疲劳驾驶交通事故的严重程度影响因素分析

为探究和定量分析疲劳驾驶交通事故严重程度的影响因素,以广东省1 370条疲劳驾驶事故数据为基础,对比分析不同年份、时间段以及年龄段的疲劳驾驶交通事故特征;以交通事故严重程度为因变量,将其分为严重事故和非严重事故,从驾驶员年龄、驾龄、车辆类型等17个初步选择的自变量中筛选对疲劳驾驶交通事故严重程度具有显著影响的因素;采用二元Logistic回归模型分别对全体数据和不同道路类型下的数据建立疲劳驾驶交通事故严重程度预测模型,并对模型进行参数估计和检验。研究结果表明：模型拟合度良好,准确性高;对疲劳驾驶交通事故严重程度具有显著影响的因素有年龄、人员类型、车辆类型、道路类型、道路线形和能见度;车辆类型和道路线形是影响城市道路交通事故严重程度的重要因素,能见度是影响1,2级及其他更低级道路交通事故严重程度的重要因素。     

超大城市昼夜交通事故严重程度致因对比分析——以深圳市为例

为研究夜间交通事故严重程度致因,基于深圳市3年3 244起交通事故数据,获取昼夜交通事故分布的时空特征;进一步选取交通事故集聚的南山区、福田区、罗湖区的1 798起交通事故,以交通事故严重程度为因变量,以事故原因、日期、事故形态等10个因素为候选自变量,构建广义有序Logit回归模型,对比分析昼夜不同严重程度交通事故的影响因素。结果表明:路口路段类型、疲劳驾驶、事故日期在夜间模型参数估计值分别为0.493,-0.363,-0.309,而在日间模型表现为不显著,道路路面材料在日间模型参数估计值为-0.232,而在夜间表现为不显著;事故原因、道路横断面渠化方式等因素在日间和夜间所引起交通事故的严重等级均存在较大差异。     

Risk factors affecting injury severity determined by the MAIS score

Objective: Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score.

Method: In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006–2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS.

Results: This study showed the impact of variables, such as the presence of blood alcohol, the use of protection devices, the type of crash, and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analyzed as risk factors, showing that elderly and male road users are highly associated with MAIS 3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the magnitude of impact of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification.

Conclusions: The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.     

Risk factors in urban road traffic accidents

INTRODUCTION: The urban road traffic accident (RTA) risks for the city of Zagreb, Croatia, from 1999 through 2000 were analyzed with the aim of reducing the increasing injury incidence. METHOD: Simple and bivariate analysis using chi(2), odds ratio, and confidence interval of 95% was used to determine risks in three outcome groups: killed, severely, and mildly injured. RESULTS: There were 528 RTA victims consisting of 260 severely, 213 mildly injured, and 55 killed at the scene of an accident and during transportation. More fatal accidents occurred during night hours (OR=3.78; 95% CI, 2.08-6.85), on urban road links (OR=2.33; 95% CI, 1.30-4.19), and at exceeding speed limit (OR=2.56; 95% CI, 1.43-4.61). More people were injured than killed on urban junctions (OR=5.27; 95% CI, 2.21-12.57). The highest combined risk of dying or being severely injured was found in males, driving at excessive speed, on urban links, and during bad visibility (OR=16.15; 95% CI, 3.901-66.881). CONCLUSION: These results will influence the urban traffic police enforcement measures, which will change inappropriate behavior of drivers and protect the least experienced road users.     

A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana

Objective: The objective of this study was to explore the factors affecting motorcycle crash severity in Ghana.

Methods: A retrospective analysis of motorcycle crash data between 2011 and 2015 was conducted using a motorcycle crash data set extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. Injury severity was classified into 4 categories: Fatal, hospitalized, injured, and damage only. A multinomial logit modeling framework was used to identify the possible determinants of motorcycle crash severity.

Results: During the study period, a total of 8,516 motorcycle crashes were recorded, of which 22.9% were classified as fatal, 42.1% were classified as hospitalized injuries, 29.4% were classified as slight injuries, and 5.6% were classified as damage-only crashes. The estimation results indicate that the following factors increase the probability of fatal injuries: At a junction; weekend; signage; poor road shoulder; village settlement; tarred and good road surface; and collision between motorcycle and heavy goods vehicle (HGV). Motorcycle crashes occurring during the daytime and on the weekend increases the probability of hospitalized injury. The results also suggest that motorcycle crashes occurring during the daytime, in curves or inclined portions of roads, or in unclear weather conditions decrease the probability of fatal injury.

