Benchmarking for a New Railway Accident Classification Methodology and Its Database: A Case Study in Mexico, the United States, Canada, and the European Union

Abstract

Rail accidents have decreased in recent years, although not significantly if measured by train accidents recorded in the last six years. Therefore, it is essential to identify weaknesses in the implementation of security and prevention systems. This research aims to study the trend and classification of railway accidents, as well as analyze public databases. Using the business management method of benchmarking, descriptive statistics, and a novel approach to the Ishikawa diagram, this study demonstrates best practices and strategies to reduce accidents. Unlike previous studies, this research specifically examines public databases and provides a framework for developing the standardization of railway accident causes and recommendations. The main conclusion is that the proposed classification of railway accident causes, and its associated database, ensures that agencies, researchers, and the government have accessible, easily linkable, and usable data references to enhance their analysis and support the continued reduction of accidents.

1. Introduction

The transport sector, a global cornerstone of urban planning and economic development [1,2], is witnessing a surge in the importance of trains as a primary mode of transportation [3,4,5]. Despite the concerted efforts of many nations to enhance road safety, the occurrence of traffic accidents persists [6]. An accident, defined as a sudden, unforeseen event leading to an undesirable outcome [7], can result in fatalities, severe injuries, property damage, and the release of hazardous materials [8].

Within this context, our research is dedicated to a comprehensive examination of the causes and trends of railway accidents across various countries over six years. Our primary goal is to discern commonalities and disparities in the classification of railway accident causes and to suggest tangible enhancements in safety management within this sector. By consolidating and contrasting public databases from Mexico, the United States, Canada, and the European Union, we aim to offer well-informed recommendations for the redesign of the classification of fault types leading to railway accidents, as well as a proposal for the enhancement of public databases. This proposal is designed to bolster the efficiency and reliability of information for the prevention and management of future accidents.

With these purposes in mind, it is crucial to analyze the data reported by various agencies. For example, the Rail Transport Regulatory Agency, ARTF, in Mexico reports 7858 railway accidents [9]; the United States Department of Transportation records 15,005 [10]; while the Transport Safety Board of Canada presents 8139 [11]; and the European Union Railway Agency reports 3555 [12]. All of these registered accidents occurred over a six-year period from 2017 to 2022. It is essential to mention that a comparative analysis of the number of accidents in each country is needed, as well as the number of accidents due to railway infrastructure. According to the report [13], the European Union has an active railway infrastructure of 200,099 km in length. The United States, on the other hand, has 148,553 km [14]. Canada has 48,150 km [13], and Mexico has 23,389 km of operated tracks [15].

Knowing the railway infrastructure and the number of accidents that each country registers, it is assumed that direct comparisons of absolute values between countries are not viable due to their different infrastructure extensions, as well as other factors such as operating speeds. However, a more feasible approach is to analyze the trend towards a decrease in accidents which can be carried out by processing the number of accidents registered per year in each country. The country with the most significant tendency towards a decline in accidents can be used as a reference when starting to redesign the database. The redesign of the public database will allow researchers and those responsible for railway safety to identify patterns in the data, facilitating decision-making and implementing a more significant number of accident prevention strategies.

The proposed redesign, with its potential to significantly enhance the accuracy of hazard source identification, aims to create a robust platform for further research. Recent studies [16] have used subway operation dispatch records and accident reports to extract risk chains and their evolution mechanism. The results showed that the identification of hazard sources was 10% more accurate, underscoring the potential of this redesign to address one of the key problems of the current research—the lexicon and content of the databases.

Furthermore, researchers [17] have underscored the urgent need for future research related to the development of new procedures for creating databases that contain clear and necessary information on accidents that occurred at highway–rail level crossings. The analysis of the accident and hazard prediction formulas used by the different state Departments of Transportation (DOT) in the United States highlighted the problems of availability and inconsistency of existing data, emphasizing the importance of their proposed research.

