Risk factors associated with truck-involved fatal crash severity: Analyzing their impact for different groups of truck drivers

Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, ‘‘middle-aged and elderly drivers with low risk of driving violations and high historical crash records,” ‘‘drivers with high risk of driving violations and high historical crash records,” and ‘‘middle-aged drivers with no driving violations and conviction records.” Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned.     

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.     

大型公园道路网络关键节点脆弱性评价

为更加科学有效地辨识景区道路网络中的客流关键节点,以节点脆弱性为度量指标,提出1种基于FIM模型的关键节点脆弱性评价方法.以某大型公园为例,首先通过ArcGIS软件将该公园的道路网络信息转换成可编译的文本信息,经过Java枚举可行路径,然后利用嵌入FIM算法的Lingo进行优化,得出网络节点的重要度.最后综合节点容量、...     

基于多智能体建模方法的人群应急疏散模型构建研究——以腾龙芳烃（漳州）有限公司“4·6”爆炸着火重大事故疏散为例*

为从更微观角度分析人群疏散过程中疏散行为及路网设计对疏散效果的影响,基于腾龙芳烃（漳州）有限公司“4·6”爆炸着火重大事故,构建多智能体人群应急疏散模型,模拟人群中个体群组、惯性、就近、从众、信息传播的行为决策及相互交互影响。结果表明:群组行为会严重影响应急疏散效果,在疏散路网两端避难所附近会出现明显拥堵现象,在疏散路网设计和避难所选择时,应尽可能避免出现极端汇流路段或节点;在应急培训中,应告知群众减少群组行为。研究结果可为人群应急疏散提供借鉴。     

基于农户行为的土地利用人工社会模型的构造与应用

为探讨人工社会在土地利用变化研究领域中的应用,研究创建了一个模拟土地利用变化过程的人工社会模型(LUC-ASM)。该模型以农户和农民为两类主体(agents),模拟分析不同社会经济条件和政策影响下,两类主体对自己承包土地利用方式的微观决策所可能产生的宏观效应及其时空变化特征。以鄱阳湖区一个现实村落为主要研究对象,以农户从事各类生产活动的行为特征为准则,利用LUC-ASM模型模拟未来30年内该村落土地利用变化过程。结果表明,未来这一地区土地利用的变化与人口增长结构调整方式、国家土地利用与环保政策、社会经济发展态势等因素密切相关;在城市化的影响下,该地区的农业劳动力将会出现短缺,农村土地有可能被撂荒,需要及早采用政策措施予以避免。总体而言,LUC-ASM模型能够形象地反映土地利用变化过程的微观驱动机理及各种社会经济与环境因素影响的宏观效应,有助于深化我们对人与自然环境相互作用的认识。     

厌氧反应器系统动力学模型构建方法学研究

以系统动力学的基本概念、方法为基础提出了存在于变量之间的3种基本关系模式,并在此基础上提出针对复杂系统的分块建模理论.将以葡萄糖为进水基质的厌氧反应器划分为3个子系统.确定了该系统的系统结构图,在此基础上构建厌氧消化系统的SD模型.根据分块建模的优势及特点,对模型中的3个子系统采用不同的计算方式,通过对比模拟结果表明,SD模型静态模拟结果与实验数据、传统结构模型模拟结果之间的误差<10%,动态模拟结果与实际趋势一致,说明SD模型的模拟结果是可信的.根据其特性,对取消物化子系统的简化模犁的模拟结果进行分析,表明根据不同的研究目的,选择合适的模型结构是必要的,采用系统动力学的分块建模方法有利于模型的简化与升级、减少计算量、提高模型的计算速度.