Application of generalized link functions in developing accident prediction models

The Bayesian Poisson–Gamma hierarchy, leading to the negative binomial distribution, has been the standard practice in developing accident prediction models. To linearize the relationship connecting the mean of the negative binomial distribution to relevant covariates, a canonical log link has traditionally been used. Typically, little information is available regarding the choice of a particular link. To avoid link misspecification, it is proposed to nest the canonical log link model within a generalized link family and subsequently use the full Bayes method for parameter estimation, performance evaluation and inference. The proposed approach was applied to a sample of accident and traffic volume data corresponding to 99 intersections in the city of Edmonton, Alberta. The results showed that both the generalized link model and the traditional canonical link model provided adequate fit to the data. However, the Bayes factor provided a clear statistical support for the use of the generalized link approach. A procedure for link validation is also described. It allows the users (e.g., road authorities) to consider the changes in predicted accidents that will result if a generalized link is used instead of a canonical link. If a certain maximal change is tolerated, the canonical link can be used to analyze the data; otherwise the generalized link is worth the extra efforts and should be adopted. When compared with the traditional approach, the generalized link model was found to predict a lower number of accidents whenever there is a heavy traffic at the major approach, especially if combined with light flow on the minor approach. The paper concludes by identifying out areas for further research.     

基于哈默“人的差错理论”的道路交通安全研究

以道路交通安全系统为背景,人的差错为研究方向,运用"哈默"人的差错分类理论,从机动车驾驶员和非机动车驾驶员、乘客及行人两种人群的差错角度,对2004—2006年3年间我国交通事故发生率和死亡人数进行较有针对性的分析归类和研究,进而分别提出基于主动性和被动性两方面的交通事故人因预防措施。通过"哈默"全新的分类方法,全面分析交通行为中人的差错类型,为道路交通事故预防体系的研究、建立以及进一步的发展,提供了有参考价值的基于人的行为机理的理论依据和合理化建议。     

Systemic accident risk assessment in air traffic by Monte Carlo simulation

A systemic accident model considers accidents as emergent phenomena from variability and interactions in a complex system. Air traffic risk assessments have predominantly been done by sequential and epidemiological accident models. In this paper we demonstrate that Monte Carlo simulation of safety relevant air traffic scenarios is a viable approach for systemic accident assessment. The Monte Carlo simulations are based on dynamic multi-agent models, which represent the distributed and dynamic interactions of various human operators and technical systems in a safety relevant scenario. The approach is illustrated for a particular runway incursion scenario, which addresses an aircraft taxiing towards the crossing of an active runway while its crew has inappropriate situation awareness. An assessment of the risk of a collision between the aircraft taxiing with an aircraft taking-off is presented, which is based on dedicated Monte Carlo simulations in combination with a validation approach of the simulation results. The assessment particularly focuses on the effectiveness of a runway incursion alert system that warns an air traffic controller, in reducing the safety risk for good and reduced visibility conditions.     

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

Introduction: With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted. Method: Utilizing DI-related crash data collected from North Carolina for the period 2013–2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset. Results: The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.     

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.     

Road casualties and enforcement: distributional assumptions of serially correlated count data

OBJECTIVE: Road safety data are often in the form of counts and usually temporally correlated. The objective of this research is to investigate the distributional assumptions of road safety data in the presence of temporal correlation. METHODS: Using the generalized linear model framework, four distributional assumptions are considered: normal, Poisson, quasi-Poisson and negative binomial, and appropriate models are estimated. Monthly casualty and police enforcement data from Greece for a period of six years (January 1998-December 2003) have been used. The developed models include sinusoidal latent terms to capture the temporal serial correlation of observations. Several statistical goodness-of-fit diagnostic tests have been performed for the results of the estimated models, and the predictive capabilities of the models are investigated. RESULTS: The residuals of the quasi-Poisson and negative binomial models do not show any serial correlation. The signs of the estimated coefficients for all models are consistent and intuitive. In particular, a negative coefficient value for the number of breath alcohol controls indicates that the number of persons killed and seriously injured decreases as the intensity of breath alcohol controls increases. The Poisson model fails to capture the overdispersion in the data, thus underestimating the standard errors of the estimated coefficients. CONCLUSION: The results suggest that the quasi-Poisson and negative binomial outperform the normal and Poisson models in this application. The findings of this research demonstrate a clear link between the intensification of police enforcement and the reduction of traffic accident casualties. In particular, an increase in the number of breath alcohol controls in Greece after 1998 contributed to a reduction in the number of persons killed and seriously injured from traffic accidents.     

