复合绝缘子用高温硫化硅橡胶老化图谱绘制方法

目的 研究高温硫化硅橡胶材料在户外不同环境因素作用下的老化规律与相应的老化图谱绘制方法。方法 通过静态接触角、邵氏硬度法测量硅橡胶老化前后的宏观特性,利用扫描电子显微镜(SEM)观察其表面微观形貌变化,通过倒易法则、阿伦尼乌斯公式以及叠加法则构建紫外光和温度双因素老化模型,并结合人工与自然老化试验硬度测试结果,对比不同地点的加速因子,初步建立老化速率图谱绘制方法。结果 随着紫外老化以及热老化时间的增加,硅橡胶的憎水性先增强、后逐渐变差,硅橡胶硬度以幂函数形式增加。SEM结果表明,硅橡胶表面发生降解。结合表面硬度特性可以较好地表征材料的老化状态,并用于老化图谱绘制,老化模型结果与自然试验结果一致。结论 硅橡胶老化图谱可以较好地预测各地不同环境下硅橡胶的老化状态,帮助电网制定运维策略,降低成本。     

The combined benefits of motorcycle antilock braking systems (ABS) in preventing crashes and reducing crash severity

Objective: Several studies have reported the benefits of motorcycle antilock braking systems (ABS) in reducing injury crashes, due to improved stability and braking performance. Both aspects may prevent crashes but may also reduce the crash severity when a collision occurs. However, it is still unknown to what extent the reductions in injury crashes with ABS may be due to a combination of these mechanisms.

Methods: Swedish hospital and police reports (2003–2012) were used. The risk for permanent medical impairment (RPMI) was calculated, showing the risk of at least 1 or 10% permanent medical impairment. In total, 165 crashes involving ABS-equipped motorcycles were compared with 500 crashes with similar motorcycles without ABS.

The analysis was performed in 3 steps. First, the reduction in emergency care visits with ABS was calculated using an induced exposure approach. Secondly, the injury mitigating effects of ABS were investigated. The mean RPMI 1+ and RPMI 10+ were analyzed for different crash types. The distributions of impairing injuries (PMI 1+) and severely impairing injuries (PMI 10+) were also analyzed. In the third step, the total reduction of PMI 1+ and PMI 10+ injured motorcyclists was calculated by combining the reductions found in the previous steps. An additional analysis of combined braking systems (CBS) together with ABS was also performed.

Results: The results showed that emergency care visits were reduced by 47% with ABS. In the second step, it was found that the mean RPMI 1+ and RPMI 10+ with ABS were 15 and 37% lower, respectively. Finally, the third step showed that the total reductions in terms of crash avoidance and mitigation of PMI 1+ and PMI 10+ injured motorcyclists with ABS were 67 and 55%, respectively. However, PMI 1+ and PMI 10+ leg injuries were not reduced by ABS to the same extent. Indications were found suggesting that the benefits of ABS together with CBS may be greater than ABS alone.

Conclusions: This article indicated that motorcycle ABS reduced impairing injuries, mostly due to fewer emergency care visits but also due to a reduction in crash severity. This may seem reasonable as the improved stability and braking performance provided by ABS could prevent some crashes but would also decrease crash severity if a collision still occurs. As suggested by previous studies, however, the lower extremities would be more exposed in a crash with ABS. It is recommended that future research should follow up these results with additional data.     

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model

Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.     

Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach

Introduction: Traffic crashes could result in severe outcomes such as injuries and deaths. Thus, understanding factors associated with crash severity is of practical importance. Few studies have deeply examined how prior violation and crash experience of drivers and roadways are associated with crash severity. Method: In this study, a set of risk indicators of road users and roadways were developed based on their prior violation and crash records (e.g., cumulative crash frequency of a roadway), in order to reflect certain aspect or degree of their driving risk. To explore the impacts of those indicators on crash severity and complex interactions among all contributing factors, a Bayesian network approach was developed, based on citywide crash data collected in Kunshan, China from 2016 to 2018. A variable selection procedure based on Information Value (IV) was developed to identify significant variables, and the Bayesian network was employed to explicitly explore statistical associations between crash severity and significant variables. Results: In terms of balanced accuracy and AUCs, the proposed approach performed reasonably well. Bayesian modeling results indicated that the prior crash/violation experiences of road users and roadways were very important risk indicators. For example, migrant workers tend to have high injury risk due to their dangerous violation behaviors, such as retrograding, red-light running, and right-of-way violation. Furthermore, results showed that certain variable combinations had enhanced impacts on severity outcome than single variables. For example, when a migrant worker and a non-motorized vehicle are involved in a crash happening on a local road with high cumulative violation frequency in the previous year, the probability for drivers suffering serious injury or fatality is much higher than that caused by any single factor. Practical applications: The proposed methodology and modeling results provide insights for developing effective countermeasures to reduce crash severity and improve traffic system safety performance.     

Validating the underlying assumption of quasi-induced exposure technique disaggregated by crash injury severity

Introduction: Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality. Method: The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes. Results: The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.     

Data mining approach to model bus crash severity in Australia

Introduction: Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality. Method: Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006–2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted. Results: Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians’ distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.     

The effect of motorcyclists’ age on injury severities in single-motorcycle crashes with unobserved heterogeneity

Introduction: Due to the myriad of unique characteristics associated with motorcycle operation, motorcycle safety is a public health concern as complex as it is serious. National crash data suggest motorcyclists are 28 times more likely to be killed when compared to passenger car occupants. In the state of Florida, motorcycle crashes are 1.5 times more likely to result in the death of the rider, placing Florida among the top deadliest states for motorcyclists in the nation. Using police-reported data from 2016, this study addresses the complex and interconnected nature of the many characteristics associated with motorcycle operation by investigating the effect of age on motorcyclists’ riding behavior as it relates to injury severity for single-motorcycle crashes in the state of Florida. Method: To account for unobserved heterogeneity in the crash data, mixed logit models with heterogeneity in means and variances were estimated to model three injury severity outcomes (non-visible, severe, and fatal) for three age groups (under 30, 30–49, and 50 and above). Results: Model results indicate that age affects motorcyclists’ safety perception and ability to assess risks, thereby influencing their involvement in risky behaviors. Characteristics unique to motorcycle operation—spatial characteristics, speed, motorcycle type, time of day, helmet usage, alcohol consumption, ejection from motorcycle, passenger presence, endorsement status, and lighting—are further complicated by their dependency on the characteristics of the individual motorcyclist. Age of motorcyclist indicates a relationship between motorcyclists’ behavior and perceived safety. Conclusion: The model results indicated that statistically significant parameters constituted different models and they were not equal across the age groups of motorcyclists: aged under 30, aged 30–49, and aged 50 and above. Through advanced econometric modeling, this study fills a gap in the existing literature and assists the safety professionals, motorcycle trainers, policymakers, law enforcement agencies, and roadway designers in developing countermeasures.