Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Ratchanee Joomjee; Momthicha Raksin; Yanitha Paengprakhon; Jaruporn Duangsri; Niruwan Turnbull

doi:10.12688/f1000research.139489.1

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Research Article

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

[version 1; peer review: 2 approved with reservations]

Ratchanee Joomjee¹, Momthicha Raksin¹, Yanitha Paengprakhon¹, Jaruporn Duangsri¹, Niruwan Turnbull ^2,3

Ratchanee Joomjee¹, Momthicha Raksin¹, [...] Yanitha Paengprakhon¹, Jaruporn Duangsri¹, Niruwan Turnbull ^2,3

PUBLISHED 11 Mar 2024

Author details Author details

¹ Faculty of Public Health, Ubon Ratchathani Rajabhat University, Nai Mueang, Nakhon Ratchasima, Thailand
² Faculty of Public Health, Mahasarakham University, Maha Sarakham, Maha Sarakham, Thailand
³ Public Health and Environmental Policy in Southeast Asia Research Cluster (PHEP-SEA), Mahasarakham University, Maha Sarakham, Thailand

Ratchanee Joomjee
Roles: Conceptualization, Investigation, Writing – Original Draft Preparation

Momthicha Raksin
Roles: Resources, Supervision

Yanitha Paengprakhon
Roles: Formal Analysis, Investigation

Jaruporn Duangsri
Roles: Methodology, Project Administration, Software

Niruwan Turnbull
Roles: Project Administration, Validation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the QUVAE Research and Publications gateway.

Abstract

Introduction

The importance of understanding the factors contributing to road accidents at schools cannot be overstated. This study aims to determine the risk of accidents in situations that could lead to accidents near schools.

Methods

A total of 10 kindergarten to junior high schools were selected for the study. The research used the Haddon Matrix to classify factors at risk of accidents, risk assessment through fault tree analysis, and the analytic hierarchy process (FTA-AHP) techniques. Fourteen sub-criteria were defined for risk identification, risk probability analysis, and risk assessment of the 10 roads. The likelihood of each event was analyzed using the AHP technique for all schools with an expert choice program. RI (random index) was calculated, and CR (consistency ratio) < 0.10 was considered satisfactory.

Results

The possibility of human accidents ranked highest in three areas: 1) Risk perception in SC 01, 03, and 02, with probabilities of 69.30%, 61.90%, and 57.4%, respectively. 2) The likelihood of accidents from vehicles/equipment, with the highest probabilities in a) Handling (SC01) at 64.70%, b) Braking (SC07) at 61.90%, and c) Lighting (SC03) at 57.80%. 3) The likelihood of accidents from the environment, with the highest probabilities in 1) driving at excessive speeds in areas SC01, 06, 03, and 09, which were 43.60%, 40.90%, and 40.00%, respectively.

Conclusions

The impacts of all three main factors were as follows: a) humans had the highest impact in the SC01 area (77.90%), b) vehicles/equipment had the highest impact in the SC01 area (75.90%), and c) the highest environmental impact in the SC01 area was 69.90%. The accident risk assessment revealed the highest risk score in three areas: 1) human risk perception, 2) environment with driving at excessive speeds, and 3) vehicle/equipment, including lighting, braking, and handling.

Keywords

Risk assessment, School road, Traffic accident, (FTA-AHP) techniques, Analytic Hierarchy Process

Corresponding author: Niruwan Turnbull

Competing interests: No competing interests were disclosed.

Grant information: This research project was supported by the Ubon Ratchathani Rajabhat University, Thailand under grant (ID 2561A16402066).

Copyright: © 2024 Joomjee R et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Joomjee R, Raksin M, Paengprakhon Y et al. Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand [version 1; peer review: 2 approved with reservations]. F1000Research 2024, 13:187 (https://doi.org/10.12688/f1000research.139489.1) First published: 11 Mar 2024, 13:187 (https://doi.org/10.12688/f1000research.139489.1) Latest published: 11 Mar 2024, 13:187 (https://doi.org/10.12688/f1000research.139489.1)

Introduction

In 2018, 1.35 million people died due to crashes involving motor vehicles, most of which were young people between the ages of five to 29 years old, and the majority of victims were pedestrians, cyclists, motorcyclists, and animals. Most accidents occur in developing countries.¹^,² Accidents affect victims, both directly and indirectly. Direct effects include death, injury, loss of medical expenses, and property damage. Some indirect effects of road accidents include loss of income, changes in the mental health of the victim and those involved, and impact on the country’s business and society.³^,⁴ The global death toll from traffic accidents is increasing. Registration statistics indicate that vehicle incidents have increased annually.⁵ Accidents directly and indirectly impact victims, their families, society, and the nation, as well as injuries, medical costs, and road safety campaign budgets.³ According to the World Bank Country and Lending Groups, Thailand is a developing country with a middle-income⁶ economy, which ranks first in Southeast Asia and eighth in the world for road fatalities, with an average of 32.7 deaths per 100,000 residents.¹ Road environments can contribute to this tragedy, which kills young adults worldwide. Thailand has one of the highest accident rates worldwide.¹^,²^,⁷ There have been reports of road traffic injuries and fatalities among 12–14-year-old children in the southern region of Thailand, resulting in severe disability and mortality, especially among those with pediatric traumatic brain injuries (TBI).⁸ The most hazardous environment was rural roads with intersections. In rural areas, the accident rate is approximately 30%, 6% of all severe accidents.⁵

The Haddon Matrix principle was devised to investigate accidents and road traffic injuries. It was evaluated from three vantage points: pre-event, event, and post-event phases, as well as three factors:1) human variables, 2) equipment/vehicle variables, and 3) environmental variables.⁹ Fault tree analysis was used to analyze the risk of accidents to identify risk factors and the primary cause of public safety accidents by defining the potentially severe impact as the top event and identifying the fundamental event leading to a potentially powerful impact. For example, the evaluation of road accidents at the severity level that will impact catastrophe revealed the following risk factors:1) internal system failures, including battery failures, mechanical failures, and flight control system failures; 2) pilot factors, including unqualified knowledge and skills, poor safety awareness, violations, and lack of legal understanding; and 3) external environmental impacts, including obstacles, route planning issues, and unclear airspace. The Analytical Hierarchy Process (AHP) is also used to rank risky events to create a risk management plan that can be influenced by accidents.¹⁰^,¹¹ The Haddon Matrix method classifies the causes of road incidents into three factors: driver behavior (41%), road environment (26%), and condition of the vehicle (33%). Males constituted a more significant proportion of road traffic casualties (RTCs) than females (61.39 vs. 38.61, P <0.05). The male mortality rate was 63.23 percent, with an average age of 33.65 and 16.76 years. Secondary school students had the highest morbidity and mortality rate (40.25%). Male adolescents are approximately three times more likely to experience road accidents than females, and mobile phone use while driving is typical at this age.¹² The percentage of individuals injured everywhere on their bodies (16.57%), upper or lower bodies (17.84%), skulls (20.77%), and abdomen (15.75%) varied considerably.¹³ Road accidents are more common among drivers aged 17–25 years old. There is a correlation between severe accidents and excessive pace. Therefore, natural control measures exist. To prevent and reduce the number of casualties, stopping at intersections (T-intersections), controlling the road environment (e.g., the number of lanes, slope adjustment, and speed limit with a stop), traffic control devices, driver safety controls (age, gender, and speed), and prevention of accidents caused by natural disasters (e.g., providing weather forecasts and specifying the day and time of storms and rain)¹⁴ are needed.