One of the methods used by agencies in different countries to objectively measure the rate and severity of accidents in rail transport is the development of safety and performance indicators. To generate these analyses, data related to rail traffic, severe accidents, causes, injuries, and fatalities are processed. In Vietnam [18], the problems in the analysis for accident prevention were analyzed. Agencies conduct studies on the number of accidents, but their causes still need to be located, recorded, and assessed. Therefore, the prevention and control of accidents and incidents cannot be accurately predicted, which increases the probability and severity of failures in future operations.

In Sweden, Jonsson et al. [19] estimated the accident risks and marginal costs of railway level crossings. Their objective was to determine to what extent the expected cost of accidents due to collisions between trains and road vehicles at a given crossing would change. The authors observed that, although they performed several sensitivity analyses, the estimated marginal costs could be sensitive to some issues, such as missing data, exclusion of accidents, and use of variables of little interest, being a limitation of the research. These findings have practical implications for the development of more accurate cost–benefit analyses in rail transport safety.

The trend in recent research is to predict accidents to reduce them significantly. Researchers [20] have employed automated solutions to extract, process, and analyze useful information from free-form text data to make systems more efficient, save costs, and increase system reliability and performance data.

Marcelo et al. [21] assessed the availability of railway risk data worldwide and developed an assessment framework to support consistent benchmarks. A total of 148 countries operating railways were analyzed. After studying the literature, they highlighted that one of the challenges when conducting this type of research is the transparency of railway safety data and international performance comparisons. Therefore, they proposed the following research gaps:

• There are no previous studies examining the availability of railway safety data worldwide.
• There is no standardized approach to assessing data transparency.
• Comparative research on railway data systems and safety across countries is lacking.

In summary, the literature review underlines the need for further standardization of the lexicon for classifying accidents, and the content of the databases is a limitation. The problem is that many railways do not have an adequate database of their accidents, making it difficult to obtain results from past events and improve future conditions. Furthermore, each country has its framework for recording accidents and providing safety information. Conducting a comparative analysis of public databases and proposing a redesign is a feasible strategy. This article proposes a potentially transformative redesign of accident classification using the Ishikawa cause–effect diagram and a restructuring of the database, taking as a starting point a comparative analysis of the different databases, since it is an effective strategy to identify and adapt the best practices in railway safety management [22].

Benchmarking is a tool that seeks the best practices of competitors by comparing techniques, processes and services to increase their efficiency and competitiveness [23,24]. At the research level, it is a valuable tool [25,26]. Rungskunroch et al. [27] demonstrated that benchmarking criteria could measure and compare the risks and uncertainties of any railway network. Another study [28] examined the effect of existing infrastructure improvement projects and provided relevant implications for the efficiency of public resource investment in infrastructure improvement. These studies demonstrate that benchmarking can be a valuable tool to improve efficiency and safety in the railway sector.

The scope of this research is limited to a comprehensive analysis of the causes and trends of railway accidents in multiple countries over six years. The purpose is to identify opportunities to improve railway safety management through the comparison and analysis of data from Mexico, the United States, Canada, and the European Union. The focus is on identifying similarities and differences in the classification of the causes of accidents, as well as on proposing concrete recommendations for the redesign of the classification and the databases involved, to improve the efficiency and effectiveness of preventive measures. In this article, we use the benchmarking method to compare public databases from different countries, thereby proposing the standardization of the causes of accidents and the structure of the databases. The methodology used and the stages of development of the research are shown in the following sections.

2. Methodology

Companies use the benchmarking method to understand the entire system [29], since it provides information for management to determine the following questions: Where was the business? Where is the company now? Where is the competition [23]? That is why it is used in this research. Benchmarking does not consist of copying the best practices but «learning and applying them by adapting, creating and redesigning our organization» [30].

The proposed methodology focuses on the analysis of the causes of railway accidents classified in several countries: Mexico, the United States, Canada, and the European Union. The objective is to identify patterns, similarities, and differences that can improve safety management in the railway sector. This innovative methodology combines comprehensive data collection with the use of analysis tools such as the Ishikawa diagram, making it unique in its approach to addressing rail transportation safety issues.