一种改进的事故再现蒙特卡罗优化算法

为使事故再现结果更符合实际情况,对事故再现中的计算过程进行优化。基于蒙特卡罗方法和随机加权方法,提出一种改进的事故再现蒙特卡罗优化算法。该算法以二维碰撞模型和车辆轨迹模型为计算模型,选择碰撞点位置、碰撞前速度、法向恢复系数为优化参数,以实际车辆碰撞后运动轨迹离差最小为优化目标。分别用所提出的改进算法和Pc-Crash中的优化方法对一算例进行优化。结果表明,改进算法在准确度和稳定性等方面优于Pc-Crash中的方法。利用改进的事故再现蒙特卡罗优化算法,不仅能获得最优的事故再现结果,还能获得再现结果落在任意区间的概率。     

基于价值函数的城市道路交通安全测度模型

根据交通事故在时间和空间上的分布特性,建立基于时空分布的城市道路交通安全的测度指标体系;在分析道路交通事故的主要特征和交通事故的生成过程的基础上,利用价值函数构建城市道路交通安全的测度模型。并对城市道路交通安全的测度值进行等级界定,提出道路交通事故预防和控制手段。城市道路交通安全的测度模型是在最大可能地控制系统中的人的不安全行为,使城市道路网络系统的效益达到最佳化。应用结果表明:利用测度,可以找出影响高速公路交通安全的问题成因和限制因素,为高速公路规划和管理提供科学决策依据。     

109国道宁夏石中段交通事故特性及成因分析

搜集了平原微丘区二级公路109国道宁夏石中段2001—2003年间的道路交通事故资料,分析其交通事故的时间分布、空间分布、肇事驾驶员等分布规律。研究表明:早晚交通高峰小时过后是交通事故高发时段,交通事故月分布规律受季节影响较大,路段交通事故形态中以尾随相撞类事故居多,发生于路段的交通事故数高于交叉口处的交通事故数,驾龄小于4年和年龄18～25岁的驾驶员最容易造成交通事故。进而提出加强交通管理,增设减速震动线等交通安全设施降低主线车速,加强易发交通事故的驾驶员人群的后期培训和教育等防控措施。以期有助于缓解我国二级公路严峻的交通安全形势,增强交通事故预防的科学性。     

Random parameter models for accident prediction on two-lane undivided highways in India

Introduction

Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.

Method

The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.

Results

The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.

Conclusions

The paper is concluded with a discussion on modeling results and the limitations of the present study.     

Data mining of tree-based models to analyze freeway accident frequency

INTRODUCTION: Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. METHOD: This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. RESULTS: The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. IMPACT ON INDUSTRY: By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.     

Smoking,belt use,and road accidents of youth in Kuwait

Youth, worldwide, are involved in a disproportionate number of road accidents, in general, and fatal accidents, in particular. Kuwait is no exception to this trend. The objectives of this funded research were to identify the trends in belt use, smoking in motion, and road accidents of young drivers in Kuwait. Findings of a person-interview questionnaire survey of 1467 randomly selected young drivers in Kuwait are presented. The socio-economic, driving experience, belt use, smoking behaviour, and road accident involvements of the sample young drivers are given. The interrelationships between the socio-economic, belt use, smoking behaviour, and road traffic accidents are also examined. Degrees of association between these variables are determined, and the extent of potential contribution of smoking-in-motion to causes of road accidents is also determined. Young female drivers were found to be generally safer drivers than their young male counterparts; those who smoked while driving used seat belt less, and had a higher involvement rate in road traffic accidents. A number of recommended measures for the improvement of road safety in Kuwait end the paper.     

基于SNA的重特大道路交通事故风险指标体系构建

为加强对重特大道路交通事故的控制和预防,从95份重特大道路交通事故调查报告中提取相关数据,运用社会网络分析（SNA）构建重特大道路交通事故原因间的关系网络。通过社会网络相关指标,找到导致事故的关键原因,以及哪些原因组合导致事故的能力最强,得到重特大道路交通事故原因特征。建立重特大道路交通事故风险指标体系,并通过实例分析验证指标体系的合理性。研究结果可以进一步加深对重特大道路交通事故原因以及彼此间相互作用关系的认识,风险指标体系的建立可为重特大道路交通事故的预防预警工作提供参考。     

灰色关联分析在道路交通事故中的应用

全国或某一地区的道路交通安全系统是一个灰色系统,可应用灰色关联分析法方便地确定影响交通安全的主要相关因素。由灰色关联度导出灰色关联序,以进行优势分析,从而得到交通事故的最(准)优因素及最(准)优特征。根据1990—2000年全国道路交通事故以及全国人口、机动车、公路里程和客货运输量等主要相关因素统计资料可知,货运量、货运周转量和客运量是影响交通安全的主要相关因素,死、伤人数是交通事故的主要特征和指标。由哈尔滨市2000年道路路段交通事故与交通组成统计资料可知,城市道路交通事故的主要影响因素是各类货车、摩托车和大型车。因此,加强货车和货运管理对提高我国交通安全水平具有重要意义。     

