Modelling and Analysis of Turning Motion of a Subsurface Mapping AUV with Split-Hull Design

There is much need for autonomous underwater vehicles (AUVs) for inspection and mapping purposes. Most conventional AUVs use torpedo-shaped single-rigid hull, b...     

开目CAPP在武船机制的应用

武昌造船厂机械制造分厂(简称武船机制)是武昌造船厂的下属核心企业,主要进行武船配套零部件的工艺设计与制造。在推进信息化建设过程中,采用了开目CAPP解决方案,取得了较好的效果。武船机制工艺信息化系统需求分析1.产品和工艺特点分析(1)产品的质量要求高,工期要求严格,对工艺设计质量和效率有很高的要求。(2)采用按订单生产的方式,进行独立的成本核算和控制。(3)需从总厂ERP系统中提取工程任务树,建立工艺设计与制造任务。设计完成的工艺数据也需按照ERP系统要求的格式进行统计并向ERP系统进行传递。(4)不同订单中的工艺专业种类繁…     

闭环控制在交通安全管理中的运用

现代管理理论认为,有效的管理活动应该是一个闭合的环,应该是一个持续改进与提高的过程.柳州恒达巴士股份有限公司运用PDCA的闭环控制模式来检查发现交通安全管理中的薄弱环节,并用"戴明环"理论来不断提升管理水平,并取得了较为明显的效果.     

台北公共交通月及无车日活动回顾与展望

台北国际无车日自2002年起举办了5次,由最早的骑自行车活动扩大至包含“走路上学日”与“公共交通月”丰富多彩的系列活动,渐渐获得民众认同。无车日活动的目标是通过活动的倡导和社会参与来改变民众使用交通工具的行为,潜移默化的效应可促使形成可持续交通政策,当可持续交通政策与基础设施建设落实后又可具体改变交通环境,形成良性的循环机制。首先说明了“无车日”的由来,并回顾台北都会区过去5年来举行无车日系列活动的内容与经验,同时基于活动的发展与成果归纳出无车日活动的成功要素,最后提出未来活动将扩大举办的内容、方式与展望,以期能为其他城市作参考。     

浅论桥梁伸缩装置的选型和施工要点

该文论述了几类常用的桥梁伸缩装置的构造特点和设计施工的基本要求,结合工程实际简要介绍了各种型式的伸缩装置在不同类型桥梁结构中的应用,包括伸缩装置选型的要点、主要的施工工艺和安装注意事项等。     

Assessment of factors associated with travel time reliability and prediction: an empirical analysis using probabilistic reasoning approach

Significant efforts have been made in modeling a travel time distribution and establishing measures of travel time reliability (TTR). However, the literature on evaluating the factors affecting TTR is not well established. Accordingly, this paper presents an empirical analysis to determine potential factors that are associated with TTR. This study mainly applies the Bayesian Networks model to assess the probabilistic association between road geometry, traffic data, and TTR. The results from this model reveal that land use characteristics, intersection factors, and posted speed limits are directly associated with TTR. Evaluating the strength of the association between TTR and the directly related variables, the log odds ratio analysis indicates that the land use factor has the highest impact (0.83) followed by the intersection factor (0.57). The findings from this study can provide valuable resources to planners and traffic operators in their decision-making to improve TTR with quantitative evidence.     

Subjective well-being and travel: retrospect and prospect

Although the improvement of well-being is often an implicitly-assumed goal of many, if not most, public policies, the study of subjective well-being (SWB) and travel has so far been confined to a relatively small segment of the travel behavior community. Accordingly, one main purpose of this paper is to introduce a larger share of the community to some fundamental SWB-related concepts and their application in transportation research, with the goal of attracting others to this rewarding area of study. At the same time, however, I also hope to offer some useful reflections to those already working in this field. After discussing some basic issues of terminology and measurement of SWB, I present from the literature four conceptual models relating travel and subjective well-being. Following one of those models, I review five ways in which travel can influence well-being. I conclude by examining some challenges associated with assessing the impacts of travel on well-being, as well as challenges associated with applying what we learn to policy.

     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Multi‐objective highway alignment optimization using a genetic algorithm

The available highway alignment optimization algorithms use the total cost as the objective function. This is a single objective optimization process. In this process, travel‐time, vehicle operation accident earthwork land acquisition and pavement construction costs are the basic components of the total cost. This single objective highway alignment optimization process has limited capability in handling the cost components separately. Moreover, this process cannot yield a set of alternative solutions from a single run. This paper presents a multi‐objective approach to overcome these shortcomings. Some of the cost components of highway alignments are conflicting in nature. Minimizing some of them will yield a straighter alignment; whereas, minimizing others would make the alignment circuitous. Therefore, the goal of the multiobjective optimization approach is to handle the trade‐off amongst the highway alignment design objectives and present a set of near optimal solutions. The highway alignment objectives, i.e., cost functions, are not continuous in nature. Hence, a special genetic algorithm based multi‐objective optimization algorithm is suggested The proposed methodology is demonstrated via a case study at the end.     

The Alternative Motor Fuels Act, alternative-fuel vehicles, and greenhouse gas emissions

The corporate average fuel economy (CAFE) standard is the major policy tool to improve the fleet average miles per gallon of automobile manufacturers in the US. The Alternative Motor Fuels Act (AMFA) provides special treatment in calculating the fuel economy of alternative-fuel vehicles to give manufacturers CAFE incentives to produce more alternative-fuel vehicles. AMFA has as its goals an increase in the production of alternative-fuel vehicles and a decrease in gasoline consumption and greenhouse gas emissions. This paper examines theoretically the effects of the program set up under AMFA. It finds that, under some conditions, this program may actually increase the production of fuel-inefficient gasoline vehicles, gasoline consumption and greenhouse gas emissions.