The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways

Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.     

"有序"开采:北方地区地下水资源利用中两难选择的途径

北方干旱、半干旱地区地下水超采严重，水资源供需矛盾突出。为探索一条地下水合理开发利用的途径．本文提出了“有序”开采的思路：在来来几十年内．政府部门应根据人口，经济发展规划提出地下水资源需求量的控制计划．并给下属各地区下达取水配额。使储存资源的消耗处于有计划和监控范围内。有序开采应分阶段实现：第一阶段应以抑侧超采量的过快增长为目标；第二阶段应以需水总量的下降为目标；第三阶段应实现总需水量低于地下水系统补给资源量，使数十年超采造成的储存资源亏空逐年得以偿还。“有序”开采应注意三点：①超采必须限制在有限时间内；②开采活动应在政府的严格控制之下。任何超过计划配额的取水行为都是不允许的；③开采量和总需水量的下降，必须以节水为前提。发达国家的需水量变化经历证明。通过“有序”开采实现北方地区地下水可持续利用是可行的．     

Water quality assessment in Qu River based on fuzzy water pollution index method

A fuzzy improved water pollution index was proposed based on fuzzy inference system and water pollution index. This method can not only give a comprehensive water quality rank, but also describe the water quality situation with a quantitative value, which is convenient for the water quality comparison between the same ranks. This proposed method is used to assess water quality of Qu River in Sichuan, China. Data used in the assessment were collected from four monitoring stations from 2006 to 2010. The assessment results show that Qu River water quality presents a downward trend and the overall water quality in 2010 is the worst. The spatial variation indicates that water quality of Nanbashequ section is the pessimal. For the sake of comparison, fuzzy comprehensive evaluation and grey relational method were also employed to assess water quality of Qu River. The comparisons of these three approaches'' assessment results show that the proposed method is reliable.     

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study

Objective: The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques.

Methods: This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007–2013 to identify VRU injury severity factors at HRGCs.

Results: The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.

Conclusions: The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.     

基于零膨胀模型的航空器危险接近事件安全影响因素研究*

为探究航空器危险接近事件安全影响因素,降低航空器碰撞风险,利用二元概率函数区分航空器危险接近事件危险状态,利用probit函数确定不同风险等级结果,构建航空器危险接近事件零膨胀有序概率模型。结果表明:机组人员程序执行偏差、侵犯领空（无人驾驶飞机未获许可在管制空域飞行）、意图沟通无效或不充分、未监控其他飞机或监控不及时4个安全影响因素,增加航空器危险接近时碰撞风险概率分别为7.03%、4.25%、7.14%和6.03%。     

基于地下水中四环素去除的缓释剂构建及其性能

为了有效去除地下水中抗生素等有机污染物,本文首次提出有序介孔氧化锰（O-MnO_x）为活性催化剂和过硫酸钠（Na₂S₂O₈）为氧化剂制备活化过硫酸钠缓释剂,并将其用于地下水中四环素的降解修复过程.通过有序O-MnO_x与H₂O₂和Na₂S₂O₈构建的两种类芬顿催化反应体系,明确了O-MnO_x对过硫酸钠的活化及其对水中四环素优异的降解性能.以O-MnO_x与Na₂S₂O₈为活性组分制备的缓释剂在地下水中可以缓慢释放过硫酸根离子.缓释剂中O-MnO_x加入比例增加,其释放性能减弱,而Na₂S₂O₈加入比例减少,过硫酸根释放速率就会加快.O-MnO_x可以活化过硫酸根溶...     

综合评选FGD技术专家系统的研制

运用模糊决策方法和推理机原理，研制了电厂选择合适的烟气脱硫技术的专家系统，并实现了系统的软件化。用该系统对几个电厂的烟气脱硫技术选择过程进行了模拟测试，测试结果与实际情况较为吻合，初步证明了本系统的可用性和适用性。