基于CIDAS数据与可解释模型的行人交通事故风险识别

行人道路交通事故是一种常见的交通事故,为了构建有效的行人交通安全防治体系,论文使用中国事故深度调查（CIDAS）数据集进行分析研究。采用多次重复的K折交叉验证评估,并确认随机森林模型在该数据集上具有统计学功效后,利用基于排列的特征重要性算法对影响行人交通事故的特征进行了量化分析。随后对重要事故特征的数据进行统计,并使用卡方检验确定随机性的影响。研究表明,事故参与人员数、行人年龄段、事故发生时间与道路最高允许车速是影响行人交通事故后果的最重要特征。整体趋势表明事故参与人员数越多,事故后果越严重;对于13岁及以上的人群,行人年龄越大发生事故的后果也更严重;在凌晨0:00-4:00发生的事故中,事故的严重程度明显高于其他时间段;在限速为80km/h及以上的道路上发生事故的后果更严重。     

基于机器学习的危险货物道路运输事故影响因素分析

为研究危险货物道路运输事故严重程度的影响因素,以2015—2019年发生在我国的1267起危险货物道路运输事故案例为基础,比较决策树C5.0、支持向量机和多层感知器对危险货物道路运输事故数据的分析性能,并选用表现最佳的模型探索影响3种不同严重程度的事故发生的主要因素.结果表明,决策树C5.0整体表现最佳.影响仅财产损失事故发生的主要因素依次为直接事故形态(刮擦、泄漏、火灾和其他),间接事故形态(泄漏)和路段类型(站区);影响受伤事故的发生的主要因素依次为直接事故形态(侧翻、撞固定物、2车追尾、2车相撞、冲出路面和坠车),间接事故形态(泄漏和侧翻),路段类型(普通路段、桥梁、隧道和出入口),道路类型(省道和国道)和时间(07:00—12:00);影响死亡事故发生的主要因素依次为直接事故形态(多车相撞、多车追尾和爆炸),危化品类别(氧化性物质、气体和易燃固体),间接事故形态(火灾和爆炸)和道路线形(长下坡和急弯).      

高速公路入城段碰擦与追尾事故分析

高速入城段的车流混杂,驾驶环境复杂,交通事故高发,其中以碰擦与追尾事故为主。采集G2京沪高速公路上海入城段事故数据,以平均兴趣度作为最优指标,采用改进的关联规则Apriori算法挖掘高兴趣度的碰擦与追尾关联规则,发现碰擦、追尾事故与天气、车型、地点等因素关联较大。在上述基础上,基于支持向量机构建高速公路入城段的追尾与碰擦判断模型,输入所有事故因素组合,得出所有可能发生追尾与碰擦的因素组合。研究成果可为高速公路安全管理与事故预防工作提供理论支持,制定精准的交通安全改善措施和管理。     

基于GIS的道路交通事故分析系统功能设计初探

基于GIS的道路交通事故分析系统实现了交通事故位置点的地图标注和信息采集,从而可以对北京市全市的道路交通事故状况进行动态监控。系统设计中涉及的事故信息采集和地理信息技术符合公安部的有关技术规范,模型参数可根据实际情况进行调整。该系统对提高道路事故隐患排查、事故防治等具有良好的应用前景。     

Safety Characteristics of Two-lane Highway Sections Passing through Towns/villages in Mountainous Area Based on Negative Binomial Prediction Model

针对我国交通事故呈现出的在公路穿村镇路段聚集的趋势,在某山岭地形区域进行了双车道公路的交通事故、交通组成、道路和路侧等因素的大量数据采集,并对公路穿村镇路段的整体安全特性进行了分析,采用负二项模型等事故预测模型和相关检验理论对模型形式,以及公路穿村镇路段全部事故和追尾、碰撞、路侧等不同形态的事故规律特性进行了深入分析.研究结果发现,公路穿村镇路段的事故时空分布呈一定规律性;交通量、混杂率和道路横坡度为影响公路穿村镇路段交通安全的3个显著因素,且均呈正向影响.     