The Ishikawa diagram, known as the cause–effect diagram, is a visual tool used to identify and analyze the possible causes of a problem, that lead to undesirable results, to define stable and continuous solutions.

The benchmarking method was designed to research best practices, collect data, measure the performance gap, quantify targets, and develop action plans (Figure 1).

The methodology begins with the direct collection of railway accident data from official sources in the selected countries [9,10,11,12]. Afterward, the data is cleaned and analyzed to ensure comparability between databases. Additionally, a six-year trend approach is used to identify countries with consistent declines in accidents. Subsequently, the most frequent types of railway accidents are identified, and the causes are analyzed to find similarities and differences between them. This comparison allows for the proposal of a redesign in the final section of this study.

The innovation of the methodological proposal lies in two aspects:

• Use of the Ishikawa diagram for cause analysis: This methodology incorporates the Ishikawa diagram as an innovative tool to visualize and understand the underlying causes of railway accidents, allowing more precise identification of the factors involved in these events.
• Focused international comparison: The methodology is distinguished by its focus on comparing databases from specific countries, which facilitates the identification of best practices at a global level and their adaptation to local reality.

3. Results

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.

Companies use benchmarking to measure their success against other similar companies. Its objective is to analyze the competition to discover possible gaps for improvement and efficiency. Amid so much information and data, a study on the competition can be the key to understanding what is happening in the market and knowing where the companies are. That is why benchmarking allowed the authors to analyze the extensive databases provided by each country for its redesign.

The following sections present the adequacy of the databases downloaded from the countries evaluated to compare the past and current state of railway accidents registered in the last six years and their trend. In the same way, this paper intends to identify the root cause of the most repetitive accidents during this period and identify similarities and differences in classifying the causes of railway accidents.

Benchmarking made it possible to analyze the databases provided by each country and compare the current and past status of railway accidents, as well as the identification of patterns and differences in the classification of causes. This approach is directly linked to the presentation of the data and its subsequent analysis in terms of trends and similarities, which underlines the relevance of the methodology in obtaining results.

3.1. Database—Rail Accident Trend

The database redesign is proposed in the recommendations section because, when comparing Mexico, the United States, Canada, and the European Union, the following issues were detected: 1. Little information, with which analyzing the exact root cause of the rail accident was impossible; 2. Excess information; and 3. Duplicate data.

A good database allows for grouping and storing information according to the needs of the transport regulatory agencies or the researcher, avoiding redundancy and inconsistencies, and minimizing the possibility of errors in the information handling.

The information had to be verified and adjusted to compare the databases appropriately. The data cleansing process for rail accidents included the following:

• Removing invalid data sets.
• Matching documents released by rail authorities.
• Re-verifying missing data sets.

The objective for generating

Table 1. Accidents—Mexico.

Year	Total
2017	1491
2018	1180
2019	1312
2020	1298
2021	1391
2022	1186
Total	7858

,

Table 2. Accidents—United States.

Year	Total
2017	2421
2018	2703
2019	2754
2020	2316
2021	2352
2022	2459
Total	15,005

,

Table 3. Accidents—Canada.

Year	Total
2017	1339
2018	1461
2019	1500
2020	1218
2021	1236
2022	1385
Total	8139

and

Table 4. Accidents—European Union.

Year	Total
2017	218
2018	217
2019	208
2020	176
2021	126
2022	142
Total	1087

is to group all the information pertinent to the number of railway accidents registered between 2017 and 2022. Table 1 presents a summary of railway accidents recorded yearly in Mexico; Table 2 in the United States; Table 3 in Canada; and Table 4 in the European Union.

Bar graphs are presented in Figure 2, Figure 3, Figure 4 and Figure 5 to understand the trend for accidents registered in the six years evaluated, and to analyze the trend from the past to the current state, as proposed by the benchmarking method.

