⚡ electrical engineering

Root Cause Identification and Risk Prioritization of Road Transportation of Hazardous Materials in Iran: An Integrated Fault Tree Analysis and Risk Matrix Approach

This study integrates Fault Tree Analysis with a Risk Matrix to identify and prioritize the root causes of hazardous materials transport incidents in Iran, revealing that lax technical inspection is the highest-risk factor among a complex mix of technical, human, organizational, and economic vulnerabilities.

Original authors: Mojdeh Raateghi, Reza Gholamnia, Reza Saeedi, Amin Bagheri

Published 2026-10-09

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Mojdeh Raateghi, Reza Gholamnia, Reza Saeedi, Amin Bagheri

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Every day, massive trucks crisscross the highways of Iran, carrying more than 50 million tons of dangerous goods. These loads include flammable liquids, toxic chemicals, and other materials that can cause fires, explosions, or severe environmental harm if something goes wrong. While the movement of these substances is essential for the country's industry and energy supply, it carries a heavy price. When a tanker crashes, leaks, or catches fire, the consequences can be catastrophic, claiming lives and damaging the land. For years, officials and safety experts have tried to understand why these accidents happen, but they often found themselves looking only at the surface. They saw the broken truck or the tired driver, but they missed the deeper reasons why the truck was broken or why the driver was tired.

To get a clearer picture, researchers need a way to trace a disaster back to its very beginning, much like following a river upstream to find its source. One powerful tool for this is called fault tree analysis. Imagine a tree growing upside down, where the top branch is the accident itself, and the branches below it are the things that could have caused it. By breaking down an accident into smaller and smaller pieces, experts can see not just the immediate trigger, but the chain of events and decisions that led to it. Another tool, the risk matrix, helps them decide which of these causes are the most dangerous to ignore. By combining these two methods, the researchers aimed to move beyond simple guesses and build a complete map of the dangers facing Iran's hazardous material transport.

A team of researchers from Shahid Beheshti University of Medical Sciences set out to map these dangers using real-world data from the last five years. They gathered 34 documented reports of serious incidents involving hazardous materials on Iran's interurban roads. These reports covered a range of disasters, including rollovers that caused leaks, explosions, fires, and severe damage to vehicles and cargo. However, the team quickly realized that the official police and government reports told only part of the story. These documents usually listed the obvious, immediate causes, such as a driver falling asleep or a tire blowing out, but they rarely mentioned the deeper, structural problems that allowed those immediate causes to happen. To fill in these gaps, the researchers interviewed 23 experts from a wide variety of fields, including firefighters, police officers, transport company managers, and environmental specialists. They asked these experts to look past the surface and identify the hidden roots of the problems.

By combining the official accident reports with the insights from these experts, the team built five detailed fault trees, one for each type of incident. They identified 74 distinct basic events that could lead to a disaster. When they analyzed these events, they found a surprising pattern. The common belief in the industry is that accidents are usually caused by a single dominant factor, often blamed on the driver or a specific mechanical failure. The study, however, showed that the reality is far more complex. The risks are not the result of one broken link in a chain, but rather the simultaneous intersection of four separate, parallel clusters of problems. These clusters are: the technical condition of the vehicles, the human factors of the drivers, the governance and management of the organizations, and the transparency of the economic and documentation systems.

The researchers then asked the experts to rate how likely each of these 74 events was to happen and how severe the consequences would be if they did. They used a standard scale to score each event and then calculated a risk score to see which ones were the most critical. The results were stark. None of the identified risks fell into a "low" category. In fact, 81 percent of the events were classified as high risk. The single highest-risk factor was not a driver error or a specific chemical leak, but "lax technical inspection." This means that the failure to properly check and maintain the vehicles was the most significant threat. However, this was just the tip of the iceberg. The next most dangerous factors included the absence of legal requirements for a second driver, a lack of oversight by technical managers, driver fatigue, and the profit-seeking behavior of intermediaries in the transport chain.

Perhaps the most significant finding of the study was that the official reports were missing three-quarters of the high-risk causes. The government documents never mentioned the lack of specialized management, the gaps in regulatory oversight, or the deficiencies in the licensing chain. These are the invisible roots that allow the visible problems to grow. The study suggests that as long as safety officials rely only on the standard accident reports, they will be blind to the most important causes of danger. The researchers argue that fixing the problem requires a simultaneous effort across all four clusters. You cannot simply train drivers better if the trucks are not inspected, and you cannot inspect trucks better if the management system does not prioritize safety.

The study concludes that the risk of transporting hazardous materials in Iran is a multi-causal phenomenon that demands a multi-dimensional solution. The highest priority is to tighten the technical inspection of vehicles, but this must be done alongside improvements in driver management, organizational governance, and economic transparency. The researchers recommend that the country's official incident reporting system be redesigned to capture these structural root causes, not just the immediate ones. By doing so, policymakers can move from reacting to accidents to preventing them by addressing the deep, interconnected systems that allow them to occur. The work provides a practical roadmap for safety officials, showing that to keep the roads safe, they must look beyond the crash site and examine the entire system that brought the truck there.