This study investigates main road safety in Ubon Ratchathani Province, Thailand. A total of 17 Ubon Ratchathani town schools with routes to other districts were studied. It studied three pre-accident risk factors:1) in humans to analyze road user hazards to kindergarten and lower secondary students (6–15 years old). 2) Vehicle/equipment characteristics of cars that drive across the road in front of all ten schools. 3) How the school’s road environment may affect children’s ability to prioritize road risk mitigation actions. This study employed fault tree analysis and the analytic hierarchy process (AHP) to identify risk variables and road accident prevention techniques in the following order.

Objective

To assess the road accident risk factors for each event resulting in an accident on a school road.

Methods

The study design was approved by the Ethical Review Committee for Human Research, Ubon Ratchathani Rajabhat University (Study No. 015/2563-HE632027) on 7 March 2020. Written informed consent forms were completed by all participants.

Study design and sampling

A cross-sectional study utilized a checklist to effectively gather information for assessing the probability and risk of incidents on selected school main roads in Ubon Rattanathi, Thailand, from April to December 2020. We engaged stakeholders, including schoolteachers, community leaders, and parents of students, representing 17 schools. Our selection process focused on the main roads adjacent to those schools, involving 10 kindergartens to junior high schools in Ubon Rattanathi. There were 125 participants in the risk assessment model for road accidents near schools, and this assessment took place from April to December 2020.

The study was conducted in three phases. The initial phase, spanning from April to May 2020, involved data collection from three schools: Ban Dampra School, Ban Thabor School, and Ban Namean School. Subsequently, the second phase took place from June to July 2020, during which data was gathered from three different schools: Ban Khamyai School, Ban Tungkunnoi-Nongjan Wittaya School, and Ban Pladuk School. The final phase, covering September to October 2020, involved data collection from four schools: Ban Yang Lum School and Ban Nonglai School. Following the completion of the data collection phases, three months were allocated for data gathering, culminating in data analysis conducted in November and December 2020 (Figure 1).

Figure 1. Map of the study area.

Data collection for this study occurred at different times for various schools, potentially introducing temporal bias or seasonal variations. However, during data analysis, we addressed this concern by accounting for seasonal differences in traffic patterns and weather conditions. One potential issue with this study is selection bias since it focused on specific schools in the province of Ubon Rattanathi, Thailand, which may only be representative of regional schools. We selected schools with diverse characteristics and locations to minimize bias to ensure a more representative sample. We also addressed measurement bias by employing standardized incident reporting protocols and training personnel for data collection. To account for potentially confounding factors like traffic volume, speed limits, road conditions, and weather conditions, we used AHP with FTA-AHP analysis to adjust for their influence. Subgroup analyses were conducted to examine effect modifiers, such as whether the relationship between outcomes and exposure varied based on school type or other factors.

Inclusion criteria and exclusion criteria

Inclusion criteria included schools under the Ubon Ratchathani Primary Educational Service Area Office’s authority in high-risk areas and on main thoroughfares. Institutions not cooperating with the study during the research period were also excluded.

The instruments and data collection

The data collection instrument used in this study was derived from the Haddon Matrix, a highly acknowledged and influential framework in injury prevention. The matrix, developed by William Haddon in 1970,¹⁵ analyses a range of factors related to individual characteristics, features of the vector or agent, and environmental variables before, during, and after an injury or fatality. Hence, the risk assessment form successfully identifies three distinct elements. The three categories considered in this study were as follows: 1) human/student, in humans to analyze road user hazards to kindergarten and lower secondary students (6–15 years old), 2) Vehicle/equipment characteristics of cars that drive across the road in front of all ten schools, and 3) the school’s road environment may affect children’s ability to prioritize road risk mitigation actions. These categories are derived from the Haddon Matrix. All 14 accidents underwent risk probability analysis and risk assessment for 10 roads based on the following factors: age, playing on the road, use of mobile phones, risk perception, vehicle/equipment, roadworthiness, lighting, braking, handling, environment, clear traffic signs, expressways, sidewalks, bus stops, school gates, and driving at excessive speeds. These accidents with similar characteristics were identified in a few studies.⁵^,¹⁶^,¹⁷ Subsequently, a risk analysis was conducted, and risks were prioritized using the FTA-AHP technique in the following order.

We conducted data collection activities from 2020 to 2022 by using a risk assessment approach for road accidents around schools. This assessment consists of three parts, which are information about students, vehicles/equipment, and the environment. Subsequently, the assessment was tested on a pilot group and implemented in a pilot school. Data from the pilot test was collected and feedback was used to make improvements during February 2020. Then, the assessment tool that had undergone quality checks was sent to experts for review in March 2020, before using it for risk assessment with a sample of 10 schools in the next phase. The risk assessment model for road accidents near schools, which has undergone quality checks, was used to assess the risk in 10 schools, namely: 1) Ban Dampra School 2) Ban Thabor School 3) Ban Namean School 4) Ban Khamyai School 5) Ban Tungkunnoi-Nongjan Wittaya 6) Ban Pladuk School 7) Ban Yang Lum School 8) Ban Nonglai School 9) Ban Huakam School and 10) Ban Phakaew School. This process was carried out in collaboration with the Education Committee in each school, including community leaders, community management committees, and school management committees, totaling 25 people. Therefore, there were 125 participants in the risk assessment model for road accidents near schools, and this assessment took place from April to December 2020. The first group of three schools collected data within the time frame covering April to May 2020. These particular schools encompassed Ban Dampra School, Ban Thabor School, and Ban Namean School. The second cohort of three schools gathered information from June to July 2020. The schools under consideration included Ban Khamyai School, Ban Tungkunnoi-Nongjan Wittaya School, and Ban Pladuk School. The final group of four schools that gathered data during a time frame covering from September to October 2020 consisted of Ban Yang Lum School and Ban Nonglai School. Following this, three months were allocated for data collection, which was later subjected to analysis during November and December 2020.