Conclusions: This study provides further empirical evidence to support motorcycle crash modeling research, which is lacking in developing countries. The ability to understand the various factors that influence motorcycle crash severity is a step forward in providing an appropriate basis upon which informed motorcycle crash policies can be developed. Particular attention should be given to the provision of road signage at junctions and speed humps and controlling traffic during the weekend. In addition, road maintenance should be carried out periodically to address motorcycle safety in Ghana.     

Investigating factors influencing pedestrian injury severity at intersections

Objective: Vehicle crashes that involve pedestrians at intersections have been reported occasionally. Pedestrian injury severity in these crashes is significantly related to driver and pedestrian attributes, vehicle characteristics, and the geometry of intersections. Identifying factors associated with pedestrian injury severity (PIS) is critical for reducing crashes and improving safety. For developing the proposed probit models, drivers involved in crashes are classified into 3 groups: young drivers (16 ≤ age ≤ 24), middle-aged drivers (25 ≤ age ≤ 64), and older drivers (age ≥ 65). This study determines that PIS is significantly but differently affected by these grouped drivers with different sets of explanatory variables.

Methods: A total of 2,614 crash records (2011–2012) at intersections in Cook County, Illinois, were collected. An ordered probit modeling approach was employed to develop the proposed model and examine factors influencing PIS. The likelihood ratio test was used to assess model performance. Elasticity analysis was conducted to interpret the marginal effect of contributing factors on PIS associated with different driver groups by age.

Results: The results show that 4 independent variables, including pedestrian age, vehicle type, point of first contact, and weather condition, significantly affect PIS at intersections for all drivers. Two additional independent variables (i.e., number of vehicles and traffic type) affect PIS for young and middle-aged drivers, and 2 other variables (i.e., divided type and hit-and-run related) are significant to PIS for both young and older drivers.

Conclusions: The independent variables significant to PIS at intersections for young, middle-aged, and older driver groups were identified and the marginal effect of each variable to the likelihood of PIS were assessed.     

基于Logistic的不同车型碰撞行人事故严重程度致因分析

为定量分析不同车型碰撞行人事故严重程度影响因素,以美国北卡罗来纳州2007-2016年人车碰撞事故数据为样本,将其分为小轿车、SUV、货车碰撞行人事故3类,以事故严重程度为因变量,交通参与者属性、道路、环境条件和事故特征为候选自变量,分别建立累计logistic模型进行对比分析,探究人、车、路和环境因素对人车碰撞事故严...     

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study

Objective: The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques.

Methods: This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007–2013 to identify VRU injury severity factors at HRGCs.

Results: The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.

Conclusions: The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.     

A 6-year survey of road traffic accidents in Southwest China: Emphasis on traumatic brain injury

Background: The objective of this study is to provide an up-to-date overview of the patterns of injuries, especially traumatic brain injury (TBI) caused by RTAs and to discuss some of the public health consequences. Methods: A scientific team was established to collect road traffic accidents occurring between 2013 and 2018 in Chongqing, Southwest China. For each accident, the environment-, vehicle-, and person- variables were analyzed and determined. The overall injury distribution and TBI patterns of four types of road users (driver, passenger, motorcyclist and pedestrian) were compared. The environmental and time distribution of accidents with TBI were shown by bar and pie chart. The risks of severe brain injury whether motorcyclist wearing helmets or not were compared and the risk factors of severe TBI in pedestrian were determined by odds ratio analysis. Results: This study enrolled 2131 accidents with 2741 persons of all kind of traffic participants, 1149 of them suffered AIS1+ head injury and 1598(58%) died in 7 days. The most common cause of deaths is due to head injury with 714(85%) and 1266(79%) persons died within 2 hours. Among 423 persons suffered both skull fracture and intracranial injury, 102 (24.1%) have an intracranial injury but no skull fractures, while none of the skull fractures without intracranial injury was found. Besides, motorcyclists without a helmet were at higher risks for all the brain injury categories. The risk of pedestrian suffering severe TBI at an impact speed of more than 70 km/h is 100 times higher than that with an impact speed of less than 40 km/h. Conclusion: It is urgently needed to develop a more reliable brain injury evaluation criterion for better protection of the road users. We believe that strengthening the emergency care to head injury at the scene is the most effective way to reduce traffic fatality.     

