Assessing the likelihood of secondary crashes on freeways with Adaptive Signal Control System deployed on alternate routes

Introduction: Reducing the likelihood of freeway secondary crashes will provide significant safety, operational and environmental benefits. This paper presents a method for assessing the likelihood of freeway secondary crashes with Adaptive Signal Control Systems (ASCS) deployed on alternate routes that are typically used by diverted freeway traffic to avoid any delay or congestion due to a freeway primary crash. Method: The method includes four steps: (1) identification of secondary crashes, (2) verification of alternate routes, (3) assessment of the likelihood of secondary crashes for freeways with ASCS deployed on alternate routes and non-ASCS (i.e. pre-timed, semi- or fully-actuated) alternate routes, and (4) investigation of unobserved heterogeneity of the likelihood of freeway secondary crashes. Four freeway sections (i.e., two with ASCS deployed on alternate routes and two non-ASCS alternate routes) in South Carolina are considered. Results and Conclusions: Findings from the logistic regression modeling reveal significant reduction in the likelihood of secondary crashes for one freeway section (i.e., Charleston I-26 E) with ASCS deployed on alternate route. Other factors such as rear-end crash, dark or limited light, peak period, and annual average daily traffic contribute to the likelihood of freeway secondary crashes. Furthermore, random-parameter logistic regression model results for Charleston I-26 E reveal that unobserved heterogeneity of ASCS effect exists across the observations and ASCS are associated with the reduction of the likelihood of freeway secondary crashes for 84% of the observations (i.e., primary crashes). Location of the primary crash on the freeway is observed to affect the benefit of ASCS toward freeway secondary crash reduction as the primary crash’s location determines how many upstream freeway vehicles will be able to take the alternate route. Practical Applications: Based on the findings, it is recommended that the South Carolina Department of Transportation (SCDOT) considers deploying ASCS on alternate routes parallel to freeway sections where high percentages of secondary crashes are found.     

Injury-severity analysis of lane change crashes involving commercial motor vehicles on interstate highways

Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs.     

Identifying crash propensity using specific traffic speed conditions

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.     

南京城市污水处理厂中微塑料的赋存特征

以南京市某污水处理厂及下游入江口作为研究对象,对其中微塑料的赋存特征进行研究.结果表明,污水处理厂进水中微塑料以尼龙材质为主（71.43%）,颜色以黑色为主（54.76%）,形状以纤维状为主（38.10%）,尺寸以50~500μm为主（69.05%）,丰度为4.2n/L（个/L）,二级处理后污水中微塑料丰度为1.6n/L,出水微塑料丰度为0.9n/L,污水处理厂处理工艺对微塑料的去除效率为78.57%.入江口处污染负荷指数为50.99,处于较低水平,由于污水处理厂出水持续排入,微塑料造成的生态风险仍不可忽视.本文研究结果为改进污水处理厂中微塑料去除工艺设计提供了基础数据.     

气载微塑料的赋存特征、迁移规律与毒性效应研究进展

微塑料作为一种新兴环境污染物,可能会对全球生态圈（水、土壤和空气）和人类健康造成潜在危害.本文综述了气载微塑料的分析方法、赋存特征、迁移规律及其毒性效应与机制等最新研究进展.当前气载微塑料定量表征主要依赖于体视显微镜检,而其定性分析则主要借助傅里叶红外光谱和拉曼光谱技术.气载微塑料遍及全球各大城市、海洋、甚至偏远山区.HYSPLIT4和沉降计算后推气流轨迹分析揭示了气载微塑料的迁移规律.气载微塑料对人体健康风险的影响研究表明,人类（尤其是儿童）能够吸入高丰度微塑料.今后研究应注重开发出适用于气载微塑料的精准、高效和低成本的分析仪器与方法,强化气载微塑料定量数据规范化,并从细胞、组织、器官等层面深入揭示气载微塑料及其复合污染物的毒性效应与机制.     

城市污水处理过程中微塑料赋存特征

以上海某污水厂为例,研究了中国沿海城市污水厂进出水、污泥中微塑料的赋存特征及微塑料排放量.结果表明,污水厂进出水中微塑料形态主要为纤维状.进水含量最高的种类依次为人造丝（36.84%）、合成革（21.05%）和聚酯纤维（18.42%）,出水中的种类依次为合成革（26.67%）、人造丝（24.44%）、聚酯纤维（17.78%）和聚乙烯（15.56%）.微塑料在总进水中的丰度为117n/L（n为微塑料个数）,调配水中为90n/L,总出水中为52n/L,污泥中为180n/50g（湿重）.该污水厂日出水量为280万m³/d,据此估算微塑料量由总进水中3276亿n/d降至总出水中1456亿n/d,去除率为55.6%.污水厂根据实际进水水质确定剩余污泥量150tDS/d,含有5.4亿个微塑料.此外,根据本研究结果讨论了有关国际刊物中对我国大陆范围内个人护理用品中微塑料入海量的不准确估算.     

东沙群岛附近海域风候特征分析

目的利用近22年的CCMP(Cross-Calibrated,Multi-Platform)风场资料,对东沙群岛海域的风候特征进行深入研究,为军地海洋建设提供科学依据。方法利用具有高精度的CCMP风场资料,统计分析近22年期间东沙群岛附近海域的风候特征。结果东沙群岛附近海域的海表风速呈"W"状双峰型月变化。峰值出现在11、12和1月期间,海表风速在9 m/s左右,次峰值位于6月,海表风速在6 m/s左右,东沙海域的年平均风速为6.5 m/s。东沙海域的海表风速表现出"U"型日变化特征。东沙附近海域的风速主要分布于3~5级,即3.4~10.7 m/s之间,1月的大风频率明显高于7月。1月和10月,东沙附近海域的风向以NE向为主,7月受西南季风影响显著,风向以S-SW向为主,4月风向则以ENE和SSE向为主。从全年来看,风向以偏东北向为主,S-SW向居其次。在近22年期间,东沙海域的海表风速整体上以0.0424 m/(s·a)的速度显著性逐年线性递增。结论运用高精度的CCMP风场资料,通过统计分析方法得到了东沙群岛海域的风候特征,对军地海战场环境建设具有科学支持作用。