bifurcation set. The results of the case study show that the risk state changes of railway dangerous goods transportation system satisfy bimodality, inaccessibility, sudden transitions and divergence, but not satisfy hysteresis. The larger the Split Coefficient is, the easier the trajectory of system control point crosses with bifurcation sets. The reason of the system state changes from a safe state to a risk state is: as long as the trajectory of system control point crosses with bifurcation sets, there must be a cusp catastrophe in the system, and the system control point will cross the fold surface, which makes the risk energy increase sharply, the structure, information and energy of the system will also be destroyed.

Using an expanded Safety Failure Event Network to analyze railway dangerous goods transportation system risk-accident

In order to improve the reliability and safety of railway dangerous goods transportation system (RDGTS), and prevent the similar accidents happened again, an easier operated, dynamic, systemic and quantitative approach called expanded Safety Failure Event Network (SFEN) is proposed to analyze the past typical RDGTS accidents. The expanded SFEN focuses on transfer the traditional safety occurrence process into a visible Accident Analysis Network (AAN) platform. To improve the previous SFEN approach, the risk factors categories are expanded including Human actions, Technical failure, Nature of transported goods, Environment factors, Management failure and External factors of the system. An AAN is established by using the risk factors as the nodes and using the interactions among these risk factors as the edges, the RDGTS risk analysis problem is transferred into a quantitative network structure analysis problem from a network perspective. After that, based on the AAN, TouchGraph and NetMiner are applied to calculate and rank the centrality degree of each sub-risk factor (or sub-heading) in a network. A RDGTS accident happened in 2001 is analyzed, the results show that TouchGraph and NetMiner can present the same interactions and importance of sub-risk factors (or sub-headings) through visible circle images in the platform, NetMiner is more digital because the results can be presented as the centrality degree values. The greatest contributed sub-risk factors are Equipment maintenance failure and Railway inspection agency failure, followed by cargo packaging problems, illegal entrainment problem. Misbehaves of the freight inspector with centrality degree 0.523810 shows that this sub-heading has the greatest contribution to the accident.

Railway Dangerous Goods Transportation System Risk Assessment: An Approach Combining FMEA With Pessimistic–Optimistic Fuzzy Information Axiom Considering Acceptable Risk Coefficient

In this article, a new systemic risk assessment approach, which combines failure mode and effect analysis (FMEA) and pessimistic–optimistic fuzzy information axiom (POFIA) considering acceptable risk coefficient (ARC), is proposed to evaluate the risk of railway dangerous goods transportation system (RDNGTS). This approach transforms the system risk assessment problem into the rank problem of severity of risk factors affecting system security. The triangular fuzzy numbers (TFNs) are applied to score the severity of failures ( S ), portability of occurrence ( O ), and possibility of detection ( D ) for each RDNGTS risk subindicator. The information contents of S , O , and D are calculated for each risk subindicator, two models are applied to calculate the information contents: POFIA, and POFIA considering ARC (POFIA-ARC). The product of information contents of S , O , and D is used to replace the risk priority number of FMEA. entropy weight method is used to calculate the weight of each risk subindicator. The comparison among the FMEA-POFIA-ARC, FMEA-POFIA, FMEA, FMEA with TFNs is conducted based on the historical data of Chinese RDNGTS accidents from 1986 to 2017. Results show that the potential human risk should be paid more attention. Compared with the analysis results of statistical accident number, the results of the approaches proposed in this article (especially the FMEA-POFIA-ARC) are more reliable than the results of FMEA and FMEA TFNs combined approach.

Fault Tree and Fuzzy D-S Evidential Reasoning combined approach: An application in railway dangerous goods transportation system accident analysis

In order to manage the railway dangerous goods transportation system (RDNGTS) successfully, an explicit and effective previous accident analysis and accident control approach is essential and necessary. In this study a Fault Tree and Fuzzy D-S Evidential Reasoning combined approach is proposed to analyze the RDNGTS accident, which can solve the uncertainty modeling and information fusion problems existing in RDNGTS accident analysis. The approach has six steps: (i) identify causes of accident and calculate their weights based on Fault Tree, (ii) establish the Fuzzy Belief Structure model of causes of accident, (iii) handle initial qualitative and quantitative data, (iv) fuse the pre-processing data based on Fuzzy D-S Evidential Reasoning algorithm, (v) allocate Confidence Level of fuzzy intersection and, (vi) rank the final Fuzzy Belief Structure of each component based on trapezoidal fuzzy numbers and triangular fuzzy numbers. A historical lithium battery railway transportation accident happened in 2016 in China is applied as the background to examine the approach mentioned in this paper. The results show that professional skills and attitudes of transportation staffs are the weakest component in this lithium battery railway transportation accident. Managers of RDNGTS in China should pay more attentions to the professional skills and attitudes of transportation staffs. Some measures such as improve the awareness of safety and protection, train and examine the professional skills of transportation staffs, may be helpful in curbing the negligent working attitude of transportation staffs. Furthermore, the results also show that D-S Evidential Reasoning could provide a unified modeling framework for uncertain, incomplete, inaccurate and even ignorant information. It could solve the limitations in probability reasoning processes effectively.

