基于并联型灰色神经网络模型的港口吞吐量预测方法探讨

港口吞吐量预测是港口规划的基础,在确定港口发展方向、投资规模等方面发挥着十分重要的作用,因此有必要对港口吞吐量的发展趋势做出合理的预测。结合灰色理论和神经网络模型的特点,尝试用灰色神经网络组合模型之一——并联型灰色神经网络模型进行港口吞吐量预测。用实际算例证明了该方法在港口吞吐量预测中的有效性。     

CubeCargo软件应用于货运交通规划的研究

随着现代物流的发展，国际和国内的货运需求均呈现快速增长的形势，对传统货运交通规划工作提出了新的挑战。与客运交通规划相比，货运交通规划具有不少鲜明的特点，专门针对货运交通规划的分析工具也很少。为此，在分析了基于车辆和基于货物两类货运交通规划方法的流程和优缺点的基础上，提出基于货物的货运交通规划方法更有优势。通过示例，探讨了如何运用CubeCargo软件开展基于货物的货运交通规划，实现货运发生和吸引、货运分布、货运方式划分、货运交通分配的预测工作。     

Forecasting container throughput based on wavelet transforms within a decomposition-ensemble methodology: a case study of China

To improve predictive accuracy, new hybrid models are proposed for container throughput forecasting based on wavelet transforms and data characteristic analysis (DCA) within a decomposition-ensemble methodology. Because of the complexity and nonlinearity of the time series of container throughputs at ports, the methodology decomposes the original time series into several components, which are rather simpler sub-sequences. Consequently, difficult forecasting tasks are simplified into a number of relatively easier subtasks. In this way, the proposed hybrid models can improve the accuracy of forecasting significantly. In the methodology, four main steps are involved: data decomposition, component reconstruction based on the DCA, individual prediction for each reconstructed component, and ensemble prediction as the final output. An empirical analysis was conducted for illustration and verification purposes by using time series of container throughputs at three main ports in Bohai Rim, China. The results suggest that the proposed hybrid models are able to forecast better than do other benchmark models. Forecasting may facilitate effective real-time decision making for strategic management and policy drafting. Predictions of container throughput can help port managers make tactical and operational decisions, such as operations planning in ports, the scheduling of port equipment, and route optimization.     

基于出行方式链的城市轨道交通客流分析方法

传统"四阶段"法预测流程无法准确预测与轨道交通接驳的交通方式及换乘客流.借鉴基于活动的交通需求模型中利用出行链来解释人的出行行为的方法,提出了基于出行方式链的轨道交通客流分析方法.使用三层的Nested-Logit模型来模拟出行者选择轨道交通为主要出行方式接驳其他交通方式和预测各种方式的客流,建立了轨道交通出行方式链模...     

两种短时交通流混沌预测方法分析

短时交通流预测是智能交通系统的核心内容和交通信息服务、交通诱导的重要基础。采用符合交通流特性的混沌理论对短期交通流进行预测。在相空间重构和混沌识别的基础上,建立短期交通流加权一阶局域预测模型和基于最大Lyapunov指数的预测模型,并对一组实际的交通流数据进行预测。仿真结果表明:两种方法都能较准确的预测交通流,但最大Lyapunov指数预测模型的预测精度相对较高。     

黄土路堤沉降特性与工后沉降预测模型分析

阐述了黄土的分布与性质,对黄土路堤沉降的机理及原因进行了分析.分析比较了基于实测资料预测黄土路堤工后沉降值的实用模型,包括指数模型,幂函数模型,双曲线模型.以指数模型为例,根据已建成某大坝实测数据,确定了预测模型的参数.     

郑州市科学大道-西三环互通式立交工程方案选型设计探讨

从预测交通量分析出发,结合互通式立交功能、既有构造物等建设条件,对互通式立交型式进行方案综合比选,从而推荐出功能完善、与既有构造物衔接良好、造价较低的互通方案。     

船舶波浪载荷短期极值的经验公式

对散货船、油船、集装箱船的多艘实船进行了短期波浪载荷直接计算,分析了这三种船型的船舯垂向弯矩、L/4(L为船长)处垂向剪力、3L/4处垂向剪力的若干规律,以及集装箱船型的扭矩、船舯水平弯矩的若干规律.根据这些规律,总结出一套针对特定船型的短期波浪载荷极值的完整计算公式.     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

一种短时交通流组合预测模型

为提高短时交通流预测的精度,提出利用BP神经网络、RBF神经网络和ARIMA模型构建组合预测模型,该组合预测模型利用最优化原理进行权系数的分配,并且满足分配到的权值始终具有实际意义。通过对分配的权系数进行显著性检验,以确保组合预测模型中选用的单项预测方法显著相关。通过实例分析,验证了组合预测模型的有效性,结果表明,相比较单一的预测模型,组合预测模型具有更高的预测精度。