航运市场预测方法研究

引入灰色理论和回归预测模型,提出预测海运市场容量的方法,运用马尔科夫法预测未来公司的市场占有率,并运用敏感性分析搜寻盈利的关键因素。结合实例计算分析,证明该方法的衫性与有效性。     

基于大地坐标的多舰协同超视距目标指示研究

针对超视距目标指示问题,建立了基于大地坐标的目标运动方程和观测方程,并将UKF滤波方法运用于目标的跟踪.由多艘舰艇组成一个协同的目标指示系统,并将一种新的分布式融合算法运用于该系统,提高了目标的跟踪精度.仿真结果验证了该算法的有效性.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.     

基于神经网络的公交车辆行程时间预测模型研究

城市公交丰辆行程时间预测是公共交通信息服务和运营调度的重要内容,要求较高的实时性和准确性。本文以智能交通运输系统为背景,通过分析公交车辆的行驶特性,基于改进的神经网络模型,建立了公交车辆动态行程时间预测模型,并对比了三种不同输入变量方案的神经网络预测模型,表明该模型具有良好的适用性。此外,将该方法与卡尔曼滤波法的行程时间预测模型进行比较,结果表明,基于神经网络的动态行程时间预测模型精确度较高。     

渡口河特大桥的施工监控

结合渡口河特大桥施工控制实践,阐述了悬臂浇筑施工过程中的施工监控原理和方法,以及有限元计算、线形监测、应变测量等方面的技术问题,研究了箱梁的线形预测控制方法和混凝土应变分布特性,科学地指导了施工。施工数据表明该特大桥的施工监控方法和计算方法的有效性以及合理性。     

小流量下短时交通量预测最佳窗口长度与时间间隔

探讨了小流量情况下，路段短时交通量预测中的可变时间间隔及预测窗口长度对预测精度的影响，分析了不同间隔序列在反映交通流特性方面的差别和最适合的预测窗口长度，并建立神经网络模型对预测效果进行了定量比较，得到序列用于预测的最佳窗口长度和一组最优时间间隔，有助于预测算法的改进和预测精度的提高。     

基于BP神经网络的厦门港集装箱吞吐量预测研究

厦门港是我国东南沿海的集装运输基本港口.集装箱吞吐量的预测是港口制定发展规划的重要依据.在研究了常用的输入数据归一化方法之后,提出了新的归一化方法.该方法能加快BP神经网络的训练速度并提高精度.运用BP神经网络建立了厦门港集装箱吞吐量预测模型,并计算出2020至2024的集装箱吞吐量预测值.无论从拟合值,还是预测值检验来看,该方法都具有很高精度.     

内贸海轮进出长江驾引力量需求预测研究

针对目前长江内贸船舶压港、滞港现象,运用灰色系统预测GM(1,1)模型对长江内贸船舶进出港艘次进行预测,进而求得未来几年内贸驾引力量需求数量。结果表明,未来几年长江内贸驾引力量需求数量巨大,驾引力量短缺严重。     

Modeling railway disruption lengths with Copula Bayesian Networks

Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice.     

隧道变形预测的灰色与回归模型对比分析

模型预测法是目前常用的隧道围岩变形预测的方法之一。文章结合广梧高速公路茶林顶隧道工程实例,建立GM（1,1）灰色模型、GM（2,1）灰色模型和双曲函数回归模型分别对隧道围岩变形进行预测,并对各模型的预测情况进行对比分析。结果表明,不论是从短期还是从长期看,GM（1,1）灰色模型都体现了优越的模拟和预测效果,且建立预测模型时不需要大量的统计数据,可应用于工程实际。