Risk identification and FTA

Fault tree analysis (FTA) quantifies a qualitatively identified risk from a relevant primary system as the top event in the tree for deductive reasoning based on risk factor probabilities.¹⁶^,¹⁷ FTA has considered three factors that cause school road traffic accidents: 1) Human/Student, such as age 6-15 years old, playing on the road, using a mobile phone, and risk perception; 2) Vehicle/Equipment, such as roadworthiness, lighting, braking, handling, and 3) Environment, such as clear traffic sign, motorway, sidewalk way, bus stop, school gates, driving at excessive speeds), The hazard conditions in front of 10 schools within a two-kilometer radius were recorded (Figure 2).

Figure 2. Fault tree model for school hazard identified.

Risk impact with AHP

The values of each factor were categorized into five ranks (very low, low, moderate, high, and very high) according to the value ranges encountered for each layer. Subsequently, the weight for each factor was estimated based on Saaty’s AHP¹⁸^,¹⁹ (Table 1). The consistency index (CI) and the consistency ratio (CR) were calculated based on equation (1) in (Figure 3).¹⁸^,¹⁹

Table 1. Analytic hierarchy process (AHP) Scale for Pairwise Comparisons.¹⁹

Intensity of importance	Definition	Explanation
1-2	Equal importance	Two activities contribute equally to the objective
3-4	Moderate importance	Experience and judgment slightly favor one activity over another
5-6	Strong importance	Experience and judgment strongly favor one activity over another
7-8	Very strong or demonstrated importance	An activity is favored very strongly over another; its dominance demonstrated in practice
9	Extreme importance	The evidence favoring one activity over another is of the highest possible order of affirmation

Figure 3. The consistency index (CI) and the consistency ratio (CR) were calculated based on equations.

The likelihood of each event was analyzed using the AHP technique of all ten schools with an expert choice program, RI=random index, and CR≤0.10 was considered acceptable, and the weights calculated for risk school road areas were considered statistically acceptable.

Risk probability analysis with AHP with FTA-AHP

The Analytic Hierarchy Process (AHP) were impact (I) and probability (P) from the FTA equation, which was obtained from equations 3 and 4¹⁷ (Figure 4).

Figure 4. The Analytic Hierarchy Process (AHP).

Data processing and analysis

The data²⁹ were validated and analyzed using the AHP technique of all 10 schools with the Expert Choice program version 11 to determine the chance of each event (Open source alternative: DecisionBuilderTM, Minitab). RI=random index and CR<0.10 was regarded satisfactory.

The probability and impact rating of the risk assessment with a five-level risk matrix (Tables 2 and 3) were based on the event’s probability. There are four risk score levels: negligible (1 to 5), tolerable (6 to 9), undesirable (10 to 16), and intolerable (17 to 25) (Figure 5).¹⁷

Table 2. Probability score and impact score from analytic hierarchy process (AHP).

Event	Analytic hierarchy process (AHP) score of school
	SC 01		SC 02		SC 03		SC 04		SC 05		SC 06		SC 07		SC 08		SC 09		SC 10
	I	P	I	P	I	P	I	P	I	P	I	P	I	P	I	P	I	P	I	P
1. Human/Student
1.1 Age 6-15 years old	0.094	0.779	0.139	0.734	0.109	0.749	0.113	0.731	0.139	0.734	0.113	0.731	0.131	0.729	0.151	0.716	0.124	0.733	0.151	0.716
1.2 Play on the road	0.163		0.235		0.22		0.279		0.235		0.279		0.274		0.265		0.241		0.265
1.3 Using cell phones	0.05		0.052		0.052		0.05		0.052		0.05		0.044		0.075		0.065		0.075
1.4 Risk perception	0.693		0.574		0.619		0.558		0.574		0.558		0.551		0.508		0.571		0.508
2. Vehicle/Equipment
2.1 Roadworthiness	0.191	0.759	0.271	0.727	0.272		0.129	0.726	0.124	0.733	0.257	0.734	0.109	0.749	0.151	0.716	0.064	0.717	0.053	0.730
2.2 Lighting	0.059		0.064		0.578	0.792	0.549		0.241		0.062		0.052		0.508		0.505		0.297
2.3 Braking	0.103		0.122		0.234		0.248		0.571		0.11		0.619		0.265		0.288		0.549
2.4 Handling	0.647		0.544		0.116		0.074		0.065		0.571		0.22		0.075		0.143		0.102
3.Environment
3.1 Keep clear Traffic Sign	0.141	0.699	0.124	0.684	0.062	0.689	0.06	0.690	0.068	0.683	0.059	0.691	0.056	0.693	0.06	0.688	0.062	0.689	0.07	0.687
3.2 Expressway	0.274		0.063		0.096		0.088		0.107		0.249		0.297		0.149		0.252		0.257
3.3 Sidewalk	0.073		0.242		0.252		0.272		0.246		0.156		0.137		0.249		0.148		0.151
3.4 Bus Stop	0.03		0.158		0.148		0.143		0.17		0.092		0.081		0.105		0.096		0.098
1.5 School Gates	0.047		0.042		0.042		0.042		0.046		0.035		0.034		0.039		0.042		0.042
1.6 Driving at excessive speeds	0.436		0.371		0.4		0.395		0.363		0.409		0.396		0.397		0.4		0.382

Table 3. Risk probability and impact rating scores.

Probability scores	Probability level	Impact level	Probability/Impact
5	Very likely	Very high	>0.80
4	Likely	High	0.51-0.80
3	Possible	Moderate	0.31-0.50
2	Unlikely	Low	0.11-0.30
1	Very unlikely	Very low	<0.10

Figure 5. Risk scoring matrix.

Results

Probability and impact of each accident-causing incident on the school’s road.

Using the FTA-AHP technique to analyze the likelihood of incidents in 10 areas, the study determined that the likelihood of an accident from each of the three main factors (Human/Student, Vehicle/Equipment, and Environment) is as follows: 1) The highest three rankings from Risk Perception in area SC 01,03, 02,05 for the effect of an accident involving human/student were 69.30%, 61.90%, and 57.4%, respectively. The highest three vehicle/equipment rankings for the likelihood of an accident were

• 64.70 percent for Handling in the SC 01 area,
• 61.90 percent for braking in the SC 07 area, and
• 57.80 percent for lighting in the SC 03 area.