In Mexico, Figure 2, although there was a decrease in accidents from 2021 to 2022, it is observed that, in the first year, 2017, there were a large number of accidents; the following year, they decreased, and in 2019, they increased again, having successively the same trend. In the United States, Figure 3, in the bar graph a decreasing trend is observed compared to 2018 and 2019, but in 2022 it increased. Canada, Figure 4, shows a trend very similar to that of the United States but not as sharp. The European Union, Figure 5, shows a constant trend line in the decrease in accidents; at the beginning of the 2017 evaluation period a more significant number of railway accidents was observed, and their decrease was constant.

3.2. Database—Causes of Railway Accidents

This section describes the data cleaning and adjustment process to guarantee comparability between the databases of the evaluated countries. Although data cleaning methods are not inherently innovative, their meticulous implementation and relevance to ensuring data quality and consistency are critical aspects of this research. This rigorous approach to data processing contributes to the reliability of the results and allows for an adequate comparison between the different contexts evaluated.

Table 5. Classification of accidents in Mexico.

Railway Accident	Total
Group I. Level Crossings	3512
Vehicle run over	3392
Impact to train	120
Group II. Railway equipment, infrastructure, and operation	3390
Affectation of the passage of trains by CFE cables	2
Collision	29
Settlement/Embankment	3
Needle rail	4
Shock	118
Technical shock	3
Damaged confinement	2
Damage to facilities	15
Air hose decoupling	2
Natural disaster	2
Train derailment	2509
Landslide	166
Equipment in bad condition	28
Violations (internal transport regulations)	1
Fire	83
Flood	76
Road objects	144
Others	1
Check loss to direct track of shift needle	1
Loss of energy in catenary	4
Loss of potential	2
Friction	165
Road in bad condition	30
Group III. Death, injury, and others	883
Railroad personal accident	9
Corpse on the railway track	181
Person run over	693
Group IV. Hazardous waste	73
Explosive device	1
Leaks/Spills	72
Grand total	7858

,

Table 6. Classification of accidents in the United States.

Railway Accident	Total
Broken train collision	35
Derailment	8218
Explosion–detonation	1
Fire/violent rupture	281
Head on collision	43
Highway–rail crossing	1452
Obstruction	680
Other (describe in narrative)	912
Other impacts	2348
Raking collision	188
Rear end collision	137
RR grade crossing	5
Side collision	705
Grand total	15,005

,

Table 7. Classification of accidents Canada.

Railway Accident	Total
Collision involving track unit	175
Component failure that affects safe operation of r/s	5
Crew member incapacitated	10
Crossing	918
Derailment involving track unit	122
Derailment involving track unit (no damage)	34
Employee	62
Explosion	1
Fire on railway right-of-way	322
Fire onboard r/s	247
Main-track switch in abnormal position	58
Main-track train collision	31
Main-track train derailment	477
Main-track train derailment (no damage)	14
Movement exceeds limits of authority	793
Non-main-track train collision	513
Non-main-track train collision (no derailment, no damage)	11
Non-main-track train derailment	3088
Non-main-track train derailment (no damage)	414
Passenger	15
R/s coll. with abandoned vehicle	30
R/s coll. with abandoned vehicle (no derailment, no damage)	3
R/s coll. with object	209
R/s coll. with object (no derailment, no damage)	17
R/s damage without derail./coll.	43
Signal less restrictive than required	11
Trespasser	399
Uncontrolled movement of r/s	87
Unprotected overlap of authorities	30
Grand total	8139

and

Table 8. Classification of accidents European Union.