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.     

Investigating hazardous factors affecting freeway crash injury severity incorporating real-time weather data: Using a Bayesian multinomial logit model with conditional autoregressive priors

Introduction: It has been demonstrated that weather conditions have significant impacts on freeway safety. However, when employing an econometric model to examine freeway crash injury severity, most of the existing studies tend to categorize several different adverse weather conditions such as rainy, snowy, and windy conditions into one category, “adverse weather,” which might lead to a large amount of information loss and estimation bias. Hence, to overcome this issue, real-time weather data, the value of meteorological elements when crashes occurred, are incorporated into the dataset for freeway crash injury analysis in this study. Methods: Due to the possible existence of spatial correlations in freeway crash injury data, this study presents a new method, the spatial multinomial logit (SMNL) model, to consider the spatial effects in the framework of the multinomial logit (MNL) model. In the SMNL model, the Gaussian conditional autoregressive (CAR) prior is adopted to capture the spatial correlation. In this study, the model results of the SMNL model are compared with the model results of the traditional multinomial logit (MNL) model. In addition, Bayesian inference is adopted to estimate the parameters of these two models. Result: The result of the SMNL model shows the significance of the spatial terms, which demonstrates the existence of spatial correlation. In addition, the SMNL model has a better model fitting ability than the MNL model. Through the parameter estimate results, risk factors such as vertical grade, visibility, emergency medical services (EMS) response time, and vehicle type have significant effects on freeway injury severity. Practical Application: According to the results, corresponding countermeasures for freeway roadway design, traffic management, and vehicle design are proposed to improve freeway safety. For example, steep slopes should be avoided if possible, and in-lane rumble strips should be recommended for steep down-slope segments. Besides, traffic volume proportion of large vehicles should be limited when the wind speed exceeds a certain grade.     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways

Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.     

Impact of helmet use on traumatic brain injury from road traffic accidents in Cambodia

Objective: Rapid urbanization and motorization without corresponding increases in helmet usage have made traumatic brain injury due to road traffic accidents a major public health crisis in Cambodia. This analysis was conducted to quantify the impact of helmets on severity of injury, neurosurgical indication, and functional outcomes at discharge for motorcycle operators who required hospitalization for a traumatic brain injury following a road traffic accident in Cambodia.

Methods: The medical records of 491 motorcycle operators who presented to a major tertiary care center in Cambodia with traumatic brain injury were retrospectively analyzed using multivariate logistic regression.

Results: The most common injuries at presentation were contusions (47.0%), epidural hematomas (30.1%), subdural hematomas (27.9%), subarachnoid hemorrhages (12.4%), skull fractures (21.4%), and facial fractures (18.5%). Moderate-to-severe loss of consciousness was present in 36.3% of patients. Not wearing a helmet was associated with an odds ratio of 2.20 (95% confidence interval [CI], 1.15–4.22) for presenting with moderate to severe loss of consciousness compared to helmeted patients. Craniotomy or craniectomy was indicated for evacuation of hematoma in 20.0% of cases, and nonhelmeted patients had 3.21-fold higher odds of requiring neurosurgical intervention (95% CI, 1.25–8.27). Furthermore, lack of helmet usage was associated with 2.72-fold higher odds of discharge with functional deficits (95% CI, 1.14–6.49). In total, 30.1% of patients were discharged with severe functional deficits.

Conclusions: Helmets demonstrate a protective effect and may be an effective public health intervention to significantly reduce the burden of traumatic brain injury in Cambodia and other developing countries with increasing rates of motorization across the world.     

基于决策树模型的煤矿安全事故严重程度分析与预测

为将数据挖掘技术应用于煤矿安全管理,通过对我国1999—2015年29 000多条煤矿安全事故数据的研究,系统分析了事故发生的区域、时间、类型和企业信息等因素对事故严重程度的影响及彼此之间的相关性。通过构建决策树分类模型,在给定事故相关信息的基础上,对事故严重程度进行分类预测;基于数据类别不平衡的特点,采用欠采样的抽样方法,同时利用梯度提升的组合分类器来提高分类精度。结果表明,采用的数据挖掘模型在预测不同严重程度的事故上均达到了较高精度。     

Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets

IntroductionDespite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads.MethodGeometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity.ResultsCrashes involving cable barriers with a height between 30″ and 42″ were less severe than other traffic barrier types, while concrete barriers with a height shorter than 32″ were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers.Practical applicationsThe results show that using flare barriers should reduce the number of crashes compared to parallel barriers.     

