道路交通安全管理的改进多级模糊综合评价

依据系统工程学原理，确定了道路交通安全管理评价指标体系，把层次分析法（AHP）与频率统计法组合起来确定评价指标权重，采用结合灰色理论的改进模糊综合评价方法对道路交通安全管理进行评价，并给出了算例。     

一种道路交通安全神经网络评价模型的构建

在综合分析国内外交通安全评价研究的基础上,针对中国道路交通安全现状,提出了基于一个城市行政分区的道路交通安全评价指标和评价模型,并针对我国城市道路交通安全的现状及存在问题,提出了采用基础数据、交通安全管理、公众安全评价3个方面共17项指标组成的面向城市道路交通安全可持续发展的评价指标集;构建了基于BP神经网络的评价模型;并结合算例详细分析了其计算方法。     

基于多层次灰色评价的空域安全宏观分析

随着空中交通流量的日益增长,保障我国空域安全越发重要,而空域安全评价方法在我国尚处于探索研究阶段。本文建立空域安全宏观评价指标体系,采用多层次灰色评价方法对空域安全系统进行分析。本文为国家空域安全评价提供基本方法和理论基础,具有较好的社会效益。     

道路交通安全设计刍议

道路交通安全设计在道路工程建设中地位很重要,它的设计原则是为了保证道路交通整体的安全。文章结合实际,分析了道路交通安全设计的重要性,结合案例阐述了道路交通安全设计的实际应用。     

一种机场安全风险灰色多层次评价方法研究

针对民用机场安全评估方法中存在的不足,综合运用灰色聚类方法和AHP法,并根据系统工程的理论,建立起由民航机场飞行保障系统、空防保障系统、机坪保障系统、运行指挥系统、安全管理系统等评价指标体系构建的民航机场安全风险多层次灰色评价模型。并结合实例进行了计算。结果表明该方法能有效地利用评价指标的信息,科学合理地综合评价民航机场的安全状况,具有更好的可操作性和实用性。     

环境噪声研究中的不确定性数学方法

本文将不确定数学中的概率模型、灰色系统理论以及模糊数学原理应用于环境噪声问题，对道路交通的计算机模拟预测、等效六级沿建筑群纵深方向一线分布的灰色系统模型预测以及道路过绿化带对环境噪声引起的烦恼度影响等三个方面的问题进行了研究，取得了有意义的结果．     

矿井通风系统安全评价研究

本文中针对模糊综合评价、层次分析、灰色理论评价、测度理论、人工神经网络理论的安全评价等等一些方法进行全方位的分析和比较，并且提出了相关的集成评价研究和集成评价的决策，为相关工作提供借鉴。     

道路交通安全管理体系概述

本文阐述道路交通安全的概念,论证并分析道路交通安全管理的系统性,并介绍了基于ISO 39001国际标准的道路交通安全管理体系的核心内涵与模型。     

农村公路交通安全设计关键技术

道路交通条件与经济发展有直接的影响,而道路交通安全设计与交通道路的安全运行至关重要,因此,致力于道路交通安全设计关键技术的研究,能够促进道路交通的安全运行、经济的健康发展。我国是农村大国,农村公路交通安全设计对于国民经济的发展有重要的影响。该文就农村公路交通安全设计相关内容进行论述,对农村公路交通安全的现状、设计原则以及其中的关键技术进行详细的论述。     

基于层次灰色理论的工业区化工生产安全评价

工业区化工安全生产的影响因素具有灰色、模糊、难以量化等特点,传统的方法难以对其合理评价。文中通过建立合适的评价指标体系,运用层次分析法和灰色系统理论结合起来的数学模型评价典型工业区,对于了解我国工业区化工生产安全状况,制定预防措施具有一定的指导意义。     

Area-wide urban traffic calming schemes: a meta-analysis of safety effects

This paper presents a meta-analysis of 33 studies that have evaluated the effects on road safety of area-wide urban traffic calming schemes. Area-wide urban traffic calming schemes are typically implemented in residential areas in towns in order to reduce the environmental and safety problems caused by road traffic. A hierarchical road system is established and through traffic is removed from residential streets by means of, for example, street closures or one-way systems. Speed reducing devices are often installed in residential streets. Main roads are improved in order to carry a larger traffic volume without additional delays or more accidents. The meta-analysis shows that area-wide urban traffic calming schemes on the average reduce the number of injury accidents by about 15%. The largest reduction in the number of accidents is found for residential streets (about 25%), a somewhat smaller reduction is found for main roads (about 10%). Similar reductions are found in the number of property damage only accidents. The results of evaluation studies are robust with respect to study design. There is no evidence of publication bias in evaluation studies. Study findings are found to have high external validity.     

Safety impact of engineering treatments on undivided rural roads

This article presents an evaluation of the safety impacts of four engineering treatments implemented in the Autonomous Community of Madrid (Spain): highway upgrading; updating and improvement of traffic signing; repainting of pavement markings and pavement resurfacings. This evaluation was carried out using the Empirical Bayes method with a comparison group. The functioning of a methodology to test the significance of the safety impact is described. The results show that highway upgrading has a positive and significant safety impact, while the updating and improvement of traffic signing, the repainting of road markings and pavement resurfacings do not exhibit a significant impact on safety.     

