基于CFD的内河船深浅水中操纵性预报

由于岸壁效应和浅水效应,内河船舶在限制水域作操纵运动时通常受到比在开阔水域中更大的水动力.这些水动力对船舶操纵性具有不利影响,有可能导致船舶碰撞或触底等海上事故.因此,为了在船舶设计阶段预报其操纵性能,考虑浅水效应和岸壁效应以准确计算内河船舶操纵运动水动力非常重要.本文基于CFD方法,通过对粘性绕流进行数值模拟,对长江中营运的三艘内河船舶的操纵运动水动力进行计算.首先,为了验证数值方法的可靠性,对标模KVLCC2纯横荡和纯首摇试验的水动力进行计算,并将计算结果与现有的试验数据进行对比.然后,对三艘内河船舶在不同水深下的静舵试验、纯横荡和纯首摇试验进行数值模拟,计算得到水动力及相应的线性水动力导数.最后,基于计算得到的水动力导数,获得Nomoto模型中的操纵性参数,对比分析三艘内河船舶在深浅水中的操纵性能.结果表明,本文方法可以揭示不同水深下三艘内河船舶的操纵性变化趋势.该方法可为船舶设计阶段内河船舶深浅水中的操纵性预报提供一种实用的工具.     

铁路专用线选线控制因素分析

为系统分析出铁路专用线选线时各种控制要素,尝试探寻铁路专用线选线的独特规律,通过对铁路专用线选线功能及作用的分析,系统阐述铁路专用线选线时"路内"、"路外"各种控制要素和关键优缺点,同时理论与实践相结合,以华能铜川电厂铁路专用线工程为实例,对铁路专用线选线时涉及的运输通道、技术标准、接轨方案等进行多角度比选分析,使推荐的线路方案满足各方需求,工程合理可行。铁路专用线选线与国铁干线选线相比,既有着相同的特点也有着独特的考虑因素,为铁路专用线选线提供系统、全面、科学的控制因素及比选方法。     

上海轨道交通3、4号线共线运营行车组织研究

基于上海轨道交通3、4号线共线运营特点,分析了共线运营在行车组织上存在非共线段运能不足、共线段运营中断时运营调整难度大的问题。通过非共线段运能不足问题剖析和共线段运营线路中断处置原则分析,提出了进行线路拆分提升运能,以及在共线段中断时的行车组织调整方案,并得出3、4号线各自独立运营需对配线进行改造的结论。     

城市轨道交通LTE综合承载CBTC与专用无线集群业务

对宁波市轨道交通3号线及宁奉城际铁路工程采用的LTE(长期演进)技术综合承载CBTC(基于通信的列车控制)与专用无线集群信号系统的设计方案进行了介绍。这些设计方案主要包括LTE有线网络、LTE车-地无线通信、LTE时钟同步、业务隔离以及综合供电方案等,系在传统的LTE仅承载CBTC业务的基础上,考虑了同时承载集群业务。为进一步增强整个系统的可靠性、安全性,独创性地设计了基带处理单元的跨网连接、设备冗余配置、集群无线覆盖、轨旁漏缆安装高度以及跨专业供电的方案。     

浅谈异型拱桥桥型设计

通过一个桥型方案设计实例 ,简要介绍异型拱桥的特点     

Neighborhood services,trip purpose,and tour-based travel

Communities are increasingly looking to land use planning strategies to reduce drive-alone travel. Many planning efforts aim to develop neighborhoods with higher levels of accessibility that will allow residents to shop closer to home and drive fewer miles. To better understand how accessible land use patterns relate to household travel behavior, this paper is divided into three sections. The first section describes the typical range of services available in areas with high neighborhood accessibility. It explains how trip-based travel analysis is limited because it does not consider the linked (chained) nature of most travel. The second section describes a framework that provides a more behavioral understanding of household travel. This framework highlights travel tours, the sequence of trips that begin and end at home, as the basic unit of analysis. The paper offers a typology of travel tours to account for different travel purposes; by doing so, this typology helps understand tours relative to the range of services typically offered in accessible neighborhoods. The final section empirically analyzes relationships between tour type and neighborhood access using detailed travel data from the Central Puget Sound region (Seattle, Washington). Households living in areas with higher levels of neighborhood access are found to complete more tours and make fewer stops per tour. They make more simple tours (out and back) for work and maintenance (personal, appointment, and shopping) trip purposes but there is no difference in the frequency of other types of tours. While they travel shorter distances for maintenance-type errands, a large portion of their maintenance travel is still pursued outside the neighborhood. These findings suggest that while higher levels of neighborhood access influences travel tours, it does not spur households to complete the bulk of their errands close to home.     

多轴专用车底盘轴荷估算方法

在理论计算和实践经验的基础上,论述和介绍了多轴(三轴及三轴以上)专用汽车底盘方案设计阶段轴荷估算的方法.     

浅析高速公路特殊路基施工方法

根据东营市高速公路建设的施工经验,介绍了高速公路施工工程中特殊地基的处理方法,包括水泥固化土、粉喷桩、土工布等施工方法,为高速公路工程特殊地基施工提供了借鉴.     

特种弹药铁路运输可调式定位装置研究

概述了特种弹药铁路运输可调试定位装置的主要功能、技术要求、主要战技术指标，介绍了特种弹药铁路运输采用的从箱体顶部固定方式，并进行了相应的可调式定位装置研究，将装置分为整体固定和单点独立固定两种，用于固定弹箱在敞车内的位置，确保弹体运输安全。     

Forecasting current and next trip purpose with social media data and Google Places

Trip purpose is crucial to travel behavior modeling and travel demand estimation for transportation planning and investment decisions. However, the spatial-temporal complexity of human activities makes the prediction of trip purpose a challenging problem. This research, an extension of work by Ermagun et al. (2017) and Meng et al. (2017), addresses the problem of predicting both current and next trip purposes with both Google Places and social media data. First, this paper implements a new approach to match points of interest (POIs) from the Google Places API with historical Twitter data. Therefore, the popularity of each POI can be obtained. Additionally, a Bayesian neural network (BNN) is employed to model the trip dependence on each individual’s daily trip chain and infer the trip purpose. Compared with traditional models, it is found that Google Places and Twitter information can greatly improve the overall accuracy of prediction for certain activities, including “EatOut”, “Personal”, “Recreation” and “Shopping”, but not for “Education” and “Transportation”. In addition, trip duration is found to be an important factor in inferring activity/trip purposes. Further, to address the computational challenge in the BNN, an elastic net is implemented for feature selection before the classification task. Our research can lead to three types of possible applications: activity-based travel demand modeling, survey labeling assistance, and online recommendations.