基于XML的船体结构CAD数据交换研究

分析了XML技术在船体结构数据信息交换应用中的特点,归纳了船体结构在计算机里的数据表达形式,并建立了相应的XML Schema.分别为船体结构设计软件CATIA和Intelliship设计了XML交换接口,基于XML文件格式,实现了不同CAD系统之间的船体结构数据交换.     

基于数据仓库的船舶运输智能决策支持系统

为解决业务数据量的不断增加导致传统船舶运输决策支持系统难以满足实际要求的问题,提出了基于数据仓库的船舶运输智能决策支持系统。设计了具有基础数据层、分析管理层、决策功能层和交互层的四层体系结构,建立了船舶运输数据仓库,使用联机分析处理技术将大量的源数据转化为有用的信息,服务于决策过程,实现了定性与定量相结合的船舶运输决策支持。采用Fortran和VB两种语言混合编程技术实现了系统执行效率的优化。     

浅谈GIS在道路管理中存在的问题

充分利用GIS对空间信息的管理能力和生动形象的图形表现形式,结合交通系统的具体特点,开发适合现代变通发展需要GIS,具有鲜明的实际意义。     

四轮驱动汽车的工作特性研究

随着汽车工业的迅猛发展,四轮驱动的汽车越采越被人们所重视。从分析四轮驱动汽车的主要特点入手,对四轮驱动汽车的主要装置、驱动系统的总布置、行驶特性进行了分析。     

基于决策属性的关联规则挖掘

针对具有决策属性的数据库模型,提出了高效挖掘关联规则算法,即矩阵划分算法:根据决策属性将扫描后的数据库划分成两个包含不同决策属性的矩阵,分别采用向量法挖掘频繁项目集.关联规则的生成可充分利用"与"运算的优点,查找规则前件或后件的支持度.所提出的算法减少了候选二项频集的生成,以及"与"运算的大小,与apriori算法及传统的向量法挖掘关联规则相比,效率明显提高.     

基于DSP的电压补偿算法及实现

为了保证对电压质量要求较高的敏感负载正常工作,需要及时补偿供电系统的电压波动以维持用户电压稳定.根据这一目的提出一种快速的电压补偿算法,该算法使控制电路能够迅速检测出电压波动并输出电压补偿信号.利用工业控制用高性能数字处理芯片TMS320F2812实现该算法,并进行了控制系统的相关设计.实验结果证明该算法以及采用的DSP控制器能够快速的检测出电压波动量,并驱动功率电路实现电压的及时补偿.     

The determinants of car ownership in England and Wales from anonymous 2001 census data

Within the transportation research literature, the attempt to understand and predict the level of car ownership is probably one of the most popular areas of study. The primary reason for this is understandable, having access to a vehicle increases an individual’s (or their household’s) travel options, leading to greater mobility. Secondary reasons for this scrutiny include the need to predict future transport investment in road infrastructure and the commercial demand for new vehicles. This paper attempts to predict the level of household car ownership as a function of the characteristics of the household and the individuals that make up the household. The primary data source for this study comes from the 2001 United Kingdom Census and the analysis methods used are from the discipline of data mining. The results of this study are in line with those from previous research but show a potential to predict the higher levels of household car ownership with greater accuracy than other similar studies.     

Passenger travel characteristics and bus operational states: a study based on IC card and GPS data in Yinchuan,China

ABSTRACT

In recent years, public transport has been developing rapidly and producing large amounts of traffic data. Emerging big data-mining techniques enable the application of these data in a variety of ways. This study uses bus intelligent card (IC card) data and global positioning system (GPS) data to estimate passenger boarding and alighting stations. First, an estimation model for boarding stations is introduced to determine passenger boarding stations. Then, the authors propose an innovative uplink and downlink information identification model (UDI) to generate information for estimating alighting stations. Subsequently, the estimation model for the alighting stations is introduced. In addition, a transfer station identification model is also developed to determine transfer stations. These models are applied to Yinchuan, China to analyze passenger flow characteristics and bus operations. The authors obtain passenger flows based on stations (stops), bus lines, and traffic analysis zones (TAZ) during weekdays and weekends. Moreover, average bus operational speeds are obtained. These findings can be used in bus network planning and optimization as well as bus operation scheduling.     

Not bored yet - Revisiting respondent fatigue in stated choice experiments

Stated choice surveys are used extensively in the study of choice behaviour across many different areas of research, notably in transport. One of their main characteristics in comparison with most types of revealed preference (RP) surveys is the ability to capture behaviour by the same respondent under varying choice scenarios. While this ability to capture multiple choices is generally seen as an advantage, there is a certain amount of unease about survey length. The precise definition about what constitutes a large number of choice tasks however varies across disciplines, and it is not uncommon to see surveys with up to twenty tasks per respondent in some areas. The argument against this practice has always been one of reducing respondent engagement, which could be interpreted as a result of fatigue or boredom, with frequent reference to the findings of Bradley and Daly (1994) who showed a significant drop in utility scale, i.e. an increase in error, as a respondent moved from one choice experiment to the next, an effect they related to respondent fatigue. While the work by Bradley and Daly has become a standard reference in this context, it should be recognised that not only was the fatigue part of the work based on a single dataset, but the state-of-the-art and the state-of-practice in stated choice survey design and implementation has moved on significantly since their study. In this paper, we review other literature and present a more comprehensive study investigating evidence of respondent fatigue across a larger number of different surveys. Using a comprehensive testing framework employing both Logit and mixed Logit structures, we provide strong evidence that the concerns about fatigue in the literature are possibly overstated, with no clear decreasing trend in scale across choice tasks in any of our studies. For the data sets tested, we find that accommodating any scale heterogeneity has little or no impact on substantive model results, that the role of constants generally decreases as the survey progresses, and that there is evidence of significant attribute level (as opposed to scale) heterogeneity across choice tasks.     

An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.