Railway Accident	Total
Accidents to persons caused by RS in motion	69
Broken axles	2
Broken rails and track buckles	1
Broken wheels	4
Broken wheels or axles	1
Electric shock	8
Fire in RS	56
Infrastructure event	3
Level crossing accident	169
Level crossing event	7
Level crossing near miss	5
Operational event	5
Other	15
Other events	35
Railway vehicle movement event	3
Rolling stock event	4
Rolling stock events	2
Runaway	24
SPAD	70
Train collision with a technical device	1
Train derailment	389
Trains collision	118
Trains collision	2
Trains collision near miss	9
Trains collision with an obstacle	43
Unauthorized train movement other than SPAD	33
Wrong-side signaling failure	1
Wrong-side signaling failure	8
Grand total	1087

show the groups of railway accidents registered in the six years evaluated in Mexico, the United States, Canada, and the European Union, respectively. The list only presents indicators comparable to each other, given the available information and the similarity of the concepts used during the analysis.

The importance of the tables lies in being able to visualize which accidents are recurring in the evaluated countries and identify coincidences with each other. With this guideline, users can prioritize maintenance action and prevention studies. These tables are the basis for restructuring the accident classification.

As can be seen, the two most frequent accidents in the evaluated countries are those of derailment and those caused by external people. The standardization of accident classification is necessary to avoid different descriptions for the same event. One of the main concerns facing the railway industry is level crossing safety, as seen in the tables above. These accidents occur on highway segments intersecting a railroad at the same elevation. Every year, there are many collisions between trains and road vehicles at level crossings.

Figure 6 presents a bar graph of the frequency of railway accidents recorded over the six years analyzed. The bar graph facilitates a visual understanding of the trends and classification of accidents and also highlights the most common types of railway accidents in Mexico, the United States, Canada, and the European Union.

The United States and Canada have a higher number of accidents and classification categories, including different types of collisions. The United States has the highest number of reported accidents, followed by Canada and Mexico, while the European Union has a lower incidence. As can be seen, the variation in the classification of accidents underscores the urgent need for accurate comparisons between regions.

4. Recommendations

Database restructuring is proposed to overcome challenges, such as lack of information, excess data, and duplication, to improve the comparison and analysis of the causes of railway accidents. The tables and graphs presented previously show a clear visualization of the trends and patterns of recorded accidents, which constitutes an innovation in the presentation of the data, facilitating its understanding, and highlighting the similarities and differences between the countries evaluated.

Section 4 is divided into two subsections: the first shows the redesign of the causes of railway accidents using an Ishikawa diagram; the second presents a proposal for restructuring the databases. This is to allow researchers, agencies, and government to understand the causes of railway accidents in a more visual and standardized way, as well as the restructuring proposal covering everything from maintenance to the prevention of railway accidents.

4.1. Redesign—Causes of Railway Accidents

The redesign of the components and classification of the type of accidents of the new database is carried out through the visual analysis of the Ishikawa cause–effect diagram [32], in which the root cause of the accident is not analyzed. It allows the authors to combine the classification of accidents from Mexico, the United States, Canada, and the European Union, using the idea of sub-groups used by Mexico [33]. A critical aspect that is not shown in the public databases of the countries studied, but can cause serious accidents such as deshunting [34], derailment, wear and fatigue in railway wheels and rails, is contamination by sand, rust, leaves, snow, grease [35].

The classification and prioritization of accidents are critical elements in correctly managing safety and prevention. It is necessary to provide the people in charge of updating public databases with the appropriate tools to report and inform about the causes of the accident. When investigating an accident, the aim is to determine its original causes to propose preventive or corrective measures to eliminate them and thus prevent their recurrence.

The importance of redesigning the classification and sub-classification of the types of railway accidents lies in that there is only one criterion for the classification of accidents. This would allow for more specific inspections and the design of new prevention systems.

Benchmarking is an excellent strategy to redesign the methodology for classification, registration, analysis, and prevention of accidents, since it is not required to start from scratch; it is a methodology that starts with comparative analysis. The most important thing is to understand the objectives of the databases of the countries evaluated and, with the knowledge acquired, carry out the necessary redesigns that allow the information to be standardized without additional or repetitive information. This classification allows for evaluation of the accident from the root cause and prediction of the accidents, thus avoiding the economic spill, damage to the environment, and the living beings affected by the accidents.