The likelihood of an accident owing to the environment was 43.60%, 40.9%, and 40% in areas SC 01, 06, 03, and 09, respectively. The combined probabilities of the three primary factors, SC 01, SC 03, SC 02, and SC 05, had the greatest effects on humans/students (77.90%, 74.90%, and 73.40%, respectively). 74.90%, 74.90%, and 73.40% of SC 01, SC07, and SC06, respectively, were affected by vehicles or equipment. The cumulative effect on the environment in sectors SC 01, SC 07, and SC 06 was 100% (69.90, 69.30, and 69.10, respectively) (Figure 6).

Figure 6. The probability of each event that will cause an accident is a road around the school.

FTA-AHP Risk Assessment Results

In 10 schools, Human/Student (Risk Perception) and Environment (Driving at excessive rates) had the highest risk score of level 3 (Undesirable 10–16). 1) Lighting zone SC 03 (Ban Namean School), SC 04 (Ban Khamyai School), SC 05 (Ban Tungkunnoi-Nongjan Wittaya), SC 08 (Ban Nonglai School), SC 09 (Ban Huakam School). 2) Braking zone SC 07 (Ban Yang Lum School), SC 10 (Ban Phakaew School). 3) Handling zone SC 01 (Ban Dampra School), SC 02 (Ban Thabor School), SC 06 (Ban Pladuk School) (Figure 7, Table 4).

Figure 7. FTA-AHP risk assessment results.

Table 4. The risk score level of the school.

Risk	Risk level of school
Risk	SC 01	SC 02	SC 03	SC 04	SC 05	SC 06	SC 07	SC 08	SC 09	SC 10
1. Human/Student
1.1 Age 6-15 years old	1	2	2	2	2	2	2	2	2	2
1.2 Play on the road	2	2	2	2	2	2	2	2	2	2
1.3 Using cell phones	1	1	1	1	1	1	1	1	1	1
1.4 Risk perception	3	3	3	3	3	3	3	3	3	3
2. Vehicle/Equipment
2.1 Roadworthiness	2	2	2	2	2	2	2	1	1	1
2.2 Lighting	1	1	3	3	3	1	1	3	3	2
2.3 Braking	2	2	2	2	2	2	3	2	2	3
2.4 Handling	3	3	2	1	1	3	2	1	2	2
3.Environment
3.1 Keep clear traffic sign	2	2	1	1	1	1	1	1	1	1
3.2 Expressway	2	1	1	1	2	2	2	2	2	2
3.3 Sidewalk	1	2	2	2	2	2	2	2	2	2
3.4 Bus stop	1	2	2	2	1	1	1	1	1	1
1.5 School gates	1	1	1	1	1	1	1	1	1	1
1.6 Driving at excessive speeds	3	3	3	3	3	3	3	3	3	3

Discussion

The risk assessment

For the 10 school road traffic accidents in 10 areas classified by the Haddon Matrix method, the risk levels of all three factors were at level 3 (undesirable). Depending on students and road users, it was found that for Human/Student, the highest probability was 77.90%, Vehicle/Equipment was 75.90%, and environment was 69.90% (found in area SC 01). The highest probability was found in 69.30% of risk perception, 64.70% of handling, and 43.60% of driving at excessive speeds, consistent with educational road traffic accidents, among the leading causes of injury and death worldwide. This resulted in a 100% risk of mortality and injury for road users, particularly for 81% of vulnerable road users and 36% of drivers. Accident-causing factors include human characteristics, vehicles, road characteristics, and environmental conditions, such as rush hour and school term time in the morning.²⁰^–²³

Transportation Research Information Services (TRIS)

Transportation Research Information Services (TRIS) found human behavioral factors to be at the highest risk of road accidents at 41%, environmental factors at 26%, and vehicle factors at 33% (5). Behaviors can contribute to the risk of accidents, such as not crossing the road on a pedestrian crossing or a designated route and crossing during heavy traffic. Risky environmental factors are infrastructures such as sidewalks, zebra crossing, pedestrian-protected space at the exit, two-way streets, protected routes/corridors for pedestrian children, and 30 km/h zones. We also found that the density of cars in road environments with speed driving is a major factor causing accidents and the severity of the accidents.²⁴^–²⁶ According to this study, lack of road user safety awareness (69.30%), driving at high speeds in school zones (46.30%), and utilizing cars in incomplete situations (e.g., damaged) had the most significant impact on road accidents. Inadequate lighting on roads and vehicles was identified as an essential factor in road accidents, accounting for 57.80% of all incidents. Poor visibility owing to insufficient lighting can lead to increased risk for drivers and pedestrians. Braking issues accounted for 61.90% of road accidents. Problems with vehicle braking systems, such as worn-out brakes or delayed response times, can lead to collisions and a reduced ability to avoid accidents. Handling-related factors contributed to 64.70% of road accidents. Difficulties in maneuvering and controlling vehicles, particularly in emergencies, can increase the likelihood of accidents.

Active school travel

Personal safety, traffic safety, distance to school, school factors, and social factors are essential for student traffic safety, and we have analyzed the risk of road accidents and established proactive preventive measures in the area to reduce the risk of traffic on the road, including legislation to regulate driving speed and response to road infrastructure. Safe crossing measures are essential to protect students while walking across the roads, especially in nearby schools. This includes providing pedestrian crossings and designated routes with proper signage and traffic signals. Educating students about the importance of using designated crossings and following traffic rules is crucial to instilling road safety awareness from a young age. Ensuring a safe road environment for motorcycles and bicycles involves several aspects. First, establishing dedicated lanes and routes for bikes and motorbikes can separate them from faster-moving vehicular traffic, reducing the risk of collisions.²³^,²⁷ In addition, the government should support road safety through public education. Personal safety, traffic safety, distance to school, school factors, and social factors are essential for student traffic safety, and we have analyzed the risk of road accidents and established proactive preventive measures in the area to reduce the risk of traffic on the road, including legislation to regulate driving speed and response to road infrastructure.

Safe crossing measures are essential to protect students while they crossroads, especially in areas near schools. This includes providing pedestrian crossings and designated routes with proper signage and traffic signals. Educating students about the importance of using designated crossings and following traffic rules is also crucial to instilling road safety awareness from a young age. Ensuring a safe road environment for motorcycles and bicycles involves several aspects. First, establishing dedicated lanes and routes for bikes and motorbikes can separate them from faster-moving vehicular traffic, reducing the risk of collisions.²³^,²⁷ In addition, the government should support road safety through public education. Strict enforcement of the law, especially regarding driving faster than the law, and improving the road environment is considered safer to ensure road user safety.²⁸

Conclusion

The FTA-AHP technique was selected for the study on the school’s main highways in the Ubon Ratchathani Province of Thailand. In terms of human/student (Risk Perception), environment (driving at excessive speeds), and vehicle/equipment (Lighting, Braking, Handling), the highest risk score was level 3. The area surrounding SC01 had the highest accident probability for all 10 schools owing to risk perception (69.30%), handling (64.70%), and driving at excessive velocities (43.60%). Human/student (77.90%), vehicle/equipment (75.90%), and environment (69.90%) had the most significant influence in area SC 01. Therefore, it is necessary to prevent or reduce the risk of road accidents. First, road users must be aware of risks and adhere rigorously to traffic regulations. Regarding traffic knowledge, a safe road environment, and strict adherence to traffic laws, schools, communities, and government organizations must support them.