基于Ordered Probit模型的追尾事故严重性影响因素分析

为了减少机动车事故带来的人身财产损失,给相关管理部门提供决策意见的理论支持,分析追尾事故的独特规律,以及研究环境因素、道路因素、车辆因素和驾驶员因素等影响因素与追尾事故严重程度之间的致因关系至关重要。通过对北卡罗来纳州2010—2014年的交通事故数据进行筛选分析,最终得到1 315条具有完整信息的追尾事故数据,并对数据重新编码。考虑到事故严重性的分类具有次序的特性,研究拟采用Ordered Probit模型(ORP)对影响追尾事故严重程度的各种因素进行回归分析。为了找出具有统计学显著的影响因素,采用向后删除变量法筛选变量,从而得到了包含全部显著因素的模型。最终对模型进行了平行线检验和似然比检验,分析模型的统计学合理性,并选取指标进行了模型数据拟合度优劣的评价。研究结果表明:使用安全带、光照条件、道路线型条件、交通控制条件以及道路交通量这5个方面的因素与追尾事故严重程度的相关度较大。模型的边际影响值显示,正确使用安全带、良好的光照条件、合理的道路线型条件、适宜的交通控制措施能够显著降低追尾事故的严重性;而过大的交通量由于易产生多车追尾事故,事故严重性程度增加。显著性检验结果显示ORP模型对于分析追尾事故严重性及其影响因素具有明显作用。     

基于SVM的水上交通事故严重程度的影响因素研究

为研究水上交通事故中事故严重程度的影响因素,减小水上交通事故发生时的人员伤亡及财产损失,对2015-2016年的水上交通事故统计数据的分析.选取了水上交通事故数据中的船舶类型、事故发生时间、地点、船舶吨位、能见度和风力等级等相关因素建立了事故信息库.根据水上交通事故造成的人员伤亡数量和财产损失的大小,将事故严重程度分为3个等级,并建立了基于支持向量机(SVM)的三分类模型.然后通过交叉验证以及网格搜索算法优化SVM分类模型的惩罚参数和核函数参数,得到最优的分类模型.模型建立后,利用SVM-RFE算法求解上述影响因素对事故严重程度的权重值并排序,筛选出对于事故严重程度影响最大的因素.结果表明,支持向量机三分类模型总体分类准确率可达70% 以上;同时自沉事故、渔船事故和秋季发生的事故易造成较大的人员伤亡;危化品船舶,内河发生的事故和渔船易造成较大的财产损失.      

高速公路上坡段交通事故特征及致因分析

通过对宁杭高速公路东庐山段2006年1月~2010年7月的交通事故资料的统计,从事故地点、事故时段、事故形态和事故车型等方面分析了上坡段交通事故的主要特征.研究发现:高速公路上坡段发生交通事故的风险要远大于下坡及其他路段;事故多发生在长大上坡段的中后部和竖曲线段;事故形态主要是追尾、撞护栏和撞固定物,而且重特大交通事故中追尾事故比例大;夜间的事故率和严重程度高于白天,尤其是凌晨02:00~06:00时为重特大交通事故高发期;大货车和小客车是坡段内交通事故的主要车型.上坡段事故的致因为疲劳驾驶、车辆速度离散性大、交通组成复杂.      

基于SVM事故分类的连环追尾事故影响因素分析

以美国公路2013—2015年所有的追尾事故数据为样本,研究导致连环追尾事故发生的关键影响因素.通过随机森林进行特征筛选,选取了与时间、驾驶人、车辆、道路和环境有关的14个相关因素作为支持向量机的输入变量,建立了基于SVM的2车追尾事故与连环追尾事故二分类模型.得到分类准确率:训练集为97.42%,测试集为80.32%,AUC为0.7,说明2种事故之间存在显著差异,且SVM模型能够较好的将2种事故进行区分.根据SVM-RFE算法计算影响分类效果的特征变量的相对重要度,得到4个对2种事故产生区别影响较大的因素,依次为:碰撞前首车的运动情况、道路的限速、季节和车道数.进一步对比各因素下2种事故发生的百分比发现,在首车停车或减速、道路限速超过80 km/h、夏季以及车道数大于2车道的情况下,更容易发生连环追尾事故.      

