Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network

Safety has always been an important component in the planning, design, and operation of highways. A framework for highway safety mitigation is outlined in this paper that provides a logical and comprehensive context within which efforts to improve highway safety can be made. This framework incorporates predictive methods of highway safety analysis to allow transportation officials to proactively improve the safety of the transportation system. This framework has been developed around the Roadway Safety Management Process contained in the Highway Safety Manual (HSM) and has six primary steps: 1) Network Screening, 2) Diagnosis, 3) Countermeasure Selection, 4) Economic Appraisal, 5) Project Prioritization, and 6) Effectiveness Evaluation. In addition to methods used in the HSM, alternative methods of predictive highway safety analysis are discussed in context of their application. This framework for safety mitigation can be applicable to transportation agencies worldwide to maximize benefits resulting from highway safety investment.

The quantity and damage degree caused by the traffic accidents on trunk highways always take the big part in the highway traffic accidents. Now, there are no more and complete evaluation methods for the trunk highway traffic safety. Whats more, the index system is not perfect. Based on the characters of traffic safety on trunk highways and feasible factors of information collection, this article confirms 3-level evaluation index system for 20 evaluation indexes and the evaluation methods for trunk highway traffic safety based on AHP. Taking Xiake Blvd of Jiangyin City as example, this article analyzes and proves the efficiency and feasibility of these methods. The evaluation results can provide the basis for the improvement of highway traffic safety.

Every year, traffic congestion and traffic accidents have been rapidly increasing in proportion to increasing number of vehicles. Although the roadway design and signal system have been improved to relieve traffic congestion, traffic casualties and property damage do not decrease. The traffic accident is a serious issue of society because vehicle is a primary means of transportation. This paper develops a real-time traffic accident detection system (RTADS): This system helps us to cope with accidents and discover the causes of traffic accident by detecting the accident. We gathered video data recorded at several intersections and used them to detect accidents at different intersections which have different traffic flow and intersection design. However, because the data gathered from intersections have incompleteness, uncertainty and complicated causal dependency between them, we construct probability-based networks which calculate based on the probability for correct accident detection. This system instantly sends the detected result to managers using accident alarm system. RTADS features real time accident detection and analysis of the cause of accidents. In performance evaluation, the proposed system achieved a detection rate of 97% with a correct detection rate of 92% and a false alarm rate of 0.77%.

The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks.

In recent years, with the extending highway construction scale in mountain areas, there is lack of traffic safety facilities in some mountain areas. The problems of unreasonable and irregular traffic guarantee engineering construction stand out. Especially in rural highway in mountain areas with complex topography, the traffic accident rates are increasing, causing lots of dead and economic losses. Studying deeply on rural highway traffic safety problems in mountain areas and improving rural highway traffic safety level are the problems that we need to solve urgently. This essay analyzes the law of rural highway traffic accidents in mountain areas and makes a conclusion that the existing problems in rural highway safety managements in mountain areas at present. Based on this, this essay analyzes systematically the influent factors of rural highway traffic safety and a conclusion can be drew that the frequent rural highway traffic accidents in mountain areas not only are caused by bad line type for roads and no security arrangements are set. At last, based on macro-management level, it proposes a series of positive highway safety management countermeasures including strengthening traffic safety education, establishing traffic safety management system, improving passenger transport policy and management system, formulating dangerous sections inspection mechanisms.

The highway infrastructure traffic safety performance evaluation is unstable and unreliable based on the traffic accident number or rate directly as well as other non-accident method. The principle of traffic accident spatial distribution along a highway and correlation between highway traffic safety performance and accidents positions for a period of time were researched. An accident aggregation indicator (AAI) was established based on the principle. In order to validate its availability, AAI was applied to evaluate infrastructure traffic safety performance of 6 region highway networks in Heilongjiang Province, China by using traffic accident data occurring on them in the period 3 years. The remarkable difference of AAI was found on 6 region highway networks, which proved the feasibility of the evaluation method based on AAI.

A probabilistic safety analysis methodology based on Bayesian networks models for the probabilistic safety assessment (PSA) of highways and roads is presented. The main idea consists of (a) identifying all the elements encountered when travelling the road, (b) reproducing these elements by sets of variables, (c) identifying the direct dependencies among variables, (d) building a directed acyclic graph to reproduce the qualitative structure of the Bayesian network, and (e) building the conditional probability tables for each variable conditioned on its parent nodes. Since human error is the most important cause of accidents, driver’s tiredness and attention are used to model how the driver’s behaviour evolves with driving and how it is affected by the environment, signs and other factors. A computer program developed in Matlab implements the Bayesian network model from the list of road items and a set of parameter values given by a group of experts. In this way, the most critical elements can be identified and sorted by importance, thus, an improvement of the global safety of the road can be done savings time and money. The proposed methodology is illustrated in real examples of a Spanish highway and a conventional road.

