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ABSTRACTThis paper describes the development of railway station choice models suitable for defining probabilistic station catchments. These catchments can then be incorporated into the aggregate demand models typically used to forecast demand for new rail stations. Revealed preference passenger survey data obtained from the Welsh and Scottish Governments was used for model calibration. Techniques were developed to identify trip origins and destinations from incomplete address information and to automatically validate reported trips. A bespoke trip planner was used to derive mode-specific station access variables and train leg measures. The results from a number of multinomial logit and random parameter (mixed) logit models are presented and their predictive performance assessed. The models were found to have substantially superior predictive accuracy compared to the base model (which assumes the nearest station has a probability of one), indicating that their incorporation into passenger demand forecasting methods has the potential to significantly improve model predictive performance. 相似文献
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Within the literature concerned with aspects of competitiveness, innovation and strategic management of industrial clusters, the body regarding forecasting of strategic management is still nascent. This work aspires to render a contribution within the domain of strategic management forecasting, through the indicative case of European maritime clusters. For this end, a two-tier model is formulated. A quantitative strengths, weaknesses, opportunities, and threats (SWOT) methodology that derives from the fusion of a quondam situation analysis and crosstabs’ theory is generated. This leads to the utilization of the crosstab’s conditional probabilities as transition probabilities; the latter are called to compile the transition matrix of a Markov chain. Through this methodology, and the successive Markov chain’s transition matrices, strategic forecasts are extracted for a devised European maritime cluster case. This work relinquishes a novel application with respect to strategic management forecasting, that provides a dichotomy of practical interpretations and scenarios for quantitative situation analysis. Thus, it may enable effective real-time decision-making for strategic management and/or policy drafting. The situation analysis forecasting model may find applicability in a plethora of practical and theoretical cases, wherein forecasting may be desirable. It may as well pertain to an intrinsic methodology for situation analysis forecasting of maritime clusters. 相似文献
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为进一步保障浮式生产储油装置(FPSO)的作业安全,通过海洋环境条件测量、FPSO运动和单点系泊系统(SPM)状态监测以及理论计算等手段,实现了软刚臂单点系泊监测预报系统的方案设计,并成功应用于渤海油田。经现场应用验证,该系统能够实现对FPSO及SPM运行情况的监测,为作业者提供FPSO操作指导及预警,有效的降低了作业风险。 相似文献
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Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting. 相似文献
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重点探讨实际道路规划项目中一般收费道路的收费收入预测方法,目的在于尽可能使收费收入预测更为精确。在我国,高速公路通常采用依靠征收通行费以偿还投资借贷的方式进行建设,因此在规划的过程中需要对通行费收入进行预测,并进行财务分析,以保证决策者做出正确的投资决策。以笔者在日本从事的高速公路建设项目为例,分析研究了基于实用型交通量增量分配方法结合使用车种转换矩阵的高速公路路网中一般收费道路通行费收入的预测方法,并将其应用到实际道路项目财务评价中。结果表明通过控制收费道路出入口间交通量预测精度,以及采用需求相关性较大的车种转换调查数据可以提高通行费预测精度。研究表明这一方法具有很强的实用性,该方法对我国的高速公路需求预测有着借鉴作用。 相似文献
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基于非参数回归的快速路行程速度短期预测算法 总被引:1,自引:0,他引:1
基于北京市快速路上的检测器所采集的历史数据,经过数据筛选,剔除判别,小波滤噪平稳处理,聚类分析等过程,建立了交通状态演变系列的历史样本数据库。基于所构建的历史数据库,通过数值试验,确定了状态向量、距离匹配原则,K近邻值等参量,构建了一种基于K近邻的非参数回归短时交通预测模型,实现了对路段行程速度的短时预测。最后,利用随机选取的历史数据系列对预测模型的精度进行了检验。结果表明,预测算法的精度可以达到90%以上,可以很好地满足ITS应用系统对于交通预测数据的精度要求。 相似文献