Author contributions

Ratchanee Joomjee: Conceptualization, Investigation, Writing – Original Draft Preparation

Momthicha Raksin: Resources, Supervision

Yanitha Paengprakhon: Formal Analysis, Investigation

Jaruporn Duangsri: Methodology, Project Administration, Software

Niruwan Turnbull: Project Administration, Validation, Writing – Review & Editing

Data availability

Underlying data

Figshare: Risk Assessment Factors of Road Accident at School Based on Fault Tree Analysis and Analytic Hierarchy Process in Rural of Thailand. https://doi.org/10.6084/m9.figshare.23626425.²⁹

This project contains the following underlying data:

• Underlying data.xlsx. (Results of the survey conducted on various aspects of road safety)

Extended data

Figshare: Risk Assessment Factors of Road Accident at School Based on Fault Tree Analysis and Analytic Hierarchy Process in Rural of Thailand. https://doi.org/10.6084/m9.figshare.23626425.²⁹

This project contains the following extended data:

• Risk Assessment Form - English.pdf. (The data used to assess and manage potential risks to students and staff at various schools)
• Risk Assessment Form - Thai.pdf

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgements

QUVAE Research & Publications provided guidance and support during the submission process for this article and data deposition.

References

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2. Abra FD, Granziera BM, Huijser MP, et al.: Pay or prevent? Human safety, costs to society and legal perspectives on animal-vehicle collisions in São Paulo state, Brazil. PLoS One. 2019; 14(4): e0215152. PubMed Abstract | Publisher Full Text | Free Full Text
3. Rizzi LI, de Dios O ; xfa, zar J: Road Safety Valuation under a Stated Choice Framework. JTEP. 2006; 40(1): 69–94.
4. Trawén A, Maraste P, Persson U: International comparison of costs of a fatal casualty of road accidents in 1990 and 1999. Accid. Anal. Prev. 2002; 34(3): 323–332. PubMed Abstract | Publisher Full Text
5. Yaacob NFF, Bohari SN, editors. A Review Analysis of Accident Factor on Road Accident Cases Using Haddon Matrix Approach. International Conference on the Future of ASEAN (ICoFA).2018.
6. World bank country and lending groups: country classification, historical classification by income.2023. Reference Source
7. Lin Z-b, Ji Y, Xiao Q-y, et al.: Risk factors of bicycle traffic injury among middle school students in chaoshan rural areas of china. Int. J. Equity Health. 2017; 16: 28. PubMed Abstract | Publisher Full Text | Free Full Text
8. Tunthanathip T, Phuenpathom N: Impact of Road Traffic Injury to Pediatric Traumatic Brain Injury in Southern Thailand. J. Neurosci. Rural Pract. 2017; 8(4): 601–608. PubMed Abstract | Publisher Full Text
9. Barnett DJ, Balicer RD, Blodgett D, et al.: The application of the Haddon matrix to public health readiness and response planning. Environ. Health Perspect. 2005; 113(5): 561–566. Publisher Full Text
10. Bennani N, Mezhoud S: Risk assessment of several hazards along railway network using AHP incorporated into GIS. Technium Soc. Sci. J. 2023; 39(1): 452–464. Publisher Full Text
11. Xiao Q, Li Y, Luo F, et al.: Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network. Technol. Soc. 2023; 73: 102229. Publisher Full Text
12. Kennedy A, Cullen B, Firman D, et al.: Peer passenger intentions to speak up to a risky driver: A theoretically-guided investigation of the effects of a high school road safety education program. Transport. Res. F: Traffic Psychol. Behav. 2018; 54: 15–27. Publisher Full Text
13. Wenyu QXL, Zhibin K: Analysis of Characteristics of Road Traffic Accident Casualties in Guilin. Open J. Soc. Sci. 2018; 6: 90–96.
14. Nicole Oneyear SH, Goswamy A, Thapa R, et al.: Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data. J. Transp. Technol. 2018; 13: 208–221. Publisher Full Text
15. Bocage C, Mashalla Y, Motshome P, et al.: Applying the Haddon matrix conceptual model to guide motor vehicle crash injury research and prevention in Botswana. Afr. J. Emerg. Med. 2020; 10: S38–S43. PubMed Abstract | Publisher Full Text | Free Full Text
16. Tao L, Chen L, Long P, et al.: Integrated risk assessment method for spent fuel road transportation accident under complex environment. Nucl. Eng. Technol. 2021; 53(2): 393–398. Publisher Full Text
17. Hyun K-C, Min S, Choi H, et al.: Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunn. Undergr. Space Technol. 2015; 49: 121–129. Publisher Full Text
18. Kanuganti S, Agarwala R, Dutta B, et al.: Road safety analysis using multi criteria approach: A case study in India. Transp. Res. Procedia. 2017; 25: 4649–4661. Publisher Full Text
19. Fernandez JJ, Paringit MC, Salvador JR, et al.: Driver’s Road Accident Factor Prioritization using AHP in Relation to Mastery of Traffic Signs in the City of Manila. Transp. Res. Procedia. 2020; 48: 1316–1324. Publisher Full Text
20. Chand A, Jayesh S, Bhasi AB: Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Mater. Today: Proc. 2021; 47: 5135–5141.
21. Yang T, Zeng H, Yang X, et al.: Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population. Heliyon. 2023; 9(1): e12822. PubMed Abstract | Publisher Full Text | Free Full Text
22. Zhao L, Wang C, Yang H, et al.: Exploring injury severity of Non-Motor vehicle riders involving in traffic accidents using the generalized ordered logit model. Ain Shams Eng. J. 2023; 14(5): 101962. Publisher Full Text
23. Kingham S, Sabel CE, Bartie P: The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis. J. Transp. Geogr. 2011; 19(4): 705–711. Publisher Full Text
24. Bina M, Confalonieri F, Abati D, et al.: Analysis of traffic upon school departure: Environment, behaviour, well-being and risk factors for road crashes. J. Transp. Health. 2021; 22: 101119. Publisher Full Text
25. Pei X, Wong SC, Sze NN: The roles of exposure and speed in road safety analysis. Accid. Anal. Prev. 2012; 48: 464–471. PubMed Abstract | Publisher Full Text
26. Ahmed S, Hossain MA, Ray SK, et al.: A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance. Transp. Res. Interdiscip. Perspect. 2023; 19: 100814. Publisher Full Text
27. Smith M, Ikeda E, Hawley G, et al.: An integrated conceptual model of environmental needs for New Zealand children’s active travel to school. J. Transp. Health. 2020; 16: 100814. Publisher Full Text
28. Zhang Q, Lu Y, Feng F, et al.: Causal analysis of coach and bus accidents in China based on road alignments. Heliyon. 2023; 9(4): e15231. PubMed Abstract | Publisher Full Text | Free Full Text
29. Joomjee R, Raksin M, Paengprakhon Y, et al.: Risk Assessment Factors of Road Accident at School Based on Fault Tree Analysis and Analytic Hierarchy Process in Rural of Thailand. [Dataset]. Figshare. 2023. Publisher Full Text

