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。     

改进不等时距GM模型在施工控制中的应用

刚构桥施工过程中应力变化复杂,影响因素多,较难预测把握应力发展趋势,实测应力缺失时,单一使用GM(1,1)模型预测精度低,对原始应力数据要求苛刻.文章改进了不等时距GM(1,1)模型,结合罗天乐特大桥工程进行验证,结果表明,改进不等时距GM(1,1)模型在刚构桥施工应力预测中具有较好的适用性和较高的预测精度.     

GM模型和Markov模型在公路客运量预测中的综合运用

文章在应用灰色理论构建的GM预测模型基础上,以Markov模型为修正方法,建立GM—Markov模型,并以陕西省2003—2012年公路客运量为基础数据对上述理论进行实例验证。结果表明：与实际客运量相比,GM模型的相对误差为11．08％,而GM—Markov模型的相对误差仅为5．61％,GM—Markov模型拟合精度较高,更加贴近实际情况。     

基于灰色马尔可夫理论的油气管道腐蚀剩余寿命预测

以灰色理论的标准GM（1,1）模型和马尔可夫TPM理论为基础,提出了基于灰色马尔可夫理论的油气管道腐蚀剩余寿命预测方法。利用灰色马尔可夫理论预测腐蚀油气管道剩余寿命的步骤主要包括：最大允许腐蚀深度的确定,腐蚀速率的预测以及剩余寿命预测。并基于该方法,采用VB系统开发了实用软件,简便可靠。该方法可以在腐蚀速率波动比较大的情况下预测油气管道的剩余寿命,为油气管道腐蚀检测周期的确定提供了科学依据。     

基于灰色理论的桥梁群桩基础工后沉降预测

文章基于灰色系统理论,应用其等时距GM（1,1）模型及改进的新陈代谢模型对桥梁群桩基础工后沉降进行预测,通过与蕴藻浜特大桥某墩的沉降观测资料的对比分析,提出了桥梁群桩基础工后沉降灰色理论预测方法。     

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。     

马尔柯夫灰色残差模型在剐构桥控制中的应用

文章结合罗天乐特大桥的马尔柯夫灰色残差模型预测实例,介绍马尔柯夫灰色残差模型在线性控制中的原理、计算及应用过程,并根据实际监控经验提出了建议。罗天乐特大桥的具体监控应用实践表明,马尔柯夫灰色残差模型预测精度高,能较好地应用于大跨连续刚构桥的施工线形控制。     

A computationally efficient two-stage method for short-term traffic prediction on urban roads

Abstract

Short-term traffic prediction plays an important role in intelligent transport systems. This paper presents a novel two-stage prediction structure using the technique of Singular Spectrum Analysis (SSA) as a data smoothing stage to improve the prediction accuracy. Moreover, a novel prediction method named Grey System Model (GM) is introduced to reduce the dependency on method training and parameter optimisation. To demonstrate the effects of these improvements, this paper compares the prediction accuracies of SSA and non-SSA model structures using both a GM and a more conventional Seasonal Auto-Regressive Integrated Moving Average (SARIMA) prediction model. These methods were calibrated and evaluated using traffic flow data from a corridor in Central London under both normal and incident traffic conditions. The prediction accuracy comparisons show that the SSA method as a data smoothing step before the application of machine learning or statistical prediction methods can improve the final traffic prediction accuracy. In addition, the results indicate that the relatively novel GM method outperforms SARIMA under both normal and incident traffic conditions on urban roads.     

