An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

Introduction: Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes. Method: This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area. Results: The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.     

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.     

Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

IntroductionRoadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method: In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results: The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions: Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications: Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity.     

The acceptance of personal protective equipment among Hong Kong construction workers: An integration of technology acceptance model and theory of planned behavior with risk perception and safety climate

Introduction: The phenomenon that construction workers do not use personal protective equipment (PPE) is a major reason for the high occurrence frequency of accidents in the construction industry. However, little efforts have been made to quantitatively examine the factors influencing construction workers’ acceptance of PPE. Method: In the current study, a PPE acceptance model for construction workers (PAMCW) was proposed to address the noted need. The PAMCW incorporates the technology acceptance model, theory of planned behavior, risk perception, and safety climate for explaining construction worker acceptance of PPE. 413 construction workers participated in this study to fill out a structured questionnaire. The PAMCW was analyzed using structural equation modeling. Results: Results provide evidence of the applicability of the technology acceptance model and theory of planned behavior to the PPE acceptance among construction workers. The positive influence of safety climate and risk perception-severity on attitude toward using PPE was significant. Safety climate positively influences perceived usefulness. Risk perception-worry and unsafe was found to positively affect intention to use PPE. Practical Applications: Practical suggestions for increasing construction workers’ use of PPE are also discussed.     

湘江干流水环境健康风险评价

为研究湘江水环境污染对人体健康产生的危害风险,在简要介绍健康风险评价方法及国内外研究、应用进展的基础上,建立了水环境健康风险评价模式,应用2000年的水质监测资料,对湘江干流主要断面进行了水环境健康风险评价.结果表明,在所评价断面中,非致癌物质由饮水途径所致健康危害的个人年风险按大小排列为Pb＞Hg＞NH3＞酚;化学致癌物质由饮水途径所致健康危害的个人年风险按大小排列为As＞Cd;化学致癌物质对人体健康危害的个人年风险远超过非致癌物质;污染最为严重的为松柏断面,其次是五星断面和三叉矶断面.湘江干流衡阳以上江段水质良好,衡阳以下江段水质较差.研究对湘江干流水污染防治方略的制定具有一定价值.     

面向突发事件智能建筑管理系统的集成结构

为了实现面向突发事件的智能建筑管理系统(IBMS),提出了智能建筑突发事件的抽象模型、基本运算操作及优先权分配机制,并根据突发事件监控和管理的目标,提出了IBMS的"分布式虚拟应急中心"集成结构.     

示踪技术用于气体泄漏扩散模拟的多元线性回归分析

采用江苏工业学院油气储运安全综合实验模型平台,利用示踪技术(以CO2为示踪剂),模拟了有害气体瞬时泄漏扩散的整个过程.并结合多元线性回归分析的基本方法,定量分析了气体泄漏扩散过程,建立了多元线性回归模型,初步确定了泄漏气体浓度与相关物理量的变化关系,在此基础上得到了2个定量评价指标:确定性系数R=0.991,相关系数r=0.995.研究表明,在实验室建立的试验模型能较好地反映现实情况,模型仿真度较高,数据拟合效果较好.研究可为评价模型的相似性提供理论参考,对有害气体泄漏扩散事故应急救援、实施有效的现场控制,具有一定的科学参考价值.     

生态缺水条件下流域生态经济系统安全性分析

生态用水和社会经济用水的竞争状况使得研究水资源短缺地区的流域生态经济系统安全状况、有效进行生态缺水条件下流域生态经济系统的调控十分必要.从宏观角度出发,首先分析了流域生态经济系统演化的机制和影响因素,在此基础上,通过建立指标体系,提出了构建流域主要指标为状态变量的生态经济系统演化模型,并利用模型进行系统安全性分析.以潮白河流域为例,构建了人口数量、人均GDP、生态系统指数以及生态用水比例为状态变量的生态经济系统演化模型.模型模拟结果表明,如果不加以调控,该流域将无法提供生态经济系统的安全保障.提出了提高生态用水比例、控制经济增长速度和积极进行生态建设、控制水土流失的对策,为实现潮白河流域的生态经济系统安全保障服务.     

基于单体对象的城市区域火灾风险评价方法研究

从分析城市区域内单体对象自身的致灾因素及其制约灭火抢险救援力量发挥的环节入手,基于指数法和层次分析法建立了单体对象火灾风险评价指标体系.以单体对象火灾风险向量为基础,基于线性加权模型构建了一种新的区域火灾风险评价计算模型,并以树形结构表征了区域火灾风险评价结构.试点地区实例表明,该方法能够有效地评价城市区域火灾风险.