Technical Summary: Root Cause Identification and Risk Prioritization of Road Transportation of Hazardous Materials in Iran

Problem Statement
Iran transports over 50 million tons of hazardous materials annually across its interurban road network. Despite the recent accession to the European Agreement concerning the International Carriage of Dangerous Goods by Road (ADR), the country's risk-management practices require fundamental reform. A critical gap exists in the current understanding of hazardous materials (hazmat) transport risks: official incident reports (police and Road Maintenance and Transportation Organization) largely document overt, surface-level causes (e.g., driver error, mechanical failure) while neglecting structural root causes related to governance, organizational management, and economic transparency. Furthermore, existing literature often relies on single-facility studies or computational models requiring precise engineering data that is unavailable at the national level in Iran. Consequently, there is a lack of a comprehensive, multi-causal analysis that integrates real incident data with expert knowledge to prioritize risks across technical, human, managerial, and economic domains.

Methodology
This study employs a descriptive-analytical, exploratory design utilizing an integrated Fault Tree Analysis (FTA) and Risk Matrix approach. The research was conducted in six stages:

Data Collection: A census of 45 incident reports (2021–2025) from the Road Maintenance and Transportation Organization was screened. After excluding 11 reports due to incomplete data or inconsistencies, 34 documented incidents were analyzed. These were categorized into five top events: rollover-with-leakage, leakage, explosion, fire, and severe damage.
Expert Elicitation: To uncover structural root causes absent from official reports, 23 multidisciplinary experts (from fields including transportation, emergency response, HSE, and law enforcement) were interviewed using open-ended questionnaires. Sixteen completed interviews were used for qualitative content analysis.
Factor Screening and Classification: Over 100 candidate causal factors were identified by converging documented data with expert insights. A four-stage screening process (convergence, frequency threshold, causal-fit, and merging) refined these into 74 unique basic events, structured into five qualitative fault trees.
Qualitative Fault Tree Construction: Five independent fault trees were constructed, one for each incident category. Unlike quantitative FTA models, these trees utilized only OR logic gates. This methodological choice was deliberate, reflecting the study's qualitative nature and the absence of a nationally validated failure-rate database; it posits that any single subordinate event is sufficient to trigger the top event in high-consequence systems.
Quantification via Risk Matrix: A second structured questionnaire was administered to 21 experts (the original 16 plus 5 new experts) to rate the likelihood and severity of each of the 74 basic events on a five-level linguistic scale. Experts were weighted based on organizational title, education, and experience.
Prioritization: Risk scores ( $R$ ) were calculated as the product of weighted likelihood ( $P$ ) and severity ( $S$ ). A 5×5 risk matrix was used to categorize events into Low, Medium, High, and Critical risk levels for relative prioritization.

Key Results

Incident Profile: Among the 34 analyzed incidents, "rollover-with-leakage" was the most frequent (52.9%), followed by leakage (17.6%). Petroleum products accounted for over 59% of the materials involved. The incidents resulted in 30 injuries and 32 fatalities, with a fatality rate of 52.9% and environmental release in 70.6% of cases.
Risk Distribution: The mean likelihood score was 3.18 and mean severity was 3.21 (on a 1–5 scale), indicating a medium-to-high risk profile. Crucially, none of the 74 basic events fell into the "Low" or "Critical" risk categories. Instead, 60 events (81%) were classified as "High" risk, and 14 events (19%) as "Medium" risk.
Causal Clusters: The analysis revealed that hazmat transport risk is a multi-causal, multi-level phenomenon. The ten highest-risk events formed four parallel causal clusters rather than a single linear chain:
1. Vehicle Technical Integrity: Led by "lax technical inspection" (Highest Risk Score: $R=13.84$ ).
2. Driver Human Factors: Including fatigue, drowsiness, and invalid documentation.
3. Organizational Governance: Including managerial inefficiency and lack of specialized management approaches.
4. Economic Transparency: Including intermediaries' profit-seeking behavior and failure to declare hazard classes.
The "Absent Causes" Finding: A significant finding was that three categories of root causes—governance/oversight gaps, lack of institutionalized training, and technical inspection/licensing deficiencies—were completely absent from the 34 official incident reports, despite being identified as high-risk by experts.

Significance and Contributions
The paper claims that its primary contribution is shifting the paradigm from a single-factor dominance (typically driver error) to a recognition of four parallel causal clusters. By integrating documented data with multidisciplinary expert knowledge, the study successfully identified governance and organizational management as independent causal clusters on par with technical and behavioral factors.

Methodologically, the study demonstrates the utility of combining qualitative FTA with a semi-quantitative risk matrix in data-poor environments. This approach allows for the prioritization of safety interventions without requiring the precise engineering failure data necessary for computational Boolean probability estimation.

Practically, the study argues that relying solely on official incident reports for policy-making systematically overlooks the most critical root causes. The authors conclude that effective risk reduction in Iran requires simultaneous intervention across technical, human, managerial, and economic domains. They specifically recommend redesigning official incident-reporting forms to capture structural root causes, noting that current forms fail to record three-quarters of the high-risk causal clusters identified in this analysis. The study maintains modesty regarding its claims, acknowledging limitations such as the reliance on subjective expert judgment, the lack of a quantitative computational model, and the uneven temporal distribution of the incident data.

Technical Summary: Root Cause Identification and Risk Prioritization of Road Transportation of Hazardous Materials in Iran

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