高速公路交通事故分析及预防对策研究

利用济青高速公路 1998～ 1999年发生的 10 85起交通事故数据 ,在资料收集、处理和统计分析的基础上 ,对影响高速公路交通事故的人、车辆、道路环境、天气和交通量等主要因素进行了分析 ,并与国外的统计结果进行对比研究 ,预测出随着高速公路交通量的增长 ,事故率将会增加。此外 ,笔者针对影响高速公路事故因素提出了主动性、被动性预防对策 ,这些对策的实施将会减少高速公路事故的发生     

Relationship between traffic fatalities and drunk driving in Japan

OBJECTIVE: The present study was performed to clarify the relation between alcohol use and traffic fatalities in accidents involving motor vehicles in Japan. METHODS: Data on traffic accidents were collected from Fukuoka Prefectural Police records of traffic accidents which occurred in that prefecture between 1987 and 1996. Multiple logistic regression models were used to assess the effect of alcohol use on the risk of traffic-accident death. RESULTS: The data showed that 58,421 male drivers were involved in traffic accidents during the 10-year study period, and that 271 of these were killed as a result of the accident. Alcohol use was significantly associated with speed, seat belt use, time, and road form. Among male motorcar drivers, the odds ratio of alcohol use before driving, after adjusting for age, calendar year, time, and road form, was 4.08 (95% confidence interval, 3.08-5.40), which means that about 75% of fatalities (attributable risk percent among exposed) might have been prevented if drivers had not drunk before driving. CONCLUSIONS: Alcohol use before driving resulted in a 4.08-fold increase in the risk of death in a traffic accident. It is suggested that alcohol use is considered an important risk factor for fatality in traffic accidents.     

基于蒙特卡洛模拟的多米诺事故风险量化管理*

为降低危化品相关的化工事故造成的人员伤亡和财产损失,以化工多米诺事故为研究对象,探讨由初始事故引发1个或多个次生事故的连锁反应机理与风险评估方法。提出应用蒙特卡洛模拟对多米诺事故风险进行动态量化的方法,梳理化工多米诺事故风险的识别、分析、评定、处理全周期管理流程,并以1个天然气压气站为案例,验证基于蒙卡模拟的化工多米诺事故风险量化方法的有效性。结果表明:该方法可以更准确地对化工多米诺事故风险进行定量评估。多米诺事故风险全周期管理流程的梳理能够有效指导化工企业开展安全管理、事故预防等工作。     

基于贝叶斯网的交通事故机理分析

针对道路交通事故的形成机理进行定性、定量研究,根据我国道路交通事故记录数据特征,应用贝叶斯网对事故发生概率进行定量分析.引入"驾驶员紧张度"和"道路线形合理度"两个隐节点,建立了事故分析的贝叶斯网多层隐类模型,采用最大似然估计方法确定了模型的边缘概率和条件概率.将贝叶斯网模型应用于国道104二级公路(K1310+000～K1330+000)的事故分析中,运用贝叶斯网分析软件包Netica对其历史事故记录数据进行分析.结果表明: 贝叶斯网不仅可以定量计算某种道路交通状态下的事故发生概率,而且可以找出影响事故概率的关键原因和最不利状态组合(事故概率最大时的道路交通状态).     

北京市道路交通安全现状分析及控制策略研究

在分析北京市道路交通安全形势以及事故特点的基础上,运用交通冲突理论分析道路交通事故发生的机理,论述道路交通事故发生的过程。并从安全系统工程学角度,使用交通事故显性/隐性致因模型对道路交通事故的致因因素进行分析,强调管理因素的重要性。最后,结合北京市道路交通现状,综合运用3E对策,讨论了改善道路交通安全的控制策略。通过加大违章行为惩罚力度、培养安全文化、建立道路交通事故应急救援体系以及应用智能交通技术等措施,消除道路交通系统中人的不安全行为和物的不安全状态,提高北京市道路交通安全水平。     

道路交通事故BP神经网络预测研究

在分析道路交通事故影响因素的基础上,确定道路交通事故评价指标体系。该体系包含交通事故次数、死亡人数、受伤人数3个输出指标及12个影响因素。利用人工神经网络具的强非线性逼近、模糊推理、自学习的优点,建立道路交通事故BP神经网络预测模型。模型对我国2006年、2007年、2008年的交通事故情况进行预测,其中,2006年、2007年预测精度较高,2008年预测误差稍大,可能的原因为2007年12月修订的《中华人民共和国道路交通安全法》对减少交通事故起到较大作用。