基于RBF神经网络的不利天气道路通行能力计算

不利天气下影响城市道路通行能力的各种因素都具有随机的、非线性,采用常态条件下修正理论通行能力的计算方法是不适合的.文章结合RBF神经网络模型方法能够良好地分析出随机的、非线性的特点,对路网组成单元进行重新划分,选定不利天气下道路通行能力的影响因素,建立了道路通行能力计算的RBF神经网络模型.并依据哈尔滨市暴雨天气下道路的实际情况进行了算例分析,计算的道路通行能力与实测数据最大误差为-1.16%,验证了模型的可行性和有效性.     

Exploring relationships between driving events identified by in-vehicle data recorders,infrastructure characteristics and road crashes

There is an increasing interest in technology-based solutions that can assist drivers in reducing their risk of involvement in road crashes. Previous studies showed that driving events produced by in-vehicle data recorders (IVDR) are applicable for identification of unsafe driving patterns, while combined examinations of driving events and road infrastructure characteristics are rare. This study explored the relationship between the IVDR-driving events, road characteristics and crashes, to examine a potential of the events for predicting crashes and identification of high-risk locations on the road network. The study database included 3500 segments of the interurban roads in Israel, for which the automatically produced IVDR events were matched with road infrastructure characteristics and crashes. Negative-binomial regression models were adjusted for the relationships between road characteristics and driving events, and subsequently, between events and crashes, given the exposure. Significant impacts were found, yet various event types showed different relations to the infrastructure characteristics and different effects on crashes, on various road types. Better road conditions were associated with a decrease in “braking” events and an increase in the “speed alert” events, where road layout constraints and junction proximity were associated with an opposite effect on events. “Braking” and total events showed better potential for predicting crashes on single-carriageway roads, with a positive link to crashes, where for other road types the “speed alert” events were stronger related to crashes, but with a negative link. The heterogeneity of findings indicates a need in further research of the above relationship, with a particular focus on definitions of driving events produced by the IVDR or other technologies.     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

空间天气对导航卫星的影响

简要介绍了空间天气的基本概念,对灾害性空间天气可能对导航卫星所产生的主要影响进行了探索;进而着重分析了电离层电子密度、电离层暴、电离层突然骚扰等空间天气要素对卫星导航定位系统的影响。     

气候条件对沥青路面路用性能的影响

结合我国沥青技术标准,参照美国SHRP研究成果,在调查陕西省和甘肃省部分地区气候特征的基础上,研究了气候条件对沥青路面路用性能的影响.     

A probabilistic framework for weather-based rerouting and delay estimations within an Airspace Planning model

In this paper, we develop a novel severe weather-modeling paradigm to be applied within the context of a large-scale Airspace Planning and collaborative decision-making model in order to reroute flights with respect to a specified probability threshold of encountering severe weather, subject to collision safety, airline equity, and sector workload considerations. This approach serves as an alternative to the current practice adopted by the Federal Aviation Administration (FAA) of adjusting flight routes in accordance with the guidelines specified in the National Playbook. Our innovative contributions in this paper include (a) the concept of “Probability-Nets” and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service; (c) the generation of flight plans that circumvent severe weather phenomena with specified probability threshold levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Additionally, we conduct an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. Computational results and insights are presented based on flight test cases derived from the Enhanced Traffic Management System data provided by the FAA and using weather scenarios derived from the Model Output Statistics forecast data provided by the National Weather Service.     

Evolving Narratives of Low-Carbon Futures in Transportation

Scenarios of low-carbon transport demonstrate that a vast range of different outcomes is possible and contingent on policy, technology and cultural developments. But a closer look indicates that different schools of thought suggest possible pathways diverging in their fine structure. This perspective reveals how three different scientific communities — integrated assessment modelers, transport-sector modelers, and place-based modelers — emphasize distinct solution domains. While integrated assessment models focus on fuel composition, transport-sector models put slightly higher emphasis on efficiency measures; in turn place-based research specifies idiosyncratic behavioral and infrastructural mitigation options that are likely to be beneficial in realizing local co-benefits. These specific local approaches could mitigate urban transport emissions by 20–50%, higher than that revealed in aggregate global models. We discuss differences in approach, possibilities for reconciliation, and the implications of normative assumptions. Targeted three-directional interactions would foster comprehensive understanding of possible low-carbon transportation futures.     

热带沙漠气候环境汽车空调系统的开发

阐述热带沙漠气候环境下汽车空调制冷性能的研究,对热带沙漠气候环境下整车空调系统的舒适性温度指标进行分析探讨;并以某车型出口迪拜的空调系统改进性开发为实例,从系统架构、性能指标、部件布置和试验验证等方面行介绍。     

轻型汽油车海拔环境排放和油耗特性的研究

海拔高度增加会导致空气密度减小,氧含量下降而使发动机的燃烧变差,污染物排放增加,整车油耗也会发生变化。因此有必要对不同海拔地区的汽油车排放及油耗情况进行研究,目前国内对相关领域的研究还处于空白。本文利用我中心海拔环境仓对两辆轻型汽油车进行各海拔高度环境下的排放及油耗试验,并对其在相应条件下排放污染物的排放量和油耗情况进行分析和比较,最终得出轻型汽油车的排放及油耗随海拔高度的变化规律。