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介绍了美国联邦公路局（FHWA）支持的动态交通分配（DTA）研究项目的发展概况。FHWA启动DTA项目的目标是研发一套可实际应用的实时交通信息估计与预测系统(TrEPS)，其中提供的交通估计与预测信息可用于动态交通管理，以满足智能交通系统（ITS）的信息需要。讨论了DTA项目，ErEPS在FHWA的战略规划和美国国家ITS体系结构中的地位，ErEPS的用户，以及其研究、开发、实施概况；提出了在我国发展类似系统的一些建议，包括实现ErEPS原型的本地化、引入各向异性交通流模型及混合交通仿真法以改善TrEPS的性能等。     

水上交通事故模式的研究

文中提出采用数据仓库技术搭建水上交通事故(事件)管理、分析信息平台，利用数据挖掘技术自动描述、寻找事故数据背后有价值的的模式和知识．实例给出1995～2000年期间长江水域的船舶事故(事件)历史数据库的构建，通过重特大恶性碰撞事故案例的数据挖掘，量化提取事故特征，并利用广义线性模型对船舶碰桥事故建立概率预测模型，为科学组织管理长江水上交通提供理论依据．     

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针对传统四阶段法不太适合长大铁路干线需求预测的实际，通过时四阶段需求预测方法及各种模型的改造，建立了一套适合我国铁路项目客运量预测的预测方法和程序.通过对京沪高速铁路的具体应用，论文就参数标定方法和应注意的问题提出了一些研究结论。     

高速三体船的阻力预报方法研究

高速航行的三体船,其航行姿态会随着傅汝德数（Fn）的增加而发生变化。常见的CFD计算预报实船阻力常常以设计浮态为基准、固定船舶航行姿态的方法进行,也是导致计算偏差的原因之一。文中选定一艘三体船模型,针对自由和约束两种展开CFD数值模拟和阻力计算,得到其差别。为了进一步了解其兴波情况和摩擦阻力的变化规律,在0.3＜Fn＜0.6的范围内对处于自由和约束状态下的模型流场相关细节进行了分析。最后,对该三体船型进行了模型试验验证。     

美国海军业务化海洋预报系统综述

美国海军海洋预报系统最早始于苏联时期,自此之后,美国海军制定了一系列计划,构建海洋观测网络,开发海洋数值模式,发展军事海洋学,旨在建成一套完整、精确的全球海洋预报系统并实现业务化应用。美国海军海洋学一直走在世界前沿,本文将对美国海军海洋预报系统的发展历程、成果及其现状进行阐述,这对于我国军事海洋学以及海洋预报系统的发展具有重要借鉴作用。     

基于三维势流理论的耐波性数值预报

准确预报船体运动响应对于砰击等波浪载荷的计算以及合理结构设计具有重要意义。船舶在大幅波浪中的运动呈明显的非线性,而现阶段耐波性预报多采用线性切片方法。三维水动力分析软件 WASIM基于时域势流理论,采用 Rankine面元法预报船舶在波浪中的运动响应,并考虑了多种非线性因素。本文以标模 DTMB5512为对象,采用 WASIM预报其在不同航速下的耐波性,并与基于线性切片理论的计算结果和模型试验结果进行对比。结果表明：利用 WASIM计算得到的船体运动响应比其他方法更接近试验值,合理体现了船舶在风浪中的实际耐波性能。因此,利用 WASIM能够较好地评估船舶在波浪中的非线性耐波特性。     

带月池深海钻井船摇荡运动性能分析

海上钻井作业过程要求钻井船具备良好的稳定性能,而月池内流体柱的不规则振荡以及钻井船细长的船体外形都将对船体运动性能造成不利影响,从而限制其在恶劣海域的作业适应性。文章基于三维势流理论对带月池钻井船的摇荡运动进行预报,并通过船模试验对预报结果进行有效性验证。在此基础上,提出钻井船摇荡运动衡准值,对钻井船海上作业适应性进行研究,分析月池尺度和形状对钻井船运动性能的影响。     

Comparing prediction methods for maritime accidents

ABSTRACT

The purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

Short/medium-term prediction for the aviation emissions in the en route airspace considering the fluctuation in air traffic demand

This paper proposes a novel short/medium-term prediction method for aviation emissions distribution in en route airspace. An en route traffic demand model characterizing both the dynamics and the fluctuation of the actual traffic demand is developed, based on which the variation and the uncertainty of the short/medium-term traffic growth are predicted. Building on the demand forecast the Boeing Fuel Flow Method 2 is applied to estimate the fuel consumption and the resulting aviation emissions in the en route airspace. Based on the traffic demand prediction and the en route emissions estimation, an aviation emissions prediction model is built, which can be used to forecast the generation of en route emissions with uncertainty limits. The developed method is applied to a real data set from Hefei Area Control Center for the en route emission prediction in the next 5 years, with time granularities of both months and years. To validate the uncertainty limits associated with the emission prediction, this paper also presents the prediction results based on future traffic demand derived from the regression model widely adopted by FAA and Eurocontrol. The analysis of the case study shows that the proposed method can characterize well the dynamics and the fluctuation of the en route emissions, thereby providing satisfactory prediction results with appropriate uncertainty limits. The prediction results show a gradual growth at an average annual rate of 7.74%, and the monthly prediction results reveal distinct fluctuation patterns in the growth.