Investigating the effects of monthly weather variations on Connecticut freeway crashes from 2011 to 2015

IntroductionThe objective of this research is to investigate the effects of monthly weather conditions on traffic crash experience on freeways, considering the interactions between weather, traffic volumes, and roadway conditions. Methods: Data from the state of Connecticut from 2011to 2015 were used. Random parameters negative binomial models with first-order, autoregressive covariance were estimated for representative types of freeway crashes (front-to-rear, sideswipe-same-direction, and fixed-object), most severe crashes (i.e., fatal and injury crashes), and non-injury crashes (i.e., property-damage-only crashes). Results: Major findings are that variations in monthly traffic volumes, roadway geometry, and weather conditions explain much of the variations in monthly traffic crashes. Time effects exist in the panel monthly data for all types of crashes. Taking into account this effect improves model prediction results. When the raw weather measures are highly correlated, using dimension reduction techniques helps to extract more interpretable weather factors. By considering the interaction effects between roadway condition variables, additional findings were found. In general, lower temperature, more heavy fog days, decreased precipitation, lower wind speed, higher monthly traffic volumes, and narrower inside shoulder were found to be associated with higher monthly crashes. The effects of area type and outside shoulder width change dramatically as the number of through lanes changes. Practical applications: The findings of this research could help researchers and general readers gain a better understanding of the effects of monthly weather conditions and other roadway factors on freeway crashes and give engineers practical guidelines on improving freeway safety.     

AN EARLY WARNING SYSTEM FOR DROUGHT MANAGEMENT USING THE PALMER DROUGHT INDEX1

ABSTRACT: The Palmer Drought Severity Index (PDSI) is used in a non-homogeneous Markov chain model to characterize the stochastic behavior of drought. Based on this characterization an early warning system in the form of a decision tree enumerating all possible sequences of drought progression is proposed for drought management. Besides yielding probabilities of occurrence of different drought severity classes, the method associates a secondary measure in terms of likely cumulative precipitation deficit to provide timely guidance in deciding drought mitigation actions. The proposed method is particularly useful for water availability task forces in various states for issuing drought warnings in advance. The applicability of the technique is illustrated for the Tidewater climatic division of Virginia.     

A multinomial logit model of motorcycle crash severity at Australian intersections

Introduction: Motorcyclists are exposed to more fatalities and severe injuries per mile of travel as compared to other vehicle drivers. Moreover, crashes that take place at intersections are more likely to result in serious or fatal injuries as compared to those that occur at non-intersections. Therefore, the purpose of this study is to evaluate the contributing factors to motorcycle crash severity at intersections. Method: A data set of 7,714 motorcycle crashes at intersections in the State of Victoria, Australia was analyzed over the period of 2006–2018. The multinomial logit model was used for evaluating the motorcycle crashes. The severity of motorcycle crashes was divided into three categories: minor injury, serious injury and fatal injury. The risk factors consisted of four major categories: motorcyclist characteristics, environmental characteristics, intersection characteristics and crash characteristics. Results: The results of the model demonstrated that certain factors increased the probability of fatal injuries. These factors were: motorcyclists aged over 59 years, weekend crashes, midnight/early morning crashes, morning rush hours crashes, multiple vehicles involved in the crash, t-intersections, crashes in towns, crashes in rural areas, stop or give-way intersections, roundabouts, and uncontrolled intersections. By contrast, factors such as female motorcyclists, snowy or stormy or foggy weather, rainy weather, evening rush hours crashes, and unpaved roads reduced the probability of fatal injuries. Practical Applications: The results from our study demonstrated that certain treatment measures for t-intersections may reduce the probability of fatal injuries. An effective way for improving the safety of stop or give-way intersections and uncontrolled intersections could be to convert them to all-way stop controls. Further, it is recommended to educate the older riders that with ageing, there are physiological changes that occur within the body which can increase both crash likelihood and injury severity.     

超大城市昼夜交通事故严重程度致因对比分析——以深圳市为例

为研究夜间交通事故严重程度致因，基于深圳市3年3 244起交通事故数据，获取昼夜交通事故分布的时空特征；进一步选取交通事故集聚的南山区、福田区、罗湖区的1 798起交通事故，以交通事故严重程度为因变量，以事故原因、日期、事故形态等10个因素为候选自变量，构建广义有序Logit回归模型，对比分析昼夜不同严重程度交通事故的影响因素。结果表明:路口路段类型、疲劳驾驶、事故日期在夜间模型参数估计值分别为0.493，-0.363，-0.309，而在日间模型表现为不显著，道路路面材料在日间模型参数估计值为-0.232，而在夜间表现为不显著；事故原因、道路横断面渠化方式等因素在日间和夜间所引起交通事故的严重等级均存在较大差异。     

Considering built environment and spatial correlation in modeling pedestrian injury severity

Objective: This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

Method: Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

Results: This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

Conclusions: This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.     

