中国铁路提速后技术进步对产出贡献的测算

选取我国铁路提速后1997~2007年间的相关数据,采用基于科布——道格拉斯生产函数的Solow余值法,将有限分布滞后模型和半参数回归模型引入计算模型,对数据进行拟合,计算得到铁路提速后技术进步、固定资本投入以及人力资本投入对产出的贡献率,并根据计算结果提出促进我国高速铁路建设的相关对策建议.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

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.     

奇异积分处理技术在低频声散射数值计算中的应用

针对散射声场的计算问题，将奇异积分处理技术应用于目标的低频声散射的数值计算、理论验模与仿真试验。若果表明，ka＜3时，新方法的仿真结果与解析值以及等价面元近似方法取得了较好的一致；而ka≥3时，新方法的仿真结耙要好于等价面元近似方法。     

语料记忆在英语教学中的价值辩护与实施策略

外语学习离不开记忆，记忆的形式有多种多样。本文从多角度为语料记忆作价值辩护，阐述它的实施策略及要注意的问题，旨在说明语料记忆是在外语学习环境下提高英语学习效率的重要途径。     

试论交通运输行业核心价值体系建设的重要意义与时代内涵

建设交通运输行业核心价值体系具有重大的现实意义.交通运输行业核心价值体系的时代内涵可以概括为“献身交通、服务大众、四海为家、扎根基层、吃苦实干和默默奉献”.建设交通运输行业核心价值体系,是一项复杂的系统工程和长期的战略任务.     

关于一类二阶共轭矩阵方程的解

考虑一类二阶共轭矩阵方程 B*X*AXB+B*X*C+C*XB+D=O 其中，B∈Cp×m，A∈n×n，C∈Cn×m，D∈Cm×m，在A＞O及A≥O，C或B为列满秩阵两种情况 下，此方程可解的充分必要条件，并在有解的情况下，给出了通解的显式表示。     

道路交通统计生命价值测算模型及其验证

基于参数服从SB分布的混合Logit 模型进行道路交通统计生命价值的测算研究. 首先，结合意愿选择法和正交实验法设计出行路径选择调查问卷；然后，基于死亡风险系数服从对数正态分布和SB分布的混合Logit 模型，构建统计生命价值测算模型；接着以大连市私家车出行者为调查对象获得调查数据，并利用Monte Carlo 仿真方法进行模型参数标定；最后，对模型进行比较分析，并获得统计生命价值的测算值. 研究结果表明：死亡风险参数服从限制域为(0.0, 0.5)SB分布的混合Logit 模型，精确性更高且更合理；道路交通统计生命价值测算值为 105.76万元，这一结果可以作为道路交通安全项目经济评价的参考数据.     

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

This paper shows the relationship between flow, generalized origin–destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra‐like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. Copyright © 2012 John Wiley & Sons, Ltd.     

城市交通能耗区的识别及等级划分方法研究

随着社会经济的发展，机动车保有量急剧增长，城市化过程中交通拥堵问题日趋严重，交通能源消耗加剧。为了降低由此带来的损失，需要及时探明路网中交通效率低、能耗高的区域，并采取有效的交通管理措施。通过研究交通状态与能耗的关系，进行相应的交通参数分析，建立能耗区识别及等级划分模型，计算得到3个等级能耗区的界定阈值分别为0．411和0．678。     

奇摄动三阶非线性边值问题

利用微分不等式技巧和Volterra型积分算子，研究了三阶非线性奇摄动边值问题εx^(3)=f(t,x,x′,w(ε）x^(3)，ε），g（x′（0），x″（0）=0，x（1）=A，x′（1）=B的存在性及渐近估计。