High-speed rail safety analysis based on dual-weighted complex network

This study uses a complex network model to analyze the causes of accidents in high-speed railway operations. By identifying the key factors that led to high-speed railway accidents, hidden safety hazards were discovered. This will help improve the operational safety of the U.S. high-speed rail line under construction. The analysis uses the regional high-speed railway network in Guangzhou, China as a case study, including the railway (including high-speed railway) accidents that occurred in the company\u27s jurisdiction from 2013 to 2017. With comparative analysis between general railways and high-speed railways, the changes of high-speed railway safety factors are explored. Data analysis results show that the main accident causes of high-speed railways and general railways have no significant differences in categories, Equipment and human factors are the most important categories of factors leading to accidents. However, there are obvious differences in specific accident factors. Which include the significant impact of driver staff on the safety of high-speed railways, and the safety of high-speed railways is highly sensitive to incidents. Another key factor is the stability of the equipment, especially the performance of the signal system is critical to the operation of high-speed rail. The underlying reasons reflected by these safety defect factors include: In the short term, a large number of equipment purchases and the construction of new railway lines will cause maintenance, driver, and mechanic pressures and staff shortages. The lack of training system leads to insufficient professional quality of maintenance employees and drivers. The proposed strategy includes enhancing the training organization within the operating company, and adjusting the high-speed railway construction and equipment procurement policies should be gentler in order to reduce the pressure on the system and improve the level of safety

Use of DELPHI-AHP Method to Identify annd Analyze Risks in Seaport Dry Port System

The dry port concept has recently gained rising consideration in the multimodal transport context from the point of view both of researchers and stakeholders related to the benefits of the seaport dry port system. Given the relevance of the topic, the present paper aims to identify the potential risk factors of the three major parts that constitute the seaport dry port system and present a conceptual framework to facilitate risk factors analysis. Based on a three-step approach, starting with a systematic literature review, which resulted in 204 collected and examined papers, which allowed identifying 181 potential risk factors with an average of 60 risk factors in each major part of the studied system. In addition, we used a survey based on the Delphi technique to ensure a good extraction of data from 12 selected experts related to the seaport dry port system; then, we used the MCDM (Multiple-Criteria Decision-Making) method AHP (Analytic Hierarchy Process) in order to: 1) present a hierarchy that simplifies the complexity of the studied system in an organized structure; 2) analyze and assess risk factors based on the identified criteria. A case study involving the Moroccan seaport dry port system of Casablanca illustrates that the seaport part is critical and any major risk factor in this part can even paralyze the operations of the whole system, especially if that risk factor belongs to the human factors category or economic risk category, which is also considered in the study as a critical category

Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

An improved text mining approach to extract safety risk factors from construction accident reports

Workplace accidents in construction commonly cause fatal injury and fatality, resulting in economic loss and negative social impact. Analysing accident description reports helps identify typical construction safety risk factors, which then becomes part of the domain knowledge to guide safety management in the future. Currently, such practice relies on domain experts' judgment, which is subjective and time-consuming. This paper developed an improved approach to identify safety risk factors from a volume of construction accident reports using text mining (TM) technology. A TM framework was devised, and a workflow for building a tailored domain lexicon was established. An information entropy weighted term frequency (TF-H) was proposed for term-importance evaluation, and an accumulative TF-H was proposed for threshold division. A case study of metro construction projects in China was conducted. A list of 37 safety risk factors was extracted from 221 metro construction accident reports. The result shows that the proposed TF-H approach performs well to extract important factors from accident reports, solving the impact of different report lengths. Additionally, the obtained risk factors depict critical causes contributing most to metro construction accidents in China. Decision-makers and safety experts can use these factors and their importance degree while identifying safety factors for the project to be constructed

A data-driven conceptual framework for understanding the nature of hazards in railway accidents

Hazards threaten railway safety by their potential to trigger railway accidents. Whilst there are a considerable number of prior works investigating railway hazards, few offer a holistic view of hazards across jurisdictions and time and demonstrate policy implementation due to the inability to analyse a large amount of safety-related textual data. The conceptual framework HazardMap is developed to overcome this gap, employing open-sourced Natural Language Processing topic model BERTopic for the automated analysis of textual data from Rail Accident Investigation Branch (RAIB), Australian Transport Safety Bureau (ATSB), National Transportation Safety Board (NTSB) and Transportation Safety Board of Canada (TSB) railway accident reports. The topic modelling depicts the relationships between hazards, railway accidents and investigator recommendations and is further extended and integrated with the existing risk theory and epidemiological accident models. Results show that each hazard in the railway system has different aspects and could trigger a railway accident when combined with other hazards. Each aspect can be partially or fully addressed by implementing hazard mitigation policies such as introducing new technologies or regulations. A case study of the application to the risk at level crossings is provided to illustrate how HazardMap works with real-world data. This demonstrates a high degree of coverage within the existing risk management system, indicating the capability of helping policymaking for managing risks with adequate accuracy. The primary contributions of the framework proposed are to enable a huge amount of knowledge accumulated for an intuitive policymaking process to be summarised, and to allow other railway investigators to leverage lessons learnt across jurisdictions and time with limited human intervention. Future research could incorporate data from road, aviation or maritime accidents

