高速公路交通事故成因分析和预防对策

随着改革开放的深入,我国高速公路通车里程在迅速增长,高速公路正在向网络化、智能化、城市化的趋势发展.虽然高速公路的事故发生率较一般公路低,但是高速公路事故损失大,殃及车辆多,死亡率高,所以重视高速公路交通安全,分析、研究高速公路交通事故的成因,能有效预防和减少高速公路交通事故的发生.     

广西高速公路速度限制研究

文章分析了广西高速公路速度限制存在的主要问题,提出了改进高速公路速度限制的相关措施,对提高广西高速公路管理水平和高速公路通过率、保证行车安全及减少公路交通事故具有现实的意义。     

雾天高速公路交通事故成因分析及管理措施

文章分析了雾天高速公路交通事故的成因与特征,提出了相应的交通管制方式和处置措施,为雾天高速公路的交通管理工作提供思路。     

基于灰色理论的事故黑点改造效果分析

文章分析了汉宜高速公路黑点路段事故原因,提出黑点路段的具体整治措施,并结合汉宜高速公路改造后的事故特征,运用灰色预测模型,预测交通事故进一步的变化态势,结果表明:交通事故逐年减少,改造效果明显.     

广西高速公路木冲隧道交通安全性分析

长大隧道是高速公路的控制路段,其运营安全对高速公路畅通和安全有重要影响。文章通过研究木冲隧道交通事故分布特征,分析了长大隧道发生交通事故的原因,提出了改善木冲隧道交通安全状况的工程措施和管理建议,其对提高木冲隧道运营安全性具有指导意义。     

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。     

盆周山区高速公路交通运行特点分析

本文界定了盆周山区高速公路的范围,并介绍了其发展历程.通过对典型盆周山区高速公路的实地调研、驾驶人问卷调查和运行数据收集,用数理统计方法分析了盆周山区高速公路的交通流、驾驶人、交通事故、运营管理等交通运行特点,并提出了有针对性的运营管理建议.     

发达的德国高速公路

通过介绍德国高速公路的通信信息系统、交通事故处理机制以及公路景观和服务区的建设,阐明德国发达的高速公路路网系统,为中国高速公路的发展提出新思路。     

浙江省“十四五”期间高速公路建设思路和重点研究

高速公路是经济社会发展的战略性、公益性、先导性基础设施，是一个国家现代化和交通运输现代化的重要标志。本文总结浙江省“十三五”期间高速公路在路网规模、运营效益、投资带动等方面的发展成效，梳理现状存在的问题，分析面临的形势和发展需求，明确浙江省“十四五”期间高速公路建设发展思路、目标和重点。测算了未来五年用地和资金需求并提出切实可行的保障措施。     

基于手机与流量数据的浙江省高速公路拥堵预测研究

本文以浙江省高速公路网为对象,基于手机上高速公路车速数据和收费站出入口流量数据开展浙江省高速公路拥堵预测研究。对收费站流量数据和手机车速数据质量进行分析,在数据质量可靠基础上,构建基于手机与流量数据的浙江省高速公路拥堵预测模型,并通过历史数据算例验证了该模型的适用性。研究成果可为有效解决浙江省高速公路拥堵问题提供有益参考。     

The relationship among highway geometrics,traffic-related elements and motor-vehicle accident frequencies

This research provides a demonstration of a statistical model of accident frequency that can eventually be used as part of a proactive program to allocate safety-related highway improvement funds. Negative binomial regressions of annual accident frequency on sections of principal arterials in Washington State were estimated using data from two years (1992 and 1993). In all, 31306 observations were used in model estimation (annual accident frequencies on specific sections of highway). Our estimation results isolated the effects of various highway geometric and traffic characteristics on annual accident frequency. Subsequent elasticity computations identified the relative importance of the variables included in our specifications. The findings show that the negative binomial regression used in this paper is a powerful predictive tool and one that should be increasingly applied in future accident frequency studies.     

小金口枢纽互通立交拥堵改造分析

文章以广惠高速公路小金口枢纽互通立交工程为依托,在预测交通量的基础上从拥堵匝道的通行能力、服务水平和交通事故等方面分析匝道拥堵的原因,并提出基于光流率和边缘率的主线改善措施和匝道改善方案,为小金口枢纽互通立交及其他立交的拥堵改造提供参考。     

道路交通事故分析及预防对策

道路交通事故的预防是一个系统工程,通过对全国和新疆道路交通安全现状与道路交通事故原因的对比分析,分别从影响道路交通安全的人、车、路及环境四个方面提出了降低和预防道路交通事故的发生,确保行车安全的相应措施和建议。     

一级公路交叉口设计中的交通安全问题及对策

一级干线公路由于开口较多,不可避免地存在较多的交通安全问题.文章以湖北荆监一级公路为例,针对交叉口设计中的交通安全典型问题,提出了改善方法及相关对策.     

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。     

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.     

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.     

公路隧道通风网络理论的研究及应用

文章结合湖南雪峰山隧道工程,讨论了通风网络理论及其在公路隧道中的应用,通过推导交通通风力的计算公式,介绍了通风网络中风机、自然风等各压力源的处理办法。     

高速公路连续长下坡路段安全评价与整治措施研究

《高速公路连续长下坡路段安全评价与整治措施研究》项目针对山区公路连续长下坡路段事故率高、伤亡率大、交通安全设施设置针对性不强等问题,通过大量事故调研及资料调查,采用汽车行驶动力学、仿真计算等技术手段对长下坡路段的安全水平评价方法等内容进行系统研究。文章着重介绍了该项目的研究现状、研究内容、技术路线、关键技术、研究成果及创新点。     

Vehicular Ad-Hoc Networks sampling protocols for traffic monitoring and incident detection in Intelligent Transportation Systems

Vehicular Ad-Hoc Networks (VANETs) are an emerging technology soon to be brought to everyday life. Many Intelligent Transport Systems (ITS) services that are nowadays performed with expensive infrastructure, like reliable traffic monitoring and car accident detection, can be enhanced and even entirely provided through this technology. In this paper, we propose and assess how to use VANETs for collecting vehicular traffic measurements. We provide two VANET sampling protocols, named SAME and TOME, and we design and implement an application for one of them, to perform real time incident detection. The proposed framework is validated through simulations of both vehicular micro-mobility and communications on the 68 km highway that surrounds Rome, Italy. Vehicular traffic is generated based on a large real GPS traces set measured on the same highway, involving about ten thousand vehicles over many days. We show that the sampling monitoring protocol, SAME, collects data in few seconds with relative errors less than 10%, whereas the exhaustive protocol TOME allows almost fully accurate estimates within few tens of seconds. We also investigate the effect of a limited deployment of the VANET technology on board of vehicles. Both traffic monitoring and incident detection are shown to still be feasible with just 50% of equipped vehicles.