This study groups all railway risks to classify them according to their general description or primary definition (Figure 7). It is necessary to understand accidents in a more specific way because this leads to the evolution of the sustainability of rail system safety policies.

The classification was made in six groups to efficiently detect which aspect shows deficiencies in their safety and prevention systems. The classification highlights whether the error is from the personnel working on the trains, external people, the railway’s maintenance problems, the train itself, or if a natural disaster or crime is the cause.

The main objective of standardizing the names of the causes of accidents is to improve and optimize the databases. This assumes that the exact definition allows for a more effective resolution of the problem. In addition, its purpose consists in the formulation of conceptualization that allows for a more outstanding order in the content and better interpretation.

4.2. Redesign—Database

With globalization and the information systems used daily, the amount of information and data available is immense. The problem is knowing how to manage so much data, collect it, treat it, classify it, and apply it. The standardization of databases can be of great help. A good database allows agencies, governments, and researchers to compare and consult the data they need, ensuring uniformity in how they find the information. This will enable more profound studies to be carried out on the root cause of accidents, points for improvement, and prevention methods.

That is why, in this research, a database redesign is proposed. Figure 8 shows the section for maintenance checks based on prior knowledge of the preventive and corrective maintenance carried out and their respective observations. Subsequently, the database section of the economic, environmental, material, and human effects caused by railway accidents is generated, as shown in Figure 9, which is then used to justify the prevention investment, as shown in Figure 10. The proposed sections will allow agencies, researchers, and the government to evaluate the background to design an accident prevention proposal, as well as to follow up on implementation and mainly assess the effect of railway accidents on society.

It is essential to mention that the ideas in the sections of the database proposal were made with the comparative study of the evaluated countries: Mexico, the United States, Canada, and the European Union.

The database manager or administrator can record preventive and corrective maintenance, as shown in Figure 8. This section gives you the complete history of maintenance actions, the frequency of intervention, and relevant findings. Analysts can predict damage to the railroad and its components by applying statistical methods and artificial intelligence to this section. Predictive actions will allow future diagnosis of the train components’ behavior and the failures that may cause accidents.

After analyzing preventive and corrective maintenance, an excellent way to encourage investment in preventing railway accidents is to analyze the effects on the economy, environment, and living beings. Figure 9 shows the relevant sections. The analysis can estimate the economic and human impact caused by a train accident. Also, it will allow researchers to determine the effect on immediate and future environments.

Figure 10 allows for knowledge of the history of accidents by the agency responsible for the trains and the tracks affected by railway accidents to propose preventive measures. It is a way to obtain complete information from the study carried out, to identify the cause of the accident and the activities committed and carried out to prevent accidents of a specific nature.

Many of the databases for public use only contain the record of the factors that influenced the accident. Still, it is essential to identify the agencies responsible for the trains and the roads used, the complete analysis, and the root causes.

With the complete record in the databases, we can precisely identify the root cause of the accident and propose preventive measures.

Figure 11 displays the non-controllable factors, which are the variables that do not depend on the control of the railway agencies or the conditions of the track or the train, such as weather conditions or accidents caused by external people. This comprehensive database reassures us that we have considered all possible factors in our safety analysis.

Figure 12 shows the controllable factors recorded during the accident; this particular classification depends entirely on the track and train managers. Examples include the speed of the train, the track conditions, the weight of the train, etc. The complete information in the databases allows for the prediction and prevention of railway accidents.

Upon restructuring, the database is divided into non-controllable and controllable factors to highlight whether the causes are external to the regular operation or caused by problems with the train, the track, or the personnel working in the agency. The first, Figure 11, are those caused by external operation and maintenance factors. These affect the operation of the train and cause accidents. They range from environmental changes to external people who drive the accident by trying to overtake the train or who are under conditions of alcoholism and end up colliding or causing a derailment.