Bayesian-network-based safety risk assessment for steel construction projects

Human errors are one of the main causes of accidents in the construction industry. Controlling all persons involved in a project with respect to safety issues are time-consuming and costly. It is clear that in each working group there is a person who affects others because of his experience, knowledge and etc. Therefore, improving the safety of an effective person's behaviour can lead to the safety of other people's behaviours. This study presents a new framework to detect effective persons by using social network analysis. To do this, firstly the most frequent accidents are identified and next, for each accident according to their activities and working groups the most influential person is identified. Finally, the influential level of each person in different accidents is combined with a new decision-making method to find the most influential person. The proposed framework was implemented in one of the construction projects in Iran.

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

With the advent of the era of traffic information technology, traffic data appears to be extremely large, and the traditional way of managing highway crashes has become difficult to adapt to the many implications of the complicated data. In this paper, we design a highway traffic safety assessment system based on crash data mining and modeling. By constructing a visualized highway traffic crash data analysis and mining platform, which is used as a basis to gather traffic crash analysis solutions and build a think tank, a safety assessment system is formed that integrates traffic crash information visualization, crash data analysis and mining technology and comprehensive solutions for crash risk prediction. The system provides crash data pre-processing solutions, crash pattern mining solutions, factor analysis solutions and crash risk prediction solutions. Based on the above solution, the basic framework and seven main functional modules of the highway traffic safety assessment system are designed and implemented. The system achieves systematic and automated data processing, pattern mining, factor analysis and risk prediction, and aims to make highway traffic safety management more efficient.

The construction industry is one of the most hazardous industries in many countries. Many studies have asserted that industrial accidents could be prevented by eliminating their root causes. However, given that accident occurrence processes are considerably complex and often invisible, it is difficult to identify and eliminate the root causes. Based on this recognition, this paper aims to analyze the causality of construction accidents on the basis of direct causes that are classified into unsafe actions (UA) and unsafe conditions (UC). A logistic regression is applied to examine associations between UAs and UCs and their significances in triggering construction accidents. Then, a Delphi method is applied to determine the relationships between direct and root causes of construction accidents. This study contributes to an improved understanding of the complex causal process of construction accidents, which is a necessary stepping-stone to prevent construction accidents. Meanwhile, only one-to-one combinations of UCs and UAs are considered in this paper. Thus, follow-up studies to examine the impact of one-to-many or many-to-many combinations are needed.

Work accidents that occur in the workplace can harm workers and companies both materially and non-materially and temporarily or permanently. Workplace accidents are unwanted events that can cause physical injury to a person even fatal to death or lifelong disability and property damage. The main cause of accidents in the workplace is due to a lack of knowledge, skills and other factors from within the individual who will eventually tend to behave unsafely at work. This study is quantitative research with research type observational analytical research with a cross-sectional design. This study employed a total population sampling method, including all 35 participants from X Company and residents of the Z Plant area, conducted during March to April 2024. Data were collected through questionnaires and interviews. Spearman correlation analysis was used to assess the strength of the relationship. The results showed that the strong relationship between knowledge (r = 0.377) with safety behavior was in the low category. While the strong relationship between skills (r = 0.470) with safety behavior is in the medium category.

Crane usage is pervasive on construction sites, however, it is associated with a notably high accident rate. The analyzing of crane accident risks is essential for accident prevention, control, and ensuring the safety of lifting operations. Hence, significant emphasis should be placed on understanding the interaction among various risk factors. This paper proposes a quantitative coupling method for human, machine, management, and environmental risk factors in crane accidents, leveraging Bayesian networks (BN) and the N-K model. Firstly, text mining technology and fault tree analysis are employed to analyze the causes of crane accidents and categorize the associate risk factors. Secondly, the types of risk coupling resulting from human, machine, management, and environmental risk factors are defined. Thirdly, the BN model is developed based on the analysis of crane accident risksand its N-K model. Fourthly, the parameters of the risk coupling nodes in the developed BN are determined based on the calculation results of the N-K model. Finally, for the risk coupling types with high coupling values and the first-level node and second-level node, the failure probability is analyzed through posterior probability and sensitivity analysis. The results indicate that factors related to man and management significantly impact crane accidents and warrant enhanced attention. The interplay among multiple risk factors significantly influences the probability of crane accidents, necessitating careful attention.