Investigation on the role of traffic volume in accidents on urban highways

Introduction

Roadway safety is a major concern for the general public and public agencies, as roadway crashes claim many lives and cause substantial economic loss each year. In Iran, a large number of vehicles are involved in road accidents each year, which cause many deaths and extensive property damage; such accidents are among the major causes of death and disability in the country. Method: To reduce roadway accidents, the factors that affect the occurrence and severity of accidents should be scrutinized to prevent or reduce their effect. The method that many researchers have adopted to determine the effective parameters surrounding road accidents in recent years is through statistical modeling of accidents. In this article, the role of different kinds of vehicles in traffic flow are investigated separately in terms of the likelihood of crashes on urban highways, and the vehicles are divided into three groups: passenger cars, heavy vehicles, and light non-passenger car vehicles. Poisson and negative binomial (NB) regression models were applied to model the accidents in this research, which were categorized into two groups: no injury (property damage only) accidents and more severe (injury and fatal) accidents. Results: Ultimately, we conclude that light non-passenger car vehicles (i.e., taxis and motorcycles) play the largest role in the occurrence of crashes on urban highways for both types of accidents.     

营运驾驶员驾驶事故影响因素仿真研究

解决营运驾驶员驾驶事故频发的不安全行为问题,基于复杂适应系统（CAS）理论和多主体建模与仿真（ABMS）方法,构建营运驾驶员驾驶事故模型。通过调节车辆状况、管理者管理监督水平、营运驾驶员情绪状态、安全教育水平、驾驶经验和环境综合条件等因素,运用NetLogo仿真平台分析安全管理水平、营运驾驶员安全责任意识、心理负荷水平对驾驶事故的影响。研究表明:营运驾驶员驾驶事故的发生系统是一种典型的CAS,营运驾驶员驾驶事故是三大主体属性和行为环境交互的结果,且交互作用程度不同。因此,运输企业要考虑多种因素,采取相应的措施来降低营运驾驶员驾驶事故的发生。     

Risk factors for the severity of injury incurred in crashes involving on-duty police cars

Objective: This article explores the risk factors associated with police cars on routine patrol and/or on an emergency run and their effects on the severity of injuries in crashes.

Methods: The binary probit model is used to examine the effects of important factors on the risk of injuries sustained in crashes involving on-duty police cars.

Results: Several factors significantly increase the probability of crashes that cause severe injuries. Among those causes are police officers who drive at excessive speeds, traffic violations during emergency responses or pursuits, and driving during the evening (6 to 12 p.m.) or in rainy weather. Findings also indicate some potential issues associated with an increase in the probability of crashes that cause injuries. Younger police drivers were found to be more likely to be involved in crashes causing injuries than middle-aged drivers were. Distracted driving by on-duty police officers as well as civilian drivers who did not pull over to let a police car pass in emergency situations also caused serious crashes.

Conclusions: Police cars are exempted from certain traffic laws under emergency circumstances. However, to reduce the probability of being involved in a crash resulting in severe injuries, officers are still obligated to drive safely and follow safety procedures when responding to emergencies or pursuing a car. Enhancement of training techniques for emergency situations or driving in pursuit of an offender and following the safety procedures are essential for safety in driving during an emergency run by police.     

基于CA-SEM的煤矿安全事故风险因素结构模型

围绕煤矿安全风险管理的内涵,通过因子分析和层次聚类分析,识别煤矿安全事故风险主要包含人因风险、管理风险、信息风险、环境风险和设备风险5个风险因素层的22项风险因子。以人因风险层的6个风险因素为内源潜变量,其他4个风险层的16个风险因素为外源潜变量,构建煤矿安全事故风险因素的CA-SEM模型。运用SPASS17.0和AMOS7.0剖析各风险因素层以及各风险因子对煤矿安全事故风险的综合影响及其作用机理,从而为实现煤矿本质化安全提供决策依据。