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Version 1

VERSION 1 PUBLISHED 11 Mar 2024

Author details Author details

¹ Faculty of Public Health, Ubon Ratchathani Rajabhat University, Nai Mueang, Nakhon Ratchasima, Thailand
² Faculty of Public Health, Mahasarakham University, Maha Sarakham, Maha Sarakham, Thailand
³ Public Health and Environmental Policy in Southeast Asia Research Cluster (PHEP-SEA), Mahasarakham University, Maha Sarakham, Thailand

Ratchanee Joomjee
Roles: Conceptualization, Investigation, Writing – Original Draft Preparation

Momthicha Raksin
Roles: Resources, Supervision

Yanitha Paengprakhon
Roles: Formal Analysis, Investigation

Jaruporn Duangsri
Roles: Methodology, Project Administration, Software

Niruwan Turnbull
Roles: Project Administration, Validation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research project was supported by the Ubon Ratchathani Rajabhat University, Thailand under grant (ID 2561A16402066).

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Published: 11 Mar 2024, 13:187

https://doi.org/10.12688/f1000research.139489.1

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Joomjee R, Raksin M, Paengprakhon Y et al. Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand [version 1; peer review: 2 approved with reservations]. F1000Research 2024, 13:187 (https://doi.org/10.12688/f1000research.139489.1)

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Version 1

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PUBLISHED 11 Mar 2024

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13

Reviewer Report 22 May 2024

Arroon Ketsakorn, Thammasat University, Bangkok, Bangkok, Thailand

Approved with Reservations

https://doi.org/10.5256/f1000research.152765.r267027

According to the title, the manuscript describes the risk assessment by applying the fault tree analysis and analytic hierarchy process in rural areas of Thailand. Partly due to language issues, the manuscript is not easy to read and understand, especially ... Continue reading

According to the title, the manuscript describes the risk assessment by applying the fault tree analysis and analytic hierarchy process in rural areas of Thailand. Partly due to language issues, the manuscript is not easy to read and understand, especially regarding the fault tree analysis and analytic hierarchy process. It is unclear how to use both fault tree analysis and analytic hierarchy process. Why did the authors choose to use those tools? However, this study has many good and important aspects, but the manuscript needs some major, but I view, very “do-able” revisions.
Title

Please specific the study area in the title. It's too spacious for rural areas.

Abstract

Please adjust the writing of the introduction to be consistent with what you want to study.
Please check typo error entire the manuscript.
Authors need to determine the factors for assessing the risk.
Please rewrite of the methods section. The authors should explain the details of study.
Fourteen sub-criteria? What are the main criteria?
“RI (random index) was calculated” This sentence is not correct. Please revise it.
The conclusion should not be written in duplicate with the results of the study.
This “traffic accident” does not reflect research study.
The FTA-AHP techniques duplicated with Analytic Hierarchy Process.

Introduction

Lack of explanation on the area of study and the issues/problems. The authors need to elaborate more on the issues towards road traffic accident at school. Please support the road traffic accident at school with citations or literature sources when elaborating the aspects. This is important step to find out the gap of research.
Due to language issues, the manuscript is not easy to read and understand. Please send the manuscript to a native speaker to check before submission.
What are the advantage and disadvantage of using AHP?
Why did the authors choose the fault tree analysis? Please provide supporting reasons.

Objective

Please adjust the objective to be consistent with the research title.

Methods

Where does the checklist form come from? Who made it? Please give details.
Please add more details of FTA and AHP including risk assessment
How does the authors prove the FTA diagram? Please add the details
The method for doing AHP should be explained in detail. In the case where there are many evaluators, what is the method for doing it?
Please add the reference in Table 3 and Figure 5.
“Figure 5 Risk scoring matrix” This is a table, not a figure.
Please give the meaning the risk class; negligible 1-5; Tolerable 6-9; Undesirable 10-16; Intolerable 17-25.

Results

Please give an example of manual calculation for AHP.
Please show the results from Expert Choice program.

Discussion

The results should be discussed in the section FTA; AHP and risk assessment.
The discussion part should not be written in duplicate with the results of the study.
Authors must first discuss the study's findings and then explain how the study's results are similar or different from other studies.

Conclusion

The conclusion should not be written in duplicate with the results of the study.

References

Harmonize format with F1000Research style requirements.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Occupational Health and Safety

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

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13

Reviewer Report 14 May 2024

Shehzad Saleem, King Edward Medical University, Punjab, Pakistan

Approved with Reservations

https://doi.org/10.5256/f1000research.152765.r261889

The study's conclusions and suggestions may influence and enhance safety protocols, legislative choices, and instructional programs, thereby lowering the chance of traffic accidents and fostering a safer atmosphere for communities and students. The scholars' commitment to this significant subject is ... Continue reading

The study's conclusions and suggestions may influence and enhance safety protocols, legislative choices, and instructional programs, thereby lowering the chance of traffic accidents and fostering a safer atmosphere for communities and students. The scholars' commitment to this significant subject is really admirable!
Description about significance of study:

The Haddon Matrix and other methods were utilized in a study on traffic accidents near schools in Ubon Rattanathi, Thailand, in order to identify and evaluate risk factors.The study discovered that the biggest risk factors were human perception of risk, the environment (particularly speeding), and problems with vehicles and equipment, with human conduct accounting for the most important portion (77.90%). The survey also found that utilizing defective vehicles, speeding in school zones, and a lack of safety knowledge were key causes of accidents. These results are consistent with earlier studies that found behavioral aspects of human conduct to be the greatest risk factor (41%), followed by environmental and vehicle components.

