灰色模型在原油管道蜡沉积速率预测中的应用

为了对原油管道内蜡沉积速率进行准确预测,基于灰色GM(1,N)模型理论和建模思路,综合考虑了管壁处剪切应力、管壁处温度梯度、管壁处蜡分子浓度梯度和原油的动力黏度4个影响因素,建立了 GM(1,5)蜡沉积速率模型,并与黄启玉蜡沉积模型进行模型精度对比.结果表明:GM(1,5)蜡沉积速率模型的平均相对误差为2.955 5％...     

基于组合模型的公路客运量预测研究

随着客运量与经济发展的联系越来越密切,如何更科学有效地对客运量进行预测变得越来越重要。文章分别介绍了灰色系统法、时间序列法、Elman神经网络及组合预测的方法原理,并以兰州市公路客运量为例,运用灰色模型、时间序列模型和Elman神经网络模型的组合模型进行客流预测,验证了该组合模型的合理性。     

基于非等间距GM(1,1)模型群对深井巷道围岩变形的预测

针对时间间距不同的深井巷道围岩变形问题,文章建立了全数据非等间距GM(1,1)模型和不同维数的部分数据非等间距GM(1,1)模型,通过误差比较分析,得到了相对误差最小的模型为5维部分数据非等间距GM(1,1)模型,并对巷道顶底板和两帮位移量做出了预测。结果表明:在一定范围内,维数越小,非等间距GM(1,1)模型的平均相对误差越小,为预测巷道围岩变形提供了一种良好的方法。     

基于小波变换与卡尔曼滤波结合的GM(1,1)模型在高铁隧道沉降变形分析中的应用

高铁隧道的变形量较小,但受随机噪声的干扰较大,使得监测得到的沉降曲线不能反映实际的沉降情况。鉴于此,文章提出了基于小波变换与卡尔曼滤波相结合的RLG降噪方法,该方法既有小波变换的去相关作用和多分辨分析功能,又有卡尔曼滤波对未知信号的线性无偏最小方差估计的特点。采用GM(1,1)预测模型对降噪后的数据进行分析,得到的结论是:基于小波变换与卡尔曼滤波相结合的GM(1,1)模型的精度较基于卡尔曼滤波的GM(1,1)模型的精度高,可有效地运用于高铁隧道沉降分析中。     

天然气消费量的灰色模型预测

介绍了利用灰色理论预测天然气消费量的模型,包括GM(1,1)模型和动态等维灰数递补灰色预测模型,对模型进行了求解,介绍了模型精度的检验方法,并用实例加以验证,同时与实际消费量进行了对比.计算结果表明:此方法预测天然气消费量既简单又准确,有较好的适应性和较高的精度,对天然气输配管网的优化运行和统一调度管理具有重要的参考意义.     

灰色GM(1,1)模型在靖西天然气管道负荷预测中的应用

靖西天然气管道是重要输气干线,准确预测需求负荷变化情况,确保管道安全、平稳、高效供气意义重大。文中以灰色理论为基础,利用管道历年气量数据建立灰色预测的GM(1,1)模型,采用后验差检验对预测模型进行检验,并对该管道未来用气需求量进行预测。计算结果显示:灰色GM(1,1)模型预测结果与实际结果具有较好的一致性,精度能够满足实际应用的要求,预测结果对靖西管道运行管理具有一定的借鉴作用。     

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。     

优化GM（1,1）模型在连续刚构桥施工监控中的应用

文章结合优化GM（1,1）模型用于罗天乐大桥施工监控的工程实例,介绍了优化GM（1,1）模型的原理、计算方法及计算过程,并根据实际监控经验提出了建议。罗天乐大桥的具体施工监控实践表明,优化GM（1,1）模有着较高的预测精度,能较好地应用于大跨度连续刚构桥的施工监控。     

灰色GM（1，1）模型在管道腐蚀预测中的应用

随着运营时间的增加,输气管道必然受到不同程度的腐蚀,如果不及时维修或更换,一旦发生泄漏引起爆裂,将会造成极大的损失。为了对输气管道的腐蚀程度进行预测,掌握输气管道腐蚀的基本规律,运用GM（1,1）模型对输气管道的腐蚀速度和腐蚀深度的原始数据进行了灰色动态拟合,建立了相应的灰色微分方程和灰色时间响应函数,应用于四川某气田输气管道未来6年的腐蚀情况预测。计算结果表明：建立的管道腐蚀预测模型GM（1,1）模型,与实测数据相比,误差较小,具有一定的实用价值,能为管道运营者采取相应防腐措施提供可靠的依据。     