A New System Risk Definition and System Risk Analysis Approach Based on Improved Risk Field

Risk is an objective quantity applied to describe the degree of harm to a specific system of many activities and technologies. In order to manage risk successfully and ensure the normal operation of a system, an explicit system risk definition and a system risk analysis approach are essential. After analyzing the system risk change curve, a new definition of system risk is given, which is formulated as the risk energy change in a system. A Gaussian pulse energy function is introduced to calculate the changed risk energy, and a new unit H is given to describe the system risk, where H formulates each unit energy change in the system or subsystem caused by the risk pulse. Based on the Gaussian pulse energy function, an improved risk field is modeled and described from two perspectives: risk potential and risk field strength. The characteristics of the improved risk field are modeled and simulated, including the superposition law of the risk potential, superposition law of the risk field strength and risk force, the flux and divergence of the risk field, and the ring flux and rotation of the risk field. The risk potential superposition law and the risk field strength superposition law provide a new system risk analysis approach to explain the formation process of system coupling risk and the interaction laws of the risk factors in a system. Finally, a real-world case study is conducted by taking a railway dangerous goods transportation accident in 2001 as a background. The simulation results of risk pulse energy show that the system risk energy value of the risk-accident critical state is between 1.712e-04H and 1.922e-04H. When the fourth risk pulse appeared, the system risk change curve crossed the risk-accident critical state line, and the accident happened. The simulation results of the risk potential and risk field strength show that risk source 1 has the biggest risk potential value and risk field strength value, followed by risk sources 1, 3, and 4, which means that risk source 1 has the biggest contribution to the accident, and it has the biggest risk impact strength to other matters.

A systematic railway dangerous goods transportation system risk analysis approach: The 24 model

In order to prevent accidents in the railway dangerous goods transportation system (RDNGTS), a 24 model is proposed to analyze the risk in this paper. 24 model is a human-oriented and organization-oriented risk analysis model. When this model applied, unsafe material conditions, unsafe human actions, individual factors, safety management system, safety culture and external factors should be analyzed one by one. After that, mutual influence path of the causes of the accident should be analyzed. Finally, the relevance among the four stages should be established and analyzed. The analysis results of the case study show that the accident happened because of the dynamic and nonlinear interaction among the risk factors. In actual operation, senior managers should focus on cultivating the safety culture of the company and establishing a complete security system. Middle managers should increase their training in emergency situations and related safety knowledge. The grassroots managers should focus on supervising the personal unsafe behaviors of front-line employees, supervising the unsafe physical conditions and increasing the safety participation level of front-line employees. Front-line employees should act in accordance with the rules and regulations, and should report to the grassroots management in time when there are any unsafe accidents. As a comparison, the FRAM also be applied to analyze the same accident. Finally, the contrastive analysis results of the two models are presented.

Discussion on Transportation Management of Railway Dangerous Goods

ABSTRACT In recent years, with the rapid development of China's economy, chemistry, atomic energy, electrical and petroleum industries have played a huge role in promoting the development of social economy. Industrial production in the increasingly use of dangerous goods as the production of raw materials, and in the production process to produce many toxic waste, enterprises in order to complete the normal production activities, it must be dangerous goods transport, and rail transport is one of the most The main form. According to a study conducted by the National Transportation Administration, 54% of the 3222 incidents involving chemical dangerous goods reported in 2010-2012 occurred in the production and storage process, and 41% occurred during transport, 5% due to other reasons. In all modes of transport, because the railway is the largest amount of traffic, the risk of rail transport seems to be the highest. However, taking into account the dangers of life and property, because most of the railway through the residential area, in the event of an accident, will endanger more people's lives, property safety, so the greater the risk of rail transport. Dangerous goods transport is a great threat to the lives and property safety of the people. Therefore, all countries in the world are actively trying to find ways and means to control and reduce the dangerous goods transport accident. In view of this, this paper mainly discusses the current railway sector in China in the transport of dangerous goods in the existing problems, in response to these problems, put forward the corresponding countermeasures. This paper is the use of normative research methods of the thematic research. The first part (the first part) elaborates the concept of dangerous goods and the concept of dangerous goods of railway; then (the second part), elaborates the dangerous goods transportation safety requirements and the dangerous goods transportation accident type; second (the third part Finally, (the fourth part), the corresponding measures are put forward to improve the safety management of railway dangerous goods transportation, to maintain the stable order of the society and to ensure the life of the masses of the people, so as to analyze the safety management problems in the transportation of dangerous goods in railway; Stability, while ensuring the safety of rail transport.

Safety Assessment of Railway Dangerous Goods Transportation Based on Set Pair Analysis

In order to overcome the drawbacks that occur when traditional safety assessment methods do not consider the certain and uncertain characteristics of information and in order to enhance the calculation efficiency, a new Identical Discrepancy Contrary (IDC) system is safety comprehensive assessment model based on Set Pair Analysis is established. Incorporating the Analytic Hierarchy Process (AHP) to obtain the index weight, this model analyses the uncertainties in the degree of connection acquired by assessment, and then makes the identical discrepancy contrary system research on the certainty and uncertainty of information in the engineering system.

Risk assessment model approach for dangerous goods transported by railway

With the development of industry, the amount of dangerous goods transported by railway increased yearly. In order to evaluate the risk to human lives, environment and property, in this paper, the characteristics of railway transport was discussed, then based on the traditional double index (probability and consequences) technology risk assessment model, a improved in-transit risk assessment model for railway dangerous goods was constructed, in which various consequences as casualties and property losses, transportation interrupt and the environmental pollution were considered comprehensively. By effectively evaluate the risk, references criteria will be provided for optimization of railway dangerous goods transportation.

Integrated optimization of railway dangerous goods station based on combined genetic algorithm

As one of the basic infrastructures of railway dangerous goods transportation, the location selection, construction scale and functional division reasonability of railway dangerous goods station are important to improve the railway dangerous goods transport efficiency, satisfy transportation requirements and control environmental risk. In order to solve the large-scale portfolio alternatives and the difficult calculation of multi-objective location-allocation model for railway dangerous goods station integrated optimization, a combined Genetic Algorithm ( GA) was used to optimize this issue. The simulation results show that this method is of fast convergence rate and stable operation, and it can effectively solve the related problems.