Comments:
1. The writers skipped over 'accidents' in the title. (The title could be changed to: Risk assessment of Road accidents in rural Thailand: A fault tree analysis)
2. The authors have describes ''The most hazardous environment was rural roads'' - So, why they have conducted this study in urban settings ''involving 10 kindergartens to junior high schools in Urbon Rattanathi'' and not in rural roads?
3. Please explain why was a variable not included in the FTA hierarchy.
4. Please find spelling mistakes such as: (Ubon=Urban; Risk Risk level of school=Risk level of School)
5. The last 2 lines of the conclusion give recommendations for the drivers and not for the environmental changes which can reduce accidents
6. The study design is appropriate, and suitable for the research question or objective.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Public Health

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

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Version 1

VERSION 1 PUBLISHED 11 Mar 2024

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Version 1 11 Mar 24	read	read

Shehzad Saleem, King Edward Medical University, Punjab, Pakistan
Arroon Ketsakorn, Thammasat University, Bangkok, Thailand

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Reviewer Report

13 Views

22 May 2024 | for Version 1

Arroon Ketsakorn, Thammasat University, Bangkok, Bangkok, Thailand

13 Views Cite this report Responses(0)

Approved With Reservations

According to the title, the manuscript describes the risk assessment by applying the fault tree analysis and analytic hierarchy process in rural areas of Thailand. Partly due to language issues, the manuscript is not easy to read and understand, especially regarding the fault tree analysis and analytic hierarchy process. It is unclear how to use both fault tree analysis and analytic hierarchy process. Why did the authors choose to use those tools? However, this study has many good and important aspects, but the manuscript needs some major, but I view, very “do-able” revisions.
Title

Please specific the study area in the title. It's too spacious for rural areas.

Abstract

Please adjust the writing of the introduction to be consistent with what you want to study.
Please check typo error entire the manuscript.
Authors need to determine the factors for assessing the risk.
Please rewrite of the methods section. The authors should explain the details of study.
Fourteen sub-criteria? What are the main criteria?
“RI (random index) was calculated” This sentence is not correct. Please revise it.
The conclusion should not be written in duplicate with the results of the study.
This “traffic accident” does not reflect research study.
The FTA-AHP techniques duplicated with Analytic Hierarchy Process.

Introduction

Lack of explanation on the area of study and the issues/problems. The authors need to elaborate more on the issues towards road traffic accident at school. Please support the road traffic accident at school with citations or literature sources when elaborating the aspects. This is important step to find out the gap of research.
Due to language issues, the manuscript is not easy to read and understand. Please send the manuscript to a native speaker to check before submission.
What are the advantage and disadvantage of using AHP?
Why did the authors choose the fault tree analysis? Please provide supporting reasons.

Objective

Please adjust the objective to be consistent with the research title.

Methods

Where does the checklist form come from? Who made it? Please give details.
Please add more details of FTA and AHP including risk assessment
How does the authors prove the FTA diagram? Please add the details
The method for doing AHP should be explained in detail. In the case where there are many evaluators, what is the method for doing it?
Please add the reference in Table 3 and Figure 5.
“Figure 5 Risk scoring matrix” This is a table, not a figure.
Please give the meaning the risk class; negligible 1-5; Tolerable 6-9; Undesirable 10-16; Intolerable 17-25.

Results

Please give an example of manual calculation for AHP.
Please show the results from Expert Choice program.

Discussion

The results should be discussed in the section FTA; AHP and risk assessment.
The discussion part should not be written in duplicate with the results of the study.
Authors must first discuss the study's findings and then explain how the study's results are similar or different from other studies.

Conclusion

The conclusion should not be written in duplicate with the results of the study.

References

Harmonize format with F1000Research style requirements.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Occupational Health and Safety

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

13 Views

14 May 2024 | for Version 1

Shehzad Saleem, King Edward Medical University, Punjab, Pakistan

13 Views Cite this report Responses(0)

Approved With Reservations

The study's conclusions and suggestions may influence and enhance safety protocols, legislative choices, and instructional programs, thereby lowering the chance of traffic accidents and fostering a safer atmosphere for communities and students. The scholars' commitment to this significant subject is really admirable!
Description about significance of study:

The Haddon Matrix and other methods were utilized in a study on traffic accidents near schools in Ubon Rattanathi, Thailand, in order to identify and evaluate risk factors.The study discovered that the biggest risk factors were human perception of risk, the environment (particularly speeding), and problems with vehicles and equipment, with human conduct accounting for the most important portion (77.90%). The survey also found that utilizing defective vehicles, speeding in school zones, and a lack of safety knowledge were key causes of accidents. These results are consistent with earlier studies that found behavioral aspects of human conduct to be the greatest risk factor (41%), followed by environmental and vehicle components.

Comments:
1. The writers skipped over 'accidents' in the title. (The title could be changed to: Risk assessment of Road accidents in rural Thailand: A fault tree analysis)
2. The authors have describes ''The most hazardous environment was rural roads'' - So, why they have conducted this study in urban settings ''involving 10 kindergartens to junior high schools in Urbon Rattanathi'' and not in rural roads?
3. Please explain why was a variable not included in the FTA hierarchy.
4. Please find spelling mistakes such as: (Ubon=Urban; Risk Risk level of school=Risk level of School)
5. The last 2 lines of the conclusion give recommendations for the drivers and not for the environmental changes which can reduce accidents
6. The study design is appropriate, and suitable for the research question or objective.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Public Health

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Respond to this report

Responses (0)

[1] 1. Global Status Report on Road Safety: 2018. Reference Source

[2] 2. Abra FD, Granziera BM, Huijser MP, et al.: Pay or prevent? Human safety, costs to society and legal perspectives on animal-vehicle collisions in São Paulo state, Brazil. PLoS One. 2019; 14(4): e0215152. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Rizzi LI, de Dios O ; xfa, zar J: Road Safety Valuation under a Stated Choice Framework. JTEP. 2006; 40(1): 69–94.

[4] 4. Trawén A, Maraste P, Persson U: International comparison of costs of a fatal casualty of road accidents in 1990 and 1999. Accid. Anal. Prev. 2002; 34(3): 323–332. PubMed Abstract | Publisher Full Text

[5] 5. Yaacob NFF, Bohari SN, editors. A Review Analysis of Accident Factor on Road Accident Cases Using Haddon Matrix Approach. International Conference on the Future of ASEAN (ICoFA).2018.