鉴别道路交通事故多发点的模糊评价法

道路交通事故多发点的鉴别是改善道路交通安全状况最关键、最重要的一步,当被鉴别路段或交叉口的道路条件、交通条件相差较大时,采用原有的鉴别方法就难以保证鉴别精度,而又鉴于交通安全概念的模糊性、评价者思维方式的多样性以及评价结果常以口语化词汇表达的特点,提出了鉴别道路交通事故多发点的模糊评价方法。经过分析比较,模糊评价方法采用的指标是亿车公里死亡率、万车死亡率、万车当量总事故次数及事故严重性指数。在此基础上,建立了二层模糊数学评价模型,以哈尔滨市区内部分道路路段及交叉口的交通事故多发点作为应用示例进行了道路交通事故多发点鉴别。     

Factors affecting severity of motorcycle accidents on Thailand's arterial roads: Multiple correspondence analysis and ordered logistics regression approaches

Thailand was classified as a middle-income country and ranked second highest in terms of road traffic fatality rate in the world in 2015. By 2018, this ranking went up to ninth in world which may be because of various earnest safety policies implementation, supporting road safety research and establishing a road safety directing center. However, crash fatality rate has considerably remained high until recent year, indicating a clear need for further related research. Considering severity of the crashes, the majority of fatal crashes involved the motorcycle road user. Therefore, motorcycle crashes are important issues and should be considered to mitigate fatality due to immoderate proportion of motorcycle road user and motorcyclist fatality. This study aims to identify factors that influence the severity of motorcycle accidents on Thailand's arterial roads by employing ordered logistic regression and multiple correspondence analysis. The results demonstrated that although both analyses were relatively different, they provided similar results. Age, road lanes, and helmet wearing were significant factors that influenced the severity of motorcycle accidents. The results could serve as reference for planning strategies or organizing campaigns to reduce and prevent death owing to road traffic accidents, which may enhance the overall image of road traffic safety in Thailand.     

Evaluation of risk factors for road accidents under mixed traffic: Case study on Indian highways

This paper presents an evaluation of risk factors for highway crashes under mixed traffic conditions. The basis of selecting study sites was abutting land use, roadway, and traffic characteristics. Accordingly, the study selected thirteen segments on the existing highway network in the state of West Bengal of India, covering a wide spectrum of such road attributes. A systematic investigation based on site-specific accident data to capture the highway sections' safety features revealed that the crash rate has steadily increased for years with traffic regardless of roadway category and conditions. A number of risk factors that affect road accidents were identified; they are mid-block access, pavement and shoulder conditions, vehicle involvement, time of day, and road configuration, i.e., two and multi-lane. The empirical observation indicates that the crash rate is relatively lower on multi-lane highways; however, the severity of any crash on such a road is relatively high. Notably, the crash frequencies on such roads are less during daylight hours due to the lane-based unidirectional traffic movement. This is quite the opposite during nighttime when drivers exhibit an inability to meet traffic contingencies, thereby increasing crash risk. The majority of crashes on two-lane highways are, on the other hand, due to unsafe driving manoeuvers. The study also observed that frequent mid-block accesses and poor shoulder conditions reduce scopes to rectify driving errors and increase crash risk as a consequence. The paper subsequently suggests proactive approaches to identify safety deficits at the time of planning and designing.     

Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

Effectiveness of enforcement resources in the highway patrol in reducing fatality rates

Motor vehicle crashes are a leading cause of death in the United States. Wyoming initiated a safety study to investigate the underlying causes of high crash rates since it has one of the highest fatality rates in the nation. Research has shown relationships between increased enforcement activity and road crash/fatality reduction. However, little research has attempted to quantitatively measure the impact of various forms of police enforcement, such as the percentage of enforcement time and the quantity of resources, on fatality rate. Therefore, this study was set forward to fill this gap. Data from the highway patrol in Wyoming and the surrounding states were used in this study. Although Wyoming and these nearby states have very similar features in terms of geography and weather, they are different in terms of road mileage and traffic. Therefore, the data was normalized based on highway mileage and miles traveled. Enforcement efforts were compared in terms of allocated enforcement budget, number of sworn officers, and time spent patrolling. The results indicated that there are negative relationships between fatality rate and budget, number of officers, and active hours on the field. This paper also investigated which variable is the best predictor of fatality rate. The results indicated that time spent on the field by highway patrol officers is the best indicator of fatality rate. It was found that although some states like Wyoming have a higher number of sworn officers, they spend less time actively enforcing highway safety. This study provides information needed for authorities to allocate more funding to the highway patrol, and for the highway patrol to spend more time on the road.     