The safety implications of vehicle seat adjustments

INTRODUCTION: The goal of this study was to gather information on the preferred front seat position of vehicle occupants and to determine the impact of variation in seat position on safety during crashes. METHOD: The study evaluated the relationship between seat position and occupant size using the chi-square test and compared the risk of severe injury for small females and large males with regard to forward and rearward seat position using logistic regression. RESULTS: While smaller drivers sat closer to the steering wheel than larger drivers, front passengers of all sizes used similar seat positions. Additionally, the risk of injury was higher for small, unbelted females in rearward seat positions and large males (belted and unbelted) in forward seat positions. CONCLUSIONS: Occupants who adjust their seats to positions that are not consistent with required federal tests are at a greater risk for severe injury in a crash.     

Evaluation of Vehicle-Based Crash Severity Metrics

Objective: Vehicle change in velocity (delta-v) is a widely used crash severity metric used to estimate occupant injury risk. Despite its widespread use, delta-v has several limitations. Of most concern, delta-v is a vehicle-based metric which does not consider the crash pulse or the performance of occupant restraints, e.g. seatbelts and airbags. Such criticisms have prompted the search for alternative impact severity metrics based upon vehicle kinematics. The purpose of this study was to assess the ability of the occupant impact velocity (OIV), acceleration severity index (ASI), vehicle pulse index (VPI), and maximum delta-v (delta-v) to predict serious injury in real world crashes.

Methods: The study was based on the analysis of event data recorders (EDRs) downloaded from the National Automotive Sampling System / Crashworthiness Data System (NASS-CDS) 2000–2013 cases. All vehicles in the sample were GM passenger cars and light trucks involved in a frontal collision. Rollover crashes were excluded. Vehicles were restricted to single-event crashes that caused an airbag deployment. All EDR data were checked for a successful, completed recording of the event and that the crash pulse was complete. The maximum abbreviated injury scale (MAIS) was used to describe occupant injury outcome. Drivers were categorized into either non-seriously injured group (MAIS2?) or seriously injured group (MAIS3+), based on the severity of any injuries to the thorax, abdomen, and spine. ASI and OIV were calculated according to the Manual for Assessing Safety Hardware. VPI was calculated according to ISO/TR 12353-3, with vehicle-specific parameters determined from U.S. New Car Assessment Program crash tests. Using binary logistic regression, the cumulative probability of injury risk was determined for each metric and assessed for statistical significance, goodness-of-fit, and prediction accuracy.

Results: The dataset included 102,744 vehicles. A Wald chi-square test showed each vehicle-based crash severity metric estimate to be a significant predictor in the model (p < 0.05). For the belted drivers, both OIV and VPI were significantly better predictors of serious injury than delta-v (p < 0.05). For the unbelted drivers, there was no statistically significant difference between delta-v, OIV, VPI, and ASI.

Conclusions: The broad findings of this study suggest it is feasible to improve injury prediction if we consider adding restraint performance to classic measures, e.g. delta-v. Applications, such as advanced automatic crash notification, should consider the use of different metrics for belted versus unbelted occupants.     

基于车辆事故等级分类的紧急呼救系统研究

基于交通事故紧急救援的现状,阐述建立车辆事故紧急呼救系统的新需求,提出了开发基于车辆事故严重等级分类的紧急呼救系统需要实现的功能;应用车辆碰撞安全性理论和研究成果、导航定位技术及通信技术,设计了基于事故严重等级分类的车辆事故紧急呼救决策流程;探讨车辆事故自动检测和等级分类及紧急救援的实施方案,以实现车辆事故紧急自动呼救,使车辆和伤员得到及时高效的紧急救助。     

熔融硝盐高温分解爆炸事故后果严重度评价

为评价热处理用硝盐槽熔融硝盐高温分解爆炸事故机理和后果严重度，结合某企业铝合金件硝盐固溶热处理工艺进行研究。 利用压缩气体容器爆炸能量计算模型、超压准则和TNT当量法估算热处理用硝盐槽内熔融硝盐高温分解爆炸事故后果，得出35 000 kg熔融硝盐在高温分解转化率为50%时爆炸的TNT当量为1 257.6 kg，爆炸会造成半径53.97 m范围内的人员轻伤，该结果和利用政府推荐的危险指数法得出的外部防护距离为50 m较为接近。 以上分析计算表明:超温爆炸过程中，熔融硝盐在硝盐槽中超温分解快速产生大量气体，在硝盐槽上盖及上部硝盐的阻挡下不能及时排出，致使硝盐槽内气体压力瞬间升高，形成类似于压力容器的空间，发生物理爆炸并引发高温硝盐喷溅。     

基于聚苯乙烯老化的大气环境严酷度分类研究

目的研究面向典型高分子材料应用的大气环境严酷度分类方法。方法开展聚苯乙烯在我国10个典型大气环境地区的自然暴露试验,对比分析聚苯乙烯表面及力学性能的变化规律及差异,确定大气环境严酷度评估参数,依据评估参数量值对大气环境严酷度分类分级。结果聚苯乙烯表面的黄色指数随时间呈线性增大,且各环境地区的规律一致,其老化模型中的常数可作为大气环境严酷度评估的性能参数。太阳辐射和高温高湿是影响聚苯乙烯劣化的主要环境因素。结论以聚苯乙烯性能为大气环境严酷度评估参数,可将我国大气环境严酷度划分为6个等级。