A controllable factor (Figure 12) is a condition that can be monitored, such as the speed at which the train circulates, the kilometers it has traveled, the weight it spreads, the condition of the wheels, etc. Therefore, its proper functioning depends on those responsible for the train and the track.

The non-controllable factor corresponds to the elements of the environment over which the person responsible has no possibility of operating, which affects the proper functioning of the trains.

Controllable and non-controllable factors make it possible to determine the root cause of the accident and apply appropriate preventive measures. The sections for recording controllable and non-controllable factors perfectly complement the corrective and preventive maintenance record.

Having complete databases, separated by sections and with well-defined labels, allows agencies and researchers to correlate sections of interest. By accessing the data, artificial intelligence techniques, deep learning, supervised learning, neural networks, and statistical analysis can be applied.

5. Discussion

Researchers, such as [36], have used benchmarking to measure railway safety data systems and performance, detecting that the lack of information in the databases is an essential limitation of the data systems of some countries. This shows the importance of their restructuring. The lack of specific measures of exposure to traffic, such as vehicle miles and the number of level crossings, limits the resolution of the evaluation and comparison of railway safety performance. This also limits researchers from different countries who need access to complete information on registered accident data, preventive measures, and many sections that could support more feasible and usable investigations. The authors consider that the investigations carried out by external people could be taken as support for the agencies.

On the other hand, the authors of [17] used content from the Federal Railroad Administration (FRA) database to obtain the Florida priority formula that evaluated the potential danger of a certain level crossing between a highway and a railway. It can be highlighted that they proposed that other states of the same country review the formula obtained to implement it. Here, it is observed that many times, the implementation of a new method, equation, algorithm, etc., involves adaptation since the accidents or the structure system is different, thus verifying that the restructuring and standardization of the databases and classification of accidents will allow the results obtained from other investigations to be applied more efficiently.

Kaewunruen et al. [37] proposed a risk analysis based on the railway diversion systems under the uncertainty of all climatic and environmental conditions. This study was made with information from 18,000 U.S. reports, thus verifying that the content from different databases was feasible.

Recent research by [21] highlights the relevance of restructuring public railway accident databases since it mapped 148 countries that supposedly operate a working railway, covering six years between 2015 and 2020, concluding that 107 countries obtained a zero data availability index.

In this research, we propose an innovative restructuring of public databases on railway accidents based on a benchmarking methodological model. This restructuring, organized into five essential sections, is designed to comprehensively evaluate the root causes of accidents, with a strong focus on the prevention and mitigation of future incidents. The structure not only enables detailed analysis of various factors but facilitates the implementation of emerging technologies and advanced methodologies, such as artificial intelligence and big data. This proposal, with its strategic sections, promises to significantly enhance the capacity for analysis and response to railway accidents, leading to the development of new public policies aimed at security.

• Preventive and corrective maintenance: This allows for identifying if there was a history of failures before the accident. It provides a deep database that could be used in the future to apply artificial intelligence techniques to estimate the remaining useful life [38,39] of critical components.
• The ‘Effects of the accident’ section of the proposed database plays a crucial role in promoting conscious and committed decision-making by railway agencies and governments. By quantifying the economic, material, environmental, and human losses, this section creates a sense of urgency and encourages investment in prevention programs and projects. This practical approach to presenting the negative impacts of accidents is a key feature of our proposal.
• Prevention: This section provides a framework for continuous monitoring and evaluation of preventive policies, which is crucial to maintaining high standards of railway safety.
• Non-controllable factors: Considering external and uncontrollable factors, such as sudden environmental changes, allows for a broader view of the risks associated with railway operations. This approach is vital to develop mitigation and resilience strategies in the face of unforeseen circumstances.
• Controllable factors: This section focuses on elements that are directly under the control of railway agencies, such as signaling, track conditions, and train failures. Documenting these factors in detail allows for identifying patterns and specific causes of failures, facilitating the implementation of effective and timely corrective measures.