[6] 6. World bank country and lending groups: country classification, historical classification by income.2023. Reference Source

[7] 7. Lin Z-b, Ji Y, Xiao Q-y, et al.: Risk factors of bicycle traffic injury among middle school students in chaoshan rural areas of china. Int. J. Equity Health. 2017; 16: 28. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Tunthanathip T, Phuenpathom N: Impact of Road Traffic Injury to Pediatric Traumatic Brain Injury in Southern Thailand. J. Neurosci. Rural Pract. 2017; 8(4): 601–608. PubMed Abstract | Publisher Full Text

[9] 9. Barnett DJ, Balicer RD, Blodgett D, et al.: The application of the Haddon matrix to public health readiness and response planning. Environ. Health Perspect. 2005; 113(5): 561–566. Publisher Full Text

[10] 10. Bennani N, Mezhoud S: Risk assessment of several hazards along railway network using AHP incorporated into GIS. Technium Soc. Sci. J. 2023; 39(1): 452–464. Publisher Full Text

[11] 11. Xiao Q, Li Y, Luo F, et al.: Analysis and assessment of risks to public safety from unmanned aerial vehicles using fault tree analysis and Bayesian network. Technol. Soc. 2023; 73: 102229. Publisher Full Text

[12] 12. Kennedy A, Cullen B, Firman D, et al.: Peer passenger intentions to speak up to a risky driver: A theoretically-guided investigation of the effects of a high school road safety education program. Transport. Res. F: Traffic Psychol. Behav. 2018; 54: 15–27. Publisher Full Text

[13] 13. Wenyu QXL, Zhibin K: Analysis of Characteristics of Road Traffic Accident Casualties in Guilin. Open J. Soc. Sci. 2018; 6: 90–96.

[14] 14. Nicole Oneyear SH, Goswamy A, Thapa R, et al.: Analysis of Stopping Behavior at Rural T-Intersections Using Naturalistic Driving Study Data. J. Transp. Technol. 2018; 13: 208–221. Publisher Full Text

[15] 15. Bocage C, Mashalla Y, Motshome P, et al.: Applying the Haddon matrix conceptual model to guide motor vehicle crash injury research and prevention in Botswana. Afr. J. Emerg. Med. 2020; 10: S38–S43. PubMed Abstract | Publisher Full Text | Free Full Text

[16] 16. Tao L, Chen L, Long P, et al.: Integrated risk assessment method for spent fuel road transportation accident under complex environment. Nucl. Eng. Technol. 2021; 53(2): 393–398. Publisher Full Text

[17] 17. Hyun K-C, Min S, Choi H, et al.: Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels. Tunn. Undergr. Space Technol. 2015; 49: 121–129. Publisher Full Text

[18] 18. Kanuganti S, Agarwala R, Dutta B, et al.: Road safety analysis using multi criteria approach: A case study in India. Transp. Res. Procedia. 2017; 25: 4649–4661. Publisher Full Text

[19] 19. Fernandez JJ, Paringit MC, Salvador JR, et al.: Driver’s Road Accident Factor Prioritization using AHP in Relation to Mastery of Traffic Signs in the City of Manila. Transp. Res. Procedia. 2020; 48: 1316–1324. Publisher Full Text

[20] 20. Chand A, Jayesh S, Bhasi AB: Road traffic accidents: An overview of data sources, analysis techniques and contributing factors. Mater. Today: Proc. 2021; 47: 5135–5141.

[21] 21. Yang T, Zeng H, Yang X, et al.: Characteristics of road traffic accident types and casualties in Guangzhou, China, from 2007 to 2020: A retrospective cohort study based on the general population. Heliyon. 2023; 9(1): e12822. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Zhao L, Wang C, Yang H, et al.: Exploring injury severity of Non-Motor vehicle riders involving in traffic accidents using the generalized ordered logit model. Ain Shams Eng. J. 2023; 14(5): 101962. Publisher Full Text

[23] 23. Kingham S, Sabel CE, Bartie P: The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis. J. Transp. Geogr. 2011; 19(4): 705–711. Publisher Full Text

[24] 24. Bina M, Confalonieri F, Abati D, et al.: Analysis of traffic upon school departure: Environment, behaviour, well-being and risk factors for road crashes. J. Transp. Health. 2021; 22: 101119. Publisher Full Text

[25] 25. Pei X, Wong SC, Sze NN: The roles of exposure and speed in road safety analysis. Accid. Anal. Prev. 2012; 48: 464–471. PubMed Abstract | Publisher Full Text

[26] 26. Ahmed S, Hossain MA, Ray SK, et al.: A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance. Transp. Res. Interdiscip. Perspect. 2023; 19: 100814. Publisher Full Text

[27] 27. Smith M, Ikeda E, Hawley G, et al.: An integrated conceptual model of environmental needs for New Zealand children’s active travel to school. J. Transp. Health. 2020; 16: 100814. Publisher Full Text

[28] 28. Zhang Q, Lu Y, Feng F, et al.: Causal analysis of coach and bus accidents in China based on road alignments. Heliyon. 2023; 9(4): e15231. PubMed Abstract | Publisher Full Text | Free Full Text

[29] 29. Joomjee R, Raksin M, Paengprakhon Y, et al.: Risk Assessment Factors of Road Accident at School Based on Fault Tree Analysis and Analytic Hierarchy Process in Rural of Thailand. [Dataset]. Figshare. 2023. Publisher Full Text

Risk assessment based on fault tree analysis and analytic hierarchy process in rural areas of Thailand

Abstract

Introduction

Methods

Results

Conclusions

Keywords

Introduction

Objective

Methods

Study design and sampling

Figure 1. Map of the study area.

Inclusion criteria and exclusion criteria

The instruments and data collection

Risk identification and FTA

Figure 2. Fault tree model for school hazard identified.

Risk impact with AHP

Table 1. Analytic hierarchy process (AHP) Scale for Pairwise Comparisons.19

Figure 3. The consistency index (CI) and the consistency ratio (CR) were calculated based on equations.

Risk probability analysis with AHP with FTA-AHP

Figure 4. The Analytic Hierarchy Process (AHP).

Data processing and analysis

Table 2. Probability score and impact score from analytic hierarchy process (AHP).

Table 3. Risk probability and impact rating scores.

Figure 5. Risk scoring matrix.

Results

Probability and impact of each accident-causing incident on the school’s road.

Figure 6. The probability of each event that will cause an accident is a road around the school.

FTA-AHP Risk Assessment Results

Figure 7. FTA-AHP risk assessment results.

Table 4. The risk score level of the school.

Discussion

The risk assessment

Conclusion

Author contributions

Data availability

Underlying data

Extended data

Acknowledgements

References

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Table 1. Analytic hierarchy process (AHP) Scale for Pairwise Comparisons.¹⁹