Using machine leaning techniques for evaluation of motorcycle injury severity

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.     

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila,Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.     

山区公路小半径弯道路段事故严重度影响因素及其异质性比较分析

为分析影响山区公路小半径路段典型事故的严重程度的相关因素及其异质性效应，基于某山区双车道公路1 067起交通事故数据，从驾驶员、车辆、道路和环境4个方面选取15个潜在特征变量，采用二项Logit模型和随机参数二项Logit模型，分别构建小半径弯道路段上追尾碰撞、正面碰撞和侧面碰撞3类典型事故的严重度分析模型，分析3类典型事故严重度的显著影响因素，并采用边际弹性系数量化分析影响因素的作用强度。结果表明，小半径弯道路段上不同形态事故的严重度影响因素存在明显差异:①追尾碰撞严重度的显著影响因素依次为摩托车、夜间、弯道转角、驾驶员年龄、季节，摩托车和冬季分别是服从(2.716.1.5642)和(-1.495，2.1162)正态分布的异质性影响因素，导致发生伤亡事故的概率为95.72%和23.58%；②正面碰撞严重度的显著影响因素依次为货车、摩托车、驾驶员超车、弯道转角和弯道长度，货车导致其伤亡事故概率增加108.8%，摩托车和弯道长度分别是服从(6.941，9.9012)和(-0.004，0.0032)正态分布的异质性影响因素，导致发生伤亡事故的概率为76.11%和9.18%；③侧面碰撞严重度的显著影响因素依次为摩托车、驾驶员年龄及弯道有接入口，摩托车和接入口分别是服从(5.211，5.1112)和(-1.408，2.1462)正态分布的异质性影响因素，导致发生伤亡事故的概率为88.87%和25.47%。④与传统二项Logit模型相比，追尾碰撞、正面碰撞和侧面碰撞的随机参数二项Logit模型的拟合优度分别提高了2.85%，4.15%，6.76%，且定量捕捉了异质性影响因素，更适用于事故严重度的精细化分析。      

基于负二项分布的高速公路交通事故影响因素分析

为分析高速公路交通事故的影响因素,构建基于负二项分布的事故分析模型,探究事故数与交通特性、公路线形及路面性能间关系.鉴于传统固定参数模型难以刻画各因素对事故风险影响的异质性,引入了随机参数建模方法.结果表明:相比于固定参数负二项模型,构建的随机参数负二项模型有更好的拟合优度,且能更合理地反映各因素对事故的作用效果;将随...     

山区高速公路隧道路段与开放路段的事故影响因素分析

高速公路隧道构造特殊且通行环境复杂,因而通常事故多发。为探究高速公路隧道路段与开放路段事故影响因素和严重程度致因机理的差异,采集沪昆高速邵怀段2011—2016年期间1 537起事故为研究样本;以事故发生路段为响应变量构建逻辑回归模型,解释各种风险因素对事故发生路段倾向性的影响差异;分别针对隧道路段与开放路段建立模型研究事故伤害严重程度的影响因素。建立二元Logit回归模型分析事故的发生倾向性和2类路段的事故严重程度的影响因素;采用随机参数Logit模型以反映异质性条件对参数的影响。统计表明:与疲劳驾驶、未保持安全距离相关的事故发生在隧道路段的概率更高,其事故发生概率分别是开放路段的2.373和2.482倍;与隧道路段事故严重程度正相关的因素包括下坡（坡度2%以上）、夏季和超速行驶,其中下坡（坡度2%以上）段的严重事故发生的概率为上坡（坡度2%以上）的3.397倍,夏季的严重事故发生概率为秋季的3.951倍,超速行驶相关的严重事故发生概率为其他不当驾驶行为的4.242倍;与开放路段事故严重程度正相关的因素包括超速行驶和疲劳驾驶,其中超速行驶相关的严重事故概率是其他不当驾驶行为的2.713倍,疲劳驾驶相关的严重事故概率是其他不当驾驶行为的4.802倍。研究表明,山区高速公路隧道路段与开放路段的事故发生概率及其严重程度的影响因素存在一定的差异性,研究结论可为山区高速公路差异管理方案制定提供依据。      

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking.