重特大道路交通事故的成因模型及应用分析

道路交通事故已成为威胁社会发展的公害,特别是特大交通事故影响最为恶劣,因此有必要研究具有重大社会影响的特大道路交通事故的成因。本文提出了一个新的道路交通事故成因模型,分析了一起典型的特大道路交通事故案例,指出了该特大道路交通事故成因特征,并结合道路交通事故成因模型对该事故案例进行成因分析,最后提出关于特大道路交通事故预防措施的建议。     

灰色预测模型在城市道路交通事故中的应用

详细介绍了灰色模型的原理和特点,根据交通事故的发生特点,探讨了灰色模型在道路交通事故预测中的具体应用,并利用此模型对青岛市某地区的交通事故进行预测,建立了灰色预测模型,根据实际事故数据与预测值进行了比较,灰色预测模型的精度比较好.     

基于支持向量机的交叉口交通事故分析方法

文章基于交叉口交通事故数据,建立交叉口交通事故严重程度的支持向量机(SVM)分类模型,并结合缺一法对影响交叉口交通事故严重程度的特征变量进行分析,得到每个特征变量的权重值,研究各特征变量对交叉口交通事故严重程度的影响,提出相应的防控策略。     

高原地区公路运行速度模型研究

文章基于对青藏公路车辆运行速度、车辆组成的调查,采用层次分析法对不同车辆组成、不同路段速度进行分析,建立了不同车辆组成下的运行速度模型,并结合交通事故数据,提出了确保交通安全的常年冻土区公路运行速度值。该运行速度模型的应用研究,为减少道路交通事故提供了一种新思路。     

论BIM技术在市政道路设计中的应用

基于BIM技术的三维可视化技术,在现阶段的建筑行业之中得到广泛应用.市政道路设计中应用BIM技术,就可以实现道路的建模处理,从而获取对应的三维模型,并且通过计算机来实现道路模拟,构建出具体的、正式的三维动画模型.因此,在市政道路设计中,BIM技术的应用具有重要的现实意义,值得相关人士探讨.     

基于VISSIM的高速公路隧道交通事故影响仿真分析

为了研究高速公路长大隧道内交通事故影响程度,文章以江西省第一长隧道九岭山隧道为研究对象,通过VISSIM交通仿真软件建立交通仿真模型,在设置不同等级的交通流量以及大货车交通比例的条件下,仿真获得高速公路长大隧道交通事故发生后车辆排队长度以及延误时间,用以分析高速公路隧道内交通事故对隧道通行能力的影响,以更大程度上实现高速公路隧道安全行车。     

交通事故与经济增长的变动关系

本文以人均GDP为解释变量,用固定效应模型分析了中国和28个OECD国家的面板数据,验证了交通事故与经济增长之间存在的倒U型关系曲线。在此基础上,将中国交通事故的发展特性与国际规律进行比较,发现我国已开始实现倒U形反转,交通安全水平正在逐步改善。     

考虑危险驾驶群体的交通事故预测模型

传统的交通事故预测是基于直接暴露量,如车辆行驶里程(VMT)等,很少有研究致力于评估不同驾驶群体对事故频率的影响,而现有的研究表明,大量的交通事故与驾驶群体相关。基于此,首先采用相对危险暴露量技术探索所有驾驶人群中的危险驾驶群体,通过相关事故参与率的计算结果可知,青年(15~29岁)和老年(大于或等于70岁)驾驶员更容易发生事故,为危险驾驶群体。然后以VMT和危险驾驶群体作为解释变量,建立了用于交通事故预测的负二项模型。最后通过计算Akaike信息准则(AIC)指标,得到了最优预测模型。结果表明,将危险驾驶群体引入交通事故预测模型可以使预测效果更好(尤其是以VMT和青年驾驶员为变量),有助于提高交通事故预测的准确性。     

机载LIDAR技术在现有高速公路路线指标评价中的应用

文章针对因施工误差、运营中的不均匀沉降而导致的现有高速公路部分路段路线不合规范要求的情况,提出了利用机载LIDAR技术,在不干扰交通的情况下获取现有高速公路的三维数据,并通过平纵拟合设计实现对现有高速公路的路线指标评价,以发现高速公路运营过程中的安全隐患,预防交通事故的发生。     

突发事件下公路网供需水平分析

本文从自然灾害、交通事故对公路交通的通行能力的影响来分析,深入了解突发事件对交通通行能力的影响程度。     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.     

