Causation Analysis of Hazardous Material Road Transportation Accidents Based on the Ordered Logit Regression Model

Identifying Key Factors of Hazardous Materials Transportation Accidents Based on Higher-Order and Multilayer Networks

This paper focuses on the application of higher-order and multilayer networks in identifying critical causes and relationships contributing to hazardous materials transportation accidents. There were 792 accidents of hazardous materials transportation that occurred on the road from 2017 to 2021 which have been investigated. By considering time sequence and dependency of causes, the hazardous materials transportation accidents causation network (HMTACN) was described using the higher-order model. To investigate the structure of HMTACN such as the importance of causes and links, HMTACN was divided into three layers using the weighted k-core decomposition: the core layer, the bridge layer and the peripheral layer. Then causes and links were analyzed in detail. It was found that the core layer was tightly connected and supported most of the causal flows of HMTACN. The results showed that causes should be given hierarchical attention. This study provides an innovative method to analyze complicated accidents, which can be used in identifying major causes and links. And this paper brings new ideas about safety network study and extends the applications of complex network theory.

The impact of accidents during the transport of dangerous good, on people, the environment, and infrastructure and measures for their reduction: a review

Road transport is one of the most dangerous methods of goods transport. Driver errors and poor traffic conditions can cause traffic accidents, which can have a negative impact on people's health, the environment, and infrastructure. The main influence on the level of the consequences is the chemical composition and amount of the transported substance. This paper presents the causes of traffic accidents during the transport of dangerous goods. In addition, how traffic accidents during the transport of dangerous goods affect people's health, the environment, and infrastructure was shown. After that, measures for accidents avoidance and the alleviation and reduction of dangerous goods were given. From the review of studies from the subject field, it can be concluded that the dangerous goods are very harmful to people, the environment, and infrastructure when transport accidents occur. Lessons should be learned from the history of accidents involving the transport of dangerous goods to avoid repeating the same mistakes. The review conclusions indicate that a routes optimization and investment in road infrastructure are needed to reduce risk during the transport of dangerous goods.

Prioritizing risk factors of hazardous material road transportation accidents using the fuzzy AHP method

BACKGROUND

Annually, large amounts of hazardous materials (hazmat) are transported through the roads and this movement causes various accidents. Identifying the causes of these accidents is a critical issue for all public governments.

OBJECTIVES

This study aimed to identify the effective risk factors for hazmat road transport accidents and determine their relative weight using the fuzzy analytical hierarchy process (AHP) method.

METHODS

This qualitative study was conducted in 2021 in Iran and included four steps, i.e., the identification (using literature review and semi-structured interview), determination (according to the expert panel opinion), classification, and prioritization of effective factors in hazmat road transportation accidents. To prioritize and determine the relative weight of the effective factors, the fuzzy AHP technique was used.

RESULTS

In total, 159 risk factors were identified, which were classified into six factors (including road, transportation management, vehicle, cargo, driver, and weather conditions) and 24 bub-factors. The main factor (greatest relative weight) with the highest priority was the driver (0.181). The road (0.167), cargo (0.166), vehicle (0.169), transportation management (0.161), and weather conditions (0.159) were the next priorities, in that order.

CONCLUSION

The results demonstrated that the driver is the most important factor in causing accidents when transporting hazmat by road. The findings of this study might have the potential to decrease the frequency and consequence of accidents caused by the road transport of hazmat.

A survey on road hazardous material transportation accidents in Iran

Hazardous material road transportation is one of the most challenging procedures performed by large trucks and trailers. In this study, after examining and analyzing road hazardous material transportation accidents, occurred over 5 years in Iran, the contributing factors of road hazardous material transportation accidents were determined. Subsequently, the introduced factors were prioritized using fault tree analysis and the Dempster-Shafer evidence theory. The results revealed that the frequency of accidents has significantly increased in recent years. It is shown that the three pivotal factors in road hazardous material transportation accidents were transport vehicle, packaging and loading of hazardous materials, and human factors. These findings provide an empirically supported theoretical basis for transportation corporations to take corrective and preventative measures to reduce the accident risks. A novel technique has been introduced for analyzing the causes of road hazardous material transportation accidents. Finally, the absence of hazardous material transportation companies in Iran is introduced as a critical reason for the higher frequency of such accidents in Iran compared to other countries.

Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

Influence of Environmental Factors on Injury Severity Using Ordered Logit Regression Model in Limpopo Province, South Africa

Globally, road traffic accidents are a major cause of death and severe injuries. It is estimated that the number of deaths on the world’s roads at 1.5 million per annum puts road traffic injuries as the eighth leading cause of death globally. Understanding the influence of environmental factors on deaths and severe injuries will help in policy-making and the development of strategies in Limpopo Province. We, therefore, aim to study environmental factors that influence road deaths and severe injuries and to identify whether their impact on injury severity levels varies. The study was based on secondary data on road traffic accidents obtained from the Department of Roads and Transport in Limpopo Province. The data comprised 18 029 road traffic accidents for the period January 2009–December 2015. The study found that weekends (Saturdays and Sundays) had the highest number of accidents when compared to weekdays. The proportion of observations in each severity level was not constant across explanatory variables. The generalized ordered logit regression (GOLR) models seemed to be an effective predicting model that can be adapted to determine the influence of environmental factors on injury severity compared to the ordered logit regression (OLR) model. The results of the GOLR model suggest that environmental factors such as slippery road conditions, rainy weather, and spring season lower the likelihood of severe crash occurrence. On the other hand, poor or defective road surface, time interval (6 a.m. to 11 p.m.), and provincial roads have a higher likelihood of severe crash occurrence. To decrease the severity of injuries in the province, provincial roadways must be maintained regularly.

A Statistical Analysis of Hazardous Chemical Fatalities (HCFs) in China between 2015 and 2021

To study the occurrence characteristics of hazardous chemical accidents in China, mathematical analysis methods were used to study hazardous chemical fatalities (HCFs) in recent years. This research focused on selecting seven accident characteristics including time characteristics, geographical characteristics, accident type characteristics, hazardous chemical types, hazardous chemical production links, accident cause characteristics, and accident classification. The research results show that: (1) the occurrence of HCFs has obvious time-domain and regional characteristics, the number of casualties on Wednesday working days is the largest, 9:00–11:00 (inclusive) and 15:00–16:00 (inclusive) in a day are the two time periods with the largest number of incidents, the number of hazardous chemical accidents and deaths in economically developed coastal provinces is relatively high; (2) Analyze according to the type of accident statistics, the number of accidents and deaths caused by explosions, poisoning, asphyxiation, and fire are the largest; (3) Analyze according to the type of hazardous chemicals, drugs, compressed gas and liquefied gas, flammable solids, and spontaneous combustion materials, as well as flammable materials when wet are the types of hazardous chemicals that cause the most casualties; (4) Analyze according to the type of hazardous chemical accidents, the number of accidents and deaths caused in the production process is the largest; (5) Analyze according to the type of unsafe behavior by personnel, operator errors, the ignorance of safety, and the ignorance of warnings are the main causes of injuries and deaths caused by hazardous chemicals.; (6) Through single-factor feature analysis and multi-feature comprehensive cross-discussion, countermeasures, and suggestions for preventing and controlling accidents in hazardous chemical enterprises are put forward according to the characteristics of accidents caused by different accident characteristics.

Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach

Traffic accidents have significant financial and social impacts. Reducing the losses caused by traffic accidents has always been one of the most important issues. This paper presents an effort to investigate the factors affecting the accident severity of drivers with different driving experience. Special focus was placed on the combined effect of driving experience and age. Based on our dataset (traffic accidents that occurred between 2005 and 2021 in Shaanxi, China), CatBoost model was applied to deal with categorical feature, and SHAP (Shapley Additive exPlanations) model was used to interpret the output. Results show that accident cause, age, visibility, light condition, season, road alignment, and terrain are the key factors affecting accident severity for both novice and experienced drivers. Age has the opposite impact on fatal accident for novice and experienced drivers. Novice drivers younger than 30 or older than 55 are prone to suffer fatal accident, but for experienced drivers, the risk of fatal accident decreases when they are young and increases when they are old. These findings fill the research gap of the combined effect of driving experience and age on accident severity. Meanwhile, it can provide useful insights for practitioners to improve traffic safety for novice and experienced drivers.

Accident and pollution risk assessment for hazardous cargo in a port environment

The catastrophic environmental, life and monetary losses concomitant to the hazardous cargo accidents have remained a matter of critical concern for the maritime transportation officials. The factors that instigate these accidents while dealing with hazardous cargo in a port environment requires rigorous analysis and evaluation, which still remains in its infancy. In accord to these prevailing issues, this study focusses on the assessment of multifactor risks associated with the dealing of hazardous cargos inside a port. The methodology adopted is the amalgamation of expert judgment and literature review for the identification of factors and developing their causal relationship, while Bayesian Network (BN) for the inference, which was based on 348 past accident reports from the year 1990 to 2018. The results indicate that under normal circumstances, the probability of an accident with considerable consequences is 59.8, where human and management were found to be the highest contributing factors. Setting evidence at the environment and pollution accident to occur, the incidence probability of the "management" is raised by 7.06%. A sensitivity analysis was conducted to determine the most critical factors for the hazardous cargo accident. This study reveals that in order to evade the hazardous cargo accidents and curtail severity of the consequences, the port authorities, concerned government departments and other related institutions should pay specific attention to the qualification, training and attitude of the involved workforce. Moreover, the development and implementation of stringent safety protocols was also revealed to have critical prominence. This study holds practical vitality for enhancing safety and mitigating risks associated to hazardous cargo dealing in a port.