Road network safety evaluation using Bayesian hierarchical joint model

Safety and efficiency are commonly regarded as two significant performance indicators of transportation systems. In practice, road network planning has focused on road capacity and transport efficiency whereas the safety level of a road network has received little attention in the planning stage. This study develops a Bayesian hierarchical joint model for road network safety evaluation to help planners take traffic safety into account when planning a road network. The proposed model establishes relationships between road network risk and micro-level variables related to road entities and traffic volume, as well as socioeconomic, trip generation and network density variables at macro level which are generally used for long term transportation plans. In addition, network spatial correlation between intersections and their connected road segments is also considered in the model.A road network is elaborately selected in order to compare the proposed hierarchical joint model with a previous joint model and a negative binomial model. According to the results of the model comparison, the hierarchical joint model outperforms the joint model and negative binomial model in terms of the goodness-of-fit and predictive performance, which indicates the reasonableness of considering the hierarchical data structure in crash prediction and analysis. Moreover, both random effects at the TAZ level and the spatial correlation between intersections and their adjacent segments are found to be significant, supporting the employment of the hierarchical joint model as an alternative in road-network-level safety modeling as well.     

Assessing the validity of road safety evaluation studies by analysing causal chains

This paper discusses how the validity of road safety evaluation studies can be assessed by analysing causal chains. A causal chain denotes the path through which a road safety measure influences the number of accidents. Two cases are examined. One involves chemical de-icing of roads (salting). The intended causal chain of this measure is: spread of salt --> removal of snow and ice from the road surface --> improved friction --> shorter stopping distance --> fewer accidents. A Norwegian study that evaluated the effects of salting on accident rate provides information that describes this causal chain. This information indicates that the study overestimated the effect of salting on accident rate, and suggests that this estimate is influenced by confounding variables the study did not control for. The other case involves a traffic club for children. The intended causal chain in this study was: join the club --> improve knowledge --> improve behaviour --> reduce accident rate. In this case, results are rather messy, which suggests that the observed difference in accident rate between members and non-members of the traffic club is not primarily attributable to membership in the club. The two cases show that by analysing causal chains, one may uncover confounding factors that were not adequately controlled in a study. Lack of control for confounding factors remains the most serious threat to the validity of road safety evaluation studies.     

Microsimulation modelling of driver behaviour towards alternative warning devices at railway level crossings

Level crossings are amongst the most complex of road safety issues, due to the addition of rail infrastructure, trains and train operations. The differences in the operational characteristics of different warning devices together with varying crossing, traffic or/and train characteristics, cause different driver behaviour at crossings. This paper compares driver behaviour towards two novel warning devices (rumble strips and in-vehicle audio warning) with two conventional warning devices (flashing light and stop sign) at railway level crossings using microsimulation modelling. Two safety performance indicators directly related to collision risks, violation and time-to-collision, were adopted. Results indicated the active systems were more effective at reducing likely collisions compared to passive devices. With the combined application of driving simulation and traffic microsimulation modelling, traffic safety performance indicators for a level crossing can be estimated. From these, relative safety comparisons for the different traffic devices are derived, or even for absolute safety evaluation with proper calibration from field investigations.     

Risk factors associated with traffic violations and accident severity in China

With the recent economic boom in China, vehicle volume and the number of traffic accident fatalities have become the highest in the world. Meanwhile, traffic accidents have become the leading cause of death in China. Systematically analyzing road safety data from different perspectives and applying empirical methods/implementing proper measures to reduce the fatality rate will be an urgent and challenging task for China in the coming years. In this study, we analyze the traffic accident data for the period 2006–2010 in Guangdong Province, China. These data, extracted from the Traffic Management Sector-Specific Incident Case Data Report, are the only officially available and reliable source of traffic accident data (with a sample size >7000 per year). In particular, we focus on two outcome measures: traffic violations and accident severity. Human, vehicle, road and environmental risk factors are considered. First, the results establish the role of traffic violations as one of the major risks threatening road safety. An immediate implication is: if the traffic violation rate could be reduced or controlled successfully, then the rate of serious injuries and fatalities would be reduced accordingly. Second, specific risk factors associated with traffic violations and accident severity are determined. Accordingly, to reduce traffic accident incidence and fatality rates, measures such as traffic regulations and legislation—targeting different vehicle types/driver groups with respect to the various human, vehicle and environment risk factors—are needed. Such measures could include road safety programs for targeted driver groups, focused enforcement of traffic regulations and road/transport facility improvements. Data analysis results arising from this study will shed lights on the development of similar (adjusted) measures to reduce traffic violations and/or accident fatalities and injuries, and to promote road safety in other regions.     

A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level

This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure.     

Power computations in time series analyses for traffic safety interventions

The evaluation of traffic safety interventions or other policies that can affect road safety often requires the collection of administrative time series data, such as monthly motor vehicle collision data that may be difficult and/or expensive to collect. Furthermore, since policy decisions may be based on the results found from the intervention analysis of the policy, it is important to ensure that the statistical tests have enough power, that is, that we have collected enough time series data both before and after the intervention so that a meaningful change in the series will likely be detected. In this short paper, we present a simple methodology for doing this. It is expected that the methodology presented will be useful for sample size determination in a wide variety of traffic safety intervention analysis applications. Our method is illustrated with a proposed traffic safety study that was funded by NIH.