基于主成分分析法的公路客运量预测模型分析

文章基于主成分分析的基本理论与模型,采用SPSS软件,对影响四川省公路客运量的相关因素进行主成分分析,克服多重共线性的问题,构建出四川省公路客运量预测模型。根据预测结果显示,该模型具有较高的精度,适用于影响因素指标较为明确的短期客运量预测,能够满足四川省目前公路客运量预测的需要,对四川地区的公路旅客运输发展也有着一定的指导作用,具有一定的科学性与有效性。     

灰色理论在土石混填路基工后沉降预测中的应用

运用灰色理论建立了土石混填路基的非等时距预测模型,同时,为了预测土石混填路基工后沉降,建立了土石混填路基工后沉降的GM(2,1)模型。通过实例计算表明,GM(2,1)模型预测精度较GM(1,1)灰色模型高,在土石混填路基工后沉降预测中具有较大的实用价值。     

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.     

基于灰色模型及月度比例系数法的铁路客流预测方法

文章针对铁路客流变化的影响因素及特点,提出了基于灰色模型及月度比例系数法的铁路客流预测方法,并通过实例分析,证明了该方法预测误差小、精度高、计算简便、可操作性强,可为铁路车站客运计划的制定及日常客运工作组织提供准确、可靠的数据。     

Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in ?ilina, the Slovak Republic, as a case study     

Optimal assignment for check-in counters based on passenger arrival behaviour at an airport

Establishing how to utilize check-in counters at airport passenger terminals efficiently is a major concern facing airport operators and airlines. Inadequate terminal capacity and the inefficient utilization of facilities such as check-in counters are major factors causing congestion and delays at airport passenger terminals. However, such delays and congestion can be reduced by increasing the efficiency of check-in counter operations, based on an understanding of passengers' airport access behaviour. This paper presents an assignment model for check-in counter operations, based on passengers' airport arrival patterns. In setting up the model, passenger surveys are used to determine when passengers arrive at the airport terminals relative to their flight departure times. The model then uses passenger arrival distribution patterns to calculate the most appropriate number of check-in counters and the duration of time that each counter should be operated. This assignment model has been applied at the Seoul Gimpo International Airport in Korea. The model provides not only a practical system for the efficient operations of time-to-time check-in counter assignments, but also a valuable means of developing effective longer-term solutions to the problem of passenger terminal congestion and delays. It also offers airlines a means of operating check-in counters with greater cost effectiveness, thus leading to enhanced customer service.     

Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency

Abstract

This paper develops alternatively structured trip frequency/generation models, and investigates their forecast performance. The first model presented is the simple linear model with a discussion of its theoretical shortcomings. Models that address, in a progressive fashion, the underlying shortcomings of the linear model are then presented. These models are namely the truncated normal model, the Poisson model, the negative binomial model, and an ordered logit model. The modeling unit employed in the study is the individual. The models are assessed by how closely they are able to replicate trips produced by each individual in the dataset, and by each traffic zone. This assessment of performance in prediction is conducted on an estimation dataset collected in the Toronto Region in 1986, and on an independent dataset collected in the same geographic region, 10 years later, in 1996. The results show that, notwithstanding the simplicity of the simple linear model and its lack of an explicit underlying travel behavioral theory, it predicts travel in the base and forecast years with less error compared to any of the more complex models.     

原油管道结蜡预测模型研究

利用灰色GM(1,1)模型的全息信息特性,在不需要多因素分析的情况下,建立了输油管道的结蜡速度和结蜡厚度的灰色GM(1,1)模型,实现了部分信息情况下的原油管道结蜡预测。实际计算表明:该模型误差在±2%以内,完全满足工程实际需要。     

Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks

This study employs back-propagation neural networks (BPN) to improve the forecasting accuracy of air passenger and air cargo demand from Japan to Taiwan. The factors which influence air passenger and air cargo demand are identified, evaluated and analysed in detail. The results reveal that some factors influence both passenger and cargo demand, and the others only one of them. The forecasting accuracy of air passenger and air cargo demand has been improved efficiently by the proposed procedure to evaluate input variables. The established model improves dramatically the forecasting accuracy of air passenger demand with an extremely low mean absolute percentage error (MAPE) of 0.34% and 7.74% for air cargo demand.