Severity analysis of road transport accidents of hazardous materials with machine learning

OBJECTIVE

The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity.

METHODS

The 2015-2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained.

RESULTS

C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents.

CONCLUSIONS

Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.

Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

Background: Construction activities not only provide the necessary conditions for citizens to live, but also cause fatal accidents. Methods: This study aimed to reveal the characteristics of fatal accidents in the construction industry in China based on statistical data. From 2010 to 2019, there were 6005 fatal accidents in China’s construction industry causing 7275 deaths. The important features of these fatal accidents, such as the type, time of occurrence, site location, severity, and geographical region of the accident, were carefully analyzed. Results: There were 258 major and severe construction accidents causing 1037 deaths, accounting for 4.3% and 14.25% of the total number of construction accidents and deaths in this period, respectively. As an important finding, more deaths occurred in August and on Mondays. The greatest number of construction accidents took place along openings and edges, accounting for 22.9% of all fatal accidents. Taking into account their economic development level and number of employees, Qinghai and Hainan experienced a higher mortality rate than Jiangsu. Falls from a high place were the dominant type of construction accident, accounting for 51.66% of all accidents. However, collapses were the primary type of major and severe construction accident, accounting for 60.09% of such accidents. The predicted number of construction deaths in 2020 is 887 according to the GM(1,1) model. Corresponding safety measures should be adopted to improve the working environment of the construction industry. Implications: The implications of these results with respect to the characteristics of construction accidents can be regarded as the foundation for accident prevention in practice.

Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach

A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers.

Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China

The metallurgical industry is a significant component of the national economy. The main purpose of this study was to establish a composite risk analysis method for fatal accidents in the metallurgical industry. We collected 152 fatal accidents in the Chinese metallurgical industry from 2001 to 2018, including 141 major accidents, 10 severe accidents, and 1 extraordinarily severe accident, together resulting in 731 deaths. Different from traffic or chemical industry accidents, most of the accidents in the metallurgical industry are poisoning and asphyxiation accidents, which account for 40% of the total number of fatal accidents. As the original statistical data of fatal accidents in the metallurgical industry have irregular fluctuations, the traditional prediction methods, such as linear or quadratic regression models, cannot be used to predict their future characteristics. To overcome this issue, the grey interval predicting method and the GM(1,1) model of grey system theory are introduced to predict the future characteristics of fatal accidents in the metallurgical industry. Different from a fault tree analysis or event tree analysis, the bow tie model integrates the basic causes, possible consequences, and corresponding safety measures of an accident in a transparent diagram. In this study, the bow tie model was used to identify the causes and consequences of fatal accidents in the metallurgical industry; then, corresponding safety measures were adopted to reduce the risk.

Characteristics, Cause, and Severity Analysis for Hazmat Transportation Risk Management

The accidents caused by hazardous material during road transportation may result in catastrophic losses of lives and economics, as well as damages to the environment. Regarding the deficiencies in the information systems of hazmat transportation accidents, this study conducts a survey of 371 accidents with consequence Levels II to V involving road transportation in China from 2004–2018. The study proposes a comprehensive analysis framework for understanding the overall status associated with key factors of hazmat transportation in terms of characteristics, cause, and severity. By incorporating the adaptive data analysis techniques and tackling uncertainty, the preventative measures can be carried out for supporting safety management in hazmat transportation. Thus, this study firstly analyzed spatial–temporal trends to understand the major characteristics of hazmat transportation accidents. Secondly, it presented a quantitative description of the relation among the hazmat properties, accident characteristics, and the consequences of the accidents using the decision tree approach. Thirdly, an enhanced F-N curve-based analysis method that can describe the relationship between cumulative probability F and number of deaths N, was proposed under the power-law distribution and applied to several practical data sets for severity analysis. It can evaluate accident severity of hazmat material by road transportation while taking into account uncertainty in terms of data sources. Through the introduction of the as low as reasonably practicable (ALARP) principle for determining acceptable and tolerable levels, it is indicated that the F-N curves are above the tolerable line for most hazmat accident scenarios. The findings can provide an empirically supported theoretical basis for the decision-makers to take action to reduce accident frequencies and risks for effective hazmat transportation management.