LNG运输船船型浅析

简述了LNG船的沿革、液货舱的类型及典型结构、总布置特征、船舶要素特征以及具体防火要求，并根据大量的实船资料，通过回归统计分析的方法得出船舶主尺度与总吨位间的系列关系曲线。     

基于GA-LSSVR模型的路网短时交通流预测研究

目前,很多短时交通流预测方法仅利用某一路段历史数据的时间相关性或者道路上下游路段的时空相关性进行交通流预测,未充分考虑路网所有路段之间的时空相关性.提出了一种基于稀疏混合遗传算法优化的最小二乘支持向量回归(LSSVR)模型,并应用于路网短时交通流预测.该预测模型不仅可以自动优化LSSVR模型参数,而且可以从高维路网交通流数据中选择有助于交通流预测的变量子集.实验结果表明,与LSSVR模型相比,所提方法具有更好的预测能力;而且,少量时空变量被选择出来构建预测模型,极大减少了信息冗余,改进了模型可解释性.     

基于支持向量回归机的道路交通事故预测模型研究

针对道路交通事故预测具有随机波动性较大、信息量较少和非线性数据序列预测的特点,引入支持向量回归机（SVR）,建立基于SVR的道路交通事故预测模型。通过实例计算,证明基于SVR的道路交通事故预测模型具备非线性、所需数据资料较少、建模简单和计算快捷等优点,同时与RBF神经网络预测模型相比,该模型的预测精度高、泛化能力强,更适用于道路交通事故预测。     

大城市合理客运交通出行结构研究

通过考察国际大都市公共交通结构的主要特征,总结出大城市合理客运交通结构的发展趋势。基于对10个国际大城市的相关指标回归分析,探讨了大城市合理公交出行结构预测模型;最后,以杭州为例,应用预测模型,在分析杭州客运交通结构与国际知名大都市客运交通结构的差异以及产生差距原因的基础上,确定了杭州市在地铁建成后的合理客运交通结构。     

结构噪声核心价值与理论逻辑解读第一部分:释义、价值及认知颠覆

  目的  与单层、双层和浮筏隔振相比,阻振质量对结构纵波和弯曲波传导衰减很小,研究人员少有兴趣应用这类措施。然而当潜艇声隐身进入\     

我国综合交通系统的社会贡献评价研究——以2000-2017年数据为例

本文首先介绍了研究背景,提出研究目的。通过梳理国内外相关文献,提出现有研究尚未涉及综合交通系统社会贡献方面。在解析综合交通社会贡献机理基础上,根据我国2000—2017年社会系统和综合交通数据确定我国社会系统发展指数S和交通系统发展指数T的组成指标,在每种交通方式内采用因子分析法确定各指标权重,结合各指标数值得到S和T数值。应用格兰杰检验法证明T是S的原因,由S关于T的回归函数可得我国综合交通社会贡献评价函数,处理后得到最佳状态数值,发现实际数值与之相比仍有较大提升空间。最后,提出相关政策建议。     

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.     

Modeling the cost of bird strikes to US civil aircraft

The objective of our analysis is to develop a model of damage costs that arise from collisions between aircraft and birds, based on data drawn from the Federal Aviation Administration National Wildlife Strike Database (NWSD). We develop a two-part model, composed of two separate statistical models, that accounts for the effects of aircraft mass category, engine type, component of the aircraft struck, and the size and number of birds struck. Our results indicate the size of bird, number of birds, and engine ingestions are the largest determinants of strike-related costs. More generally, our result is a model that provides a better understanding of the determinants of damage costs and that can be used to interpolate the substantial amount of missing data on damage costs that currently exists within the NWSD. A more complete accounting of damage costs will allow a better understanding of how damage costs vary geographically and temporally and, thus, enable more efficient allocation of management resources across airports and seasons.     

Analysis of common-cause and special-cause variation in the deterioration of transportation infrastructure: A field application of statistical process control for structural health monitoring

We present a statistical process control framework to support structural health monitoring of transportation infrastructure. We contribute an integrated, generally-applicable (to various types of structural response data) statistical approach that links the literatures on statistical performance modeling and on structural health monitoring. The framework consists of two parts: The first, estimation of statistical models to explain, predict, and control for common-cause variation in the data, i.e., changes, including serial dependence, that can be attributed to usual operating conditions. The ensuing standardized innovation series are analyzed in the second part of the framework, which consists of using Shewhart and Memory Control Charts to detect special-cause or unusual events.We apply the framework to analyze strain and displacement data from the monitoring system on the Hurley Bridge (Wisconsin Structure B-26-7). Data were collected from April 1, 2010 to June 29, 2011. Our analysis reveals that, after controlling for seasonal effects, linear trends are significant components of the response measurements. Persistent displacement may be an indication of deterioration of the bridge supports. Trends in the strain data may indicate changes in the material properties, i.e., fatigue, sensor calibration, or traffic loading. The results also show that autocorrelation and conditional heteroscedasticity are significant sources of common-cause variation. Use of the control charts detected 43 possible special-cause events, with approximately 50% displaying persisting effects, and 25% lasting longer than one week. Analysis of traffic data shows that unusually heavy loading is a possible cause of the longest special-cause event, which lasted 11 days.     

The inherent precision of regression estimated route probabilities

From basic assumptions about independent and consistent driver behaviour, and with data from traffic counts, we derive statistical properties of regression or correlation estimates of route selection probabilities, turning probabilities and travelling times. Our modelling is conditional in a way that avoids most traffic generation problems and permits an asymptotic analysis of the precision under mild assumptions allowing non-stationarity. This allows us to put together non-stationary data from the corresponding time intervals during several days when we aim at high precision estimates.