The proposed structure is not only innovative for its comprehensive and systematic approach but for its adaptability to the advancement of data analysis technologies and methodologies. By integrating different structures, such as prediction and prevention, we not only promote continuous improvement but establish a solid foundation for the development of new public policies aimed at security. This adaptability ensures the long-term relevance and effectiveness of our research.

The development of a standardized database and classification according to its content is relative to safety issues; on the one hand, it constitutes a key factor for the railway company, since it allows for the constant improvement of the management systems adopted and applied (control of the effectiveness of the systems and application of solutions that minimize the risk of accidents or failures). On the other hand, for the government entities in charge of the railway sector, this information contributes to developing verification and supervision policies that help to evaluate the infrastructure continuously, minimizing risks.

The standardization of railway accident classification is justified by the differences observed in the databases studied. Figure 6, which shows the most common accidents in Europe, Canada, and the United States, illustrates this problem: derailments, for example, are recorded under different names, making a global comparative analysis difficult. This lack of uniformity limits the identification of trends and the implementation of effective prevention strategies. The restructuring proposal seeks to solve this problem, allowing more robust analyses that facilitate the identification of patterns, the prediction of risks, and the planning of more efficient preventive maintenance.

The restructuring of the database presents a standardized classification of accidents, as well as specific content. The detailed and standardized content allows for the adaptation of monitoring systems (track condition, signaling, rolling stock) [40]. By identifying patterns and risk areas, corrective and preventive actions for railway accidents can be proposed. The analytical capacity of the database will allow the use of new technologies and algorithms, contributing to safety in the railway system.

The database must present precise information, not only on the number of accidents registered but on the analysis of the accident and the specification of the root cause. Knowing the root of the problem, a solution can be proposed, and the responsibility for implementing the improvements determined. Governments and agencies can pursue continuous improvement strategies with pre-specified information, just as manufacturing and service industries do. Researchers can carry out accident prediction and prevention studies with accurate data, providing information that significantly impacts society.

6. Conclusions

The comparative analysis carried out in the research reflects the need for standardization in the classification of accident causes. The lack of uniformity in the recording and classification of data prevents the realization of robust comparative analyses at an international level, making it difficult to identify trends and effectively implement prevention strategies.

Analysis of the data shows significant variations in accident trends and causes across the countries studied. Similarities are observed between accident typologies, and heterogeneity in recording methods prevents a definitive interpretation of long-term trends. Some recurring patterns include level crossing accidents and derailments, but further standardization is needed to identify underlying causes more accurately.

The proposed database redesign, based on the Ishikawa diagram and the differentiation between controllable and non-controllable factors, is crucial to improving railway safety management. Detailed information on the causes of accidents and their economic and social impact will enable agencies, researchers, and governments to make informed decisions and develop more effective prevention policies. The standardization of information will also facilitate a more accurate analysis of trends and causes, allowing priority areas to be identified for improving railway safety.

Understanding the complexity and usefulness of different databases is extremely important because they can be used to predict accidents and develop prevention programs. The benchmarking method allows researchers not to start generating the database from scratch but rather to benchmark the trend of recorded railway accidents in order to assess their effectiveness. Standardizing the classification of railway accident causes, and their database allows for agencies, researchers, and governments to be certain that they will have useful data at their disposal that is easily linked and usable for any required activity.

This article has carried out a comparative analysis of public databases of railway accidents in Mexico, the United States, Canada, and the European Union, using an innovative methodology that combines benchmarking with Ishikawa diagram analysis. There was a need to standardize the classification of accident causes, and the similarities and differences in accident typologies were identified.

It is essential to consider certain limitations. The heterogeneity and incompleteness of the information, as well as the complexity of comparing the causes of accidents between regions with different infrastructures and operating systems, restrict the ability to generalize the results. The incorporation of additional databases and other operating conditions in various countries could enrich this study. The proposal for redesigning the database presented in this document offers a valuable framework for improving railway safety management at an international level, facilitating the comparison of data, the analysis of trends, and the implementation of more effective prevention policies.