Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

基于多Markov链的效果评估模型研究

基于单Markov链的扫雷作战效果评估模型是一个理论意义大于实际的模型,离实际的多Markov扫雷过程还有相当的距离.文中从正交分解的角度,指出Markov评估模型只能进行定性评估.提出了用于定性评估的多Markov模型,从"分解基"相关性的角度提出了多链模型结构选择的方式,实现了基于遗传算法的模型解法,计算结果表明:在引入先验知识后,能在小样本数据条件下取得较为准确的评估结果.     

过程:思想政治教育重要的中介概念

中介就是客观事物之间的联系环节。中介是客观的、普遍的。过程是思想政治教育重要的中介概念。思想政治教育是历史的、文化的过程;是运动的、发展的过程;是具体的、有步骤的过程。     

温储备可修系统在两种开关模式下的可靠性

对由n个同型部件和一个修理设备组成的温储备可修系统，在部件寿命、开关寿命和维修时间均服从指数分布，并且所有变量相互独立等条件下，建立开关失效系统立即失效和开关失效系统不立即失效两种模型，利用齐次马尔可夫过程确定系统的转移概率，并在n＝2时，求出系统的一系列可取性数量指标．最后论证两种模式在某些特殊条件下的结果，即在一定范围内推广了前人的模型．     

大型船舶操纵性能综合评价

选取 6种典型的大型船舶操纵性能试验数据 ,分析其影响因素 ,并运用 IMO有关船舶操纵性的衡准 ,确定综合评价指标 ,运用层次分析法对其操纵性能进行排序 ,然后对大型油船、散货船和集装箱船的操纵性能进行比较 ,总结出大型船舶的操纵特点 ,并得出对实际船舶操纵具有指导意义的结论     

集装箱码头集卡调度系统

集装箱集卡调度(TPS)系统通过动态实时地调度集卡,将装船、卸船和转堆等一系列需要集卡参与的多条作业路共享所有正在码头作业的集卡,使每辆集卡同时对应多条作业路,保证集卡在卸载后可就近投入其他需要的作业路中,以此缩短集卡的空载行驶时间和距离,实现大船作业边装边卸,在不增加集卡数量的基础上大幅度提高生产效率。     

软件Markov测试的可靠性评估

计算机软件可靠性估计是当今研究的重要问题之一。基于有限Markov链理论，形式化的描述了软件统计测试方法，提出了一种软件的可靠度估计及其近似置信下限估计方法，并分别对特殊的分支和序列两类软件系统进行了具体研究。     

基于马尔可夫过程的搜索力最优化模型研究

在引出马尔可夫搜索模型的基础上，探讨了一种有效的目标搜索方法，达到了优化搜索过程，提高搜索效率。应用最优搜索理论，导出了待搜目标服从均匀分布，正态分布时搜索力的最优配置预测分析，给出搜索力分配的新数学模型及数学规划解析。上述理论和方法为搜索力如何实施最优化搜索提供了一定的理论依据。