共查询到19条相似文献,搜索用时 93 毫秒
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文章在传统的灰色模型和马尔柯夫模型的基础上,提出了动态无偏灰色马尔柯夫模型,阐述了该模型的建立方法,并采用这三种模型对我国铁路客运量进行了预测,对比结果表明动态无偏灰色马尔柯夫模型的拟合效果较好,预测精度较高,是一种行之有效的预测方法。 相似文献
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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。 相似文献
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原油结蜡是影响管道安全、经济和高效运行的一个重要因素。为了对输油管道的结蜡状况进行预测,掌握输油管道结蜡的基本规律,应用灰色系统理论中的模型对输油管道结蜡速度和结蜡厚度等指标的实际统计数据进行了灰色动态拟合,建立了相应的灰色微分方程和时间响应函数。结果表明:残差小于2%,模型精度满足工程实际需要。 相似文献
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结合广西省南友高速公路养护成本的分析,建立基于灰色理论的GM(1,1)养护成本预测模型,并通过后残差检验分析了预测精度等级,结果表明此模型结果可靠。 相似文献
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详细介绍了灰色模型的原理和特点,根据交通事故的发生特点,探讨了灰色模型在道路交通事故预测中的具体应用,并利用此模型对青岛市某地区的交通事故进行预测,建立了灰色预测模型,根据实际事故数据与预测值进行了比较,灰色预测模型的精度比较好. 相似文献
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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。 相似文献
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Justin D.K. Bishop Colin J. Axon Malcolm D. McCulloch 《Transportation Research Part D: Transport and Environment》2012,17(5):389-397
This paper develops a robust, data-driven Markov Chain method to capture real-world behaviour in a driving cycle without deconstructing the raw velocity–time sequence. The accuracy of the driving cycles developed using this method was assessed on nine metrics as a function of the number of velocity states, driving cycle length and number of Markov repetitions. The road grade was introduced using vehicle specific power and a velocity penalty. The method was demonstrated on a corpus of 1180 km from a trial of electric scooters. The accuracies of the candidate driving cycles depended most strongly on the number of Markov repetitions. The best driving cycle used 135 velocity modes, was 500 s and captured the corpus behaviour to within 5% after 1,000,000 Markov repetitions. In general, the best driving cycle reproduced the corpus behaviour better when road grade was included. 相似文献
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介绍了埋地燃气管道外防腐层老化状况评价标准,以沥青外防腐层为例,通过应用马尔可夫链理论,阐述了基于逆阵的外防腐层老化状况预测的方法,从而为确定整条管线外防腐层老化状况分布提供了理论依据。 相似文献
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This paper proposes a conceptual framework to model the travel mode searching and switching dynamics. The proposed approach is structurally different from existing mode choice models in the way that a non-homogeneous hidden Markov model (HMM) has been constructed and estimated to model the dynamic mode srching process. In the proposed model, each hidden state represents the latent modal preference of each traveler. The empirical application suggests that the states can be interpreted as car loving and carpool/transit loving, respectively. At each time period, transitions between the states are functions of time-varying covariates such as travel time and travel cost of the habitual modes. The level-of-service (LOS) changes are believed to have an enduring impact by shifting travelers to a different state. While longitudinal data is not readily available, the paper develops an easy-to-implement memory-recall survey to collect required process data for the empirical estimation. Bayesian estimation and Markov chain Monte Carlo method have been applied to implement full Bayesian inference. As demonstrated in the paper, the estimated HMM is reasonably sensitive to mode-specific LOS changes and can capture individual and system dynamics. Once applied with travel demand and/or traffic simulation models, the proposed model can describe time-dependent multimodal behavior responses to various planning/policy stimuli. 相似文献
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There is significant current interest in the development of models to describe the day-to-day evolution of traffic flows over a network. We consider the problem of statistical inference for such models based on daily observations of traffic counts on a subset of network links. Like other inference problems for network-based models, the critical difficulty lies in the underdetermined nature of the linear system of equations that relates link flows to the latent path flows. In particular, Bayesian inference implemented using Markov chain Monte Carlo methods requires that we sample from the set of route flows consistent with the observed link flows, but enumeration of this set is usually computationally infeasible.We show how two existing conditional route flow samplers can be adapted and extended for use with day-to-day dynamic traffic. The first sampler employs an iterative route-by-route acceptance–rejection algorithm for path flows, while the second employs a simple Markov model for traveller behaviour to generate candidate entire route flow patterns when the network has a tree structure. We illustrate the application of these methods for estimation of parameters that describe traveller behaviour based on daily link count data alone. 相似文献
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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。 相似文献
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Micro-simulation travel demand and land use models require a synthetic population, which consists of a set of agents characterized by demographic and socio-economic attributes. Two main families of population synthesis techniques can be distinguished: (a) fitting methods (iterative proportional fitting, updating) and (b) combinatorial optimization methods. During the last few years, a third outperforming family of population synthesis procedures has emerged, i.e., Markov process-based methods such as Monte Carlo Markov Chain (MCMC) simulations. In this paper, an extended Hidden Markov Model (HMM)-based approach is presented, which can serve as a better alternative than the existing methods. The approach is characterized by a great flexibility and efficiency in terms of data preparation and model training. The HMM is able to reproduce the structural configuration of a given population from an unlimited number of micro-samples and a marginal distribution. Only one marginal distribution of the considered population can be used as a boundary condition to “guide” the synthesis of the whole population. Model training and testing are performed using the Survey on the Workforce of 2013 and the Belgian National Household Travel Survey of 2010. Results indicate that the HMM method captures the complete heterogeneity of the micro-data contrary to standard fitting approaches. The method provides accurate results as it is able to reproduce the marginal distributions and their corresponding multivariate joint distributions with an acceptable error rate (i.e., SRSME=0.54 for 6 synthesized attributes). Furthermore, the HMM outperforms IPF for small sample sizes, even though the amount of input data is less than that for IPF. Finally, simulations show that the HMM can merge information provided by multiple data sources to allow good population estimates. 相似文献