A Correlation Analysis of Construction Site Fall Accidents Based on Text Mining

Construction site fall accidents are a high-frequency accident type in the construction industry and have received extensive attention from accident causal factor analysis and risk management research, but evaluating the relationship between accident causal factors and unstructured texts remains an area in urgent need of further study. In this paper, an analysis method based on text mining was chosen to analyze and process the collected data of 557 investigation reports of construction site fall accidents in China from 2013 to 2019. First, the accident reports were preprocessed to identify six types and 28 causal factors of fall accidents; subsequently, the 28 causal factors were classified into critical causal factors, subcritical causal factors and general causal factors according to their document frequency. Then, the Apriori algorithm was used to analyze the correlation of construction site fall accidents. Finally, strong association rules were obtained between the accident causal factors and between the causal factors and the types of construction site fall accidents. The results showed that 1) insufficient safety technology training and untimely elimination of hidden danger in safe production were the most frequent accident causal factors in fall accident reports. 2) There were different degrees of strong and weak correlations among the causal factors of construction site fall accidents, among which the higher the importance was, the stronger the correlation. 3) There were strong potential laws between the causal factors and the types of fall accidents, and the combination of some causal factors was most likely to lead to the occurrence of the corresponding accident types. This study scientifically and logically elucidated the inherent risk factors for fall accidents, which provides a theoretical basis for preventing fall accidents in construction projects

Natural Language Processing Using Neighbour Entropy-based Segmentation

In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018

A scoping literature review of natural language processing application to safety occurrence reports

Safety occurrence reports can contain valuable information on how incidents occur, revealing knowledge that can assist safety practitioners. This paper presents and discusses a literature review exploring how Natural Language Processing (NLP) has been applied to occurrence reports within safety-critical industries, informing further research on the topic and highlighting common challenges. Some of the uses of NLP include the ability for occurrence reports to be automatically classified against categories, and entities such as causes and consequences to be extracted from the text as well as the semantic searching of occurrence databases. The review revealed that machine learning models form the dominant method when applying NLP, although rule-based algorithms still provide a viable option for some entity extraction tasks. Recent advances in deep learning models such as Bidirectional Transformers for Language Understanding are now achieving a high accuracy while eliminating the need to substantially pre-process text. The construction of safety-themed datasets would be of benefit for the application of NLP to occurrence reporting, as this would allow the fine-tuning of current language models to safety tasks. An interesting approach is the use of topic modelling, which represents a shift away from the prescriptive classification taxonomies, splitting data into “topics”. Where many papers focus on the computational accuracy of models, they would also benefit from real-world trials to further inform usefulness. It is anticipated that NLP will soon become a mainstream tool used by safety practitioners to efficiently process and gain knowledge from safety-related text

Accident Analysis Methods and Models — a Systematic Literature Review

As part of our co-operation with the Telecommunication Agency of the Netherlands, we want to formulate an accident analysis method and model for use in incidents in telecommunications that cause service unavailability. In order to not re-invent the wheel, we wanted to first get an overview of all existing accident analysis methods and models to see if we could find an overarching method and commonalities between models. Furthermore, we wanted to find any methods that had been applied to incidents in telecommunication networks or even been designed specifically for these incidents. In this article, we present a systematic literature review of incident and accident analysis methods across domains. We find that accident analysis methods have experienced a rise in attention over the last 15 years, leading to a plethora of methods. We discuss the three classes in which they are often categorized. We find that each class has its own advantages and disadvantages: an analysis using a sequential method may be easier to understand and communicate and quicker to execute, but may miss vital underlying causes that can later trigger new, similar accidents. An analysis using an epidemiological method takes more time, but it also finds underlying causes the resolution of which may prevent accidents from happening in the future. Systemic methods are appropriate for complex, tightly coupled systems and executing such a method takes a lot of time and resources, rendering it very expensive. This will often not be justified by the costs of the accident (especially in telecommunications networks) and it will therefore be too expensive to be employed in regular businesses. We were not able to find any published definitions of structured methods specific to telecommunications, nor did we find any applications of structured methods specifically to telecommunications