Modeling traffic noise in a mountainous city using artificial neural networks and gradient correction

Different regions have established traffic noise prediction models to adapt to their particular environmental characteristics. This paper aimed to develop a traffic noise prediction model for mountainous cities. In China, the traffic noise prediction model HJ 2.4-2009, which itself is based on the sound pressure level corrected for roadway gradients (RGs), has been receiving widespread acceptance. On the basis of the model in HJ 2.4-2009, the RG correction coefficient was proposed to modify the original model and a per-vehicle noise prediction model was built using a multilayer feedforward artificial neural network (ANN) model. The data collected from a municipal road of a hilly city, Chongqing, was used to train and validate the ANN model. The predictor variables comprised the per-vehicle noise value, vehicle type, vehicle velocity, and roadway gradient. The results showed that the modified HJ 2.4-2009 model incorporating the gradient correction coefficient achieved a significantly higher R² for mountainous cities than the original model. Besides, the ANN-based noise prediction model achieved considerable accuracy improvement over the empirical predictive equations.     

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.     

Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction

We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.     

不等时距GM(1,1)模型在预测输气管道腐蚀中的应用

根据等时距GM(1,1)模型建立了不等时距GM(1,1)预测模型,该模型可应用于利用腐蚀指标的原始数据来预测以后的输气管道腐蚀情况。验证表明:不等时距灰色模型扩大了等时距灰色模型的应用范围,在小样本的情况下同样可以做出较准确预测,为输气管道的防腐提供了可靠的依据。     

运用灰色理论预测隧道变形的探讨

隧道变形监测对于隧道的安全有着重要的作用.运用灰色理论GM模型对其变形数据进行预测分析,发现灰色理论对隧道的变形有一定的预测效果,同时了解到不同样本数据其预测精度存在差异.为此,提出使用二次拟合参数法对其进行改进,得出二次拟合参数法对低精度的预测模型有一定的改进效果,而对于较高精度的预测模型效果并不显著.     

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。     

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。     

Integrated model for traffic flow forecasting under rainy conditions

Inclement weather, such as heavy rain, significantly affects road traffic flow operation, which may cause severe congestion in road networks in cities. This study investigates the effect of inclement weather, such as rain events, on traffic flow and proposes an integrated model for traffic flow parameter forecasting during such events. First, an analysis of historical observation data indicates that the forecasting error of traffic flow volume has a significant linear correlation with mean precipitation, and thus, forecasting accuracy can be considerably improved by applying this linear correlation to correct forecasting values. An integrated online precipitation‐correction model was proposed for traffic flow volume forecasting based on these findings. We preprocessed precipitation data transformation and used outlier detection techniques to improve the efficiency of the model. Finally, an integrated forecasting model was designed through data fusion methods based on the four basic forecasting models and the proposed online precipitation‐correction model. Results of the model validation with the field data set show that the designed model is better than the other models in terms of overall accuracy throughout the day and under precipitation. However, the designed model is not always ideal under heavy rain conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

滑动平均法在刚构桥施工应力预测中的应用

文章针对刚构桥施工过程中应力不确定性影响因素,介绍通过滑动平均法GM(1,1)模型对数据进行建模前预处理方法,并结合葫芦鼎大桥工程实例,验证了滑动平均法建立GM(1,1)模型在刚构桥施工应力预测中较好的适用性和较高的预测精度,也是对原始GM(1,1)模型的一种改进。     

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。     

Adaptive traffic control system: Control strategy,prediction, resolution,and accuracy

Recent advances in traffic control methods have led to flexible control strategies for use in an adaptive traffic control system (ATCS). ATCS aims at controlling the imminent traffic, which is yet to arrive and hence not known perfectly. Therefore, volume prediction is an essential part. Associated with the prediction are two aspects: resolution and accuracy. Recent studies indicate a tradeoff between prediction resolution and accuracy: finer resolutions, larger errors. It is imperative to study the relationship and tradeoff between the control strategy, prediction resolution, and its associated error, which are crucial to the development of ATCS. This study investigates this relationship through an extensive simulation of scenarios in Hong Kong with a recently developed dynamic traffic control model, DISCO. Based on the Hong Kong scenarios conducted with DISCO, the major findings include: (i) the importance of resolution outweighs that of error; (ii) dynamic timing plans generally outperform time‐invariant timing plans; (iii) up to a certain extent, overestimated predictions lead to better results than underestimated predictions.