Accident cost saving and highway attributes

Two semi-logarithmic regression models are developed to estimate accident rates and accident costs, respectively, for rural non-interstate highways in the state of Iowa. Data on 21,224 accidents occurring between 1989 and 1991 on 17,767 road segments are used in the analysis. Seven road attributes of these road segments are included as predictor variables. Applying the resulting regression models to a rather typical highway upgrade situation, the present value of the accident cost saving is computed. The sensitivity of the estimated cost saving to values for fatal, personal injury, and property damage only accidents is tested.Because factors other than road characteristics greatly influence accident costs, the models developed in this research explain a limited amount of the variance in these costs among road segments. Results of the analysis indicate that the most important attribute associated with accident costs is average daily traffic per lane, followed by conditions requiring passing restrictions and the sharpness of curves. Varying the values for the three categories of accidents shows that results are far more sensitive to the value of personal injuries than fatalities. The feasibility of using predictive models of accident costs in benefit-cost analyses of highway investments is demonstrated.     

道路交通事故发生机理与实例分析

交通事故发生机理是认识道路交通事故发生过程、交通事故预防和改善交通安全的基础。文章以道路交通系统为研究对象,分析道路交通事故的形成过程,将交通事故发生机理分为驾驶行为差错类事故发生机理、外部因素突变类事故发生机理、综合性事故发生机理三类,并在此基础上绘制了道路交通事故发生机理图,同时结合国道109线兰州八盘村路段进行了实例分析。     

A traffic accident morphology diagnostic model based on a rough set decision tree

ABSTRACT

To build a traffic safety feature model and to quantify accident influences caused by some traffic violation behaviors of drivers, an accident diagnostic decision-making model is established. For the purpose of diagnosing accident morphologies, rough set theory is applied and the influence of traffic factors of different accident morphologies is quantified through calculating the degree of attribute importance, selecting core traffic factors and adopting a C4.5 decision tree algorithm. In the paper, road traffic accident data from 2008 to 2013 in Anhui Province are used. Typical rules are selected, targeted strategy proposals are put forward, and then, a scientific and reasonable diagnostic basis is provided for the diagnosis of traffic safety risks and the prediction of potential traffic accidents.     

浙江省高速公路交通事故特点及防治措施研究

随着经济的快速发展,高速公路通车里程得到快速增长,道路客、货运量及周转量不断高速发展,但高速公路的交通安全问题已经成为一个重要的研究课题。本文首先分析了浙江省高速公路交通事故的特点,然后运用轨迹交叉论进行交通事故的原因分析,识别浙江省高速公路危险源,最后有针对性地提出了浙江省高速公路交通事故防治措施。     

基于WOS数据库的文献计量分析的城市交通事故态势研究

为从宏观上了解交通事故的研究态势,利用文献计量法对WOS数据库收录的474篇文献进行数据可视化分析。研究发现,发文量历经了零阶段、稳定阶段和上升阶段;中国研究机构数量和发文量都位于世界第一;研究领域形成了由122位作者组成的核心作者群体;研究方向经历了以交通参与者、道路交通事故、交通事故安全为研究目的的变化;关键词分析得出该领域未来的研究热点将集中在交通事故安全、交通事故严重程度及交通事故影响三方面。     

Updating predictive accident models of modern rural single carriageway A-roads

Abstract

Reliable predictive accident models (PAMs) are essential to design and maintain safe road networks, and yet the models most commonly used in the UK were derived using data collected 20 to 30 years ago. Given that the national personal injury accident total fell by some 30% in the last 25 years, while road traffic increased by over 60%, significant errors in scheme appraisal and evaluation based on the models currently in use seem inevitable. In this paper, the temporal transferability of PAMs for modern rural single carriageway A-roads is investigated, and their predictive performance is evaluated against a recent data set. Despite the age of these models, the PAMs for predicting the total accidents provide a remarkably good fit to recent data and these are more accurate than models where accidents are disaggregated by type. The performance of the models can be improved by calibrating them against recent data.     

Feedback control strategy of a new car-following model based on reducing traffic accident rates

ABSTRACT

To reduce the traffic accident death rate effectively and alleviate the traffic congestion phenomenon, this study proposes a new type of car-following model under the influence of drivers’ time-varying delay response time. Based on Lyapunov function theory, this paper reduces the traffic accident rate problem to the stability issues of the new model. By constructing suitable Lyapunov functions and using the linear matrix inequality method, the stability problem of the new car-following model is studied. The model, under the action of the controller, can effectively restrain traffic congestion. Using the traffic accident rate model proposed by Solomon, compared with the car-following model without the controller, the model under the controller shows a stronger convergence. This also means that the traffic congestion phenomenon has been effectively suppressed while greatly reducing the mortality rate of traffic accidents.