站间距离较短的双接近区段设计方案探讨

针对提速半自动闭塞区段站间距离较短时,双接近重叠区段载频的选用、编码条件的设计等,进行了详细的介绍和探讨。     

数量折扣条件下的动态订货批量优化

为研究卖方提供数量折扣安排时，买方优化动态订货批量的决策问题，考虑多种产品、多折扣类型和买卖双方的能力约束，建立了该问题统一的非线性混合整数规划模型．利用禁忌搜索技术设计启发式算法对模型求解．算例的计算结果验证了模型和算法的有效性。     

移动平均法在公交运行时间预测中的应用

运用概率论与数理统计中的移动平均法,本文对公交车运行时间问题进行了研究。利用移动平均法的基本原理和检验方法,结合采集到的长春市公交306路的运行时间数据进行了实证研究,结果表明该方法可以用于公交车运行时间的预测。     

用LMI方法设计不确定关联时滞大系统H∞容错控制器

针对关联项具有N×N个任意未知常时滞的不确定线性连续大系统,在执行器失效的情况下,提出了一种分散的鲁棒H∞容错控制器的设计方法,通过假设可能失效执行器的输出信号为能量有界的干扰输入信号,不确定关联时滞大系统的容错控制问题转化为分散的鲁棒H∞控制问题.控制器可以通过解线性矩阵不等式给出,因而具有数值易解性.最后用一个数值例子验证了该设计方法的有效性.     

基于最早预达时刻的进近排序模型及算法

为解决空中交通流量的高速增长造成了进近冲突，合理安排飞机着陆顺序，充分利用跑道容量，对现有排序模型进行简化．讨论了相邻飞机之间安全间隔的标准．通过分析相邻飞机速度对安全间隔的影响、飞机预计到达时刻、飞机优先权，提出了基于最早预达时刻的排序模型及算法．算例表明，该算法计算量较小，排序结果优于传统排序算法．     

Development of a Secondary Crash Identification Algorithm and occurrence pattern determination in large scale multi-facility transportation network

Secondary crash (SC) occurrences are non-recurrent in nature and lead to significant increase in traffic delay and reduced safety. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes in order to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, their identification along with the primary contributing factors is imperative. The objective of this study is to develop a procedure to identify SCs using a static and a dynamic approach in a large-scale multimodal transportation networks. The static approach is based on pre-specified spatiotemporal thresholds while the dynamic approach is based on shockwave principles. A Secondary Crash Identification Algorithm (SCIA) was developed to identify SCs on networks. SCIA was applied on freeways using both the static and the dynamic approach while only static approach was used for arterials due to lack of disaggregated traffic flow data and signal-timing information. SCIA was validated by comparison to observed data with acceptable results from the regression analysis. SCIA was applied in the State of Tennessee and results showed that the dynamic approach can identify SCs with better accuracy and consistency. The methodological framework and processes proposed in this paper can be used by agencies for SC identification on networks with minimal data requirements and acceptable computational time.     

Assessing greenhouse gas emissions from port vessel operations at the Port of Incheon

This paper measures greenhouse gas emissions from port vessel operations by considering the case of Korea’s Port of Incheon. It provides estimates of greenhouse gas emissions based on the type and the movement of a vessel from the moment of its arrival, to its docking, cargo handling, and departure. Taking a bottom-up approach based on individual vessels’ characteristics and using data on vessels processed by the port in 2012 estimate emissions. The results indicate that the level of emissions is five times higher than that estimated through the top-down approach. Among various types of vessels, international car ferries are the heaviest emitters, followed by full container vessels and car carriers. A vessel’s passage through lock gates and maneuver to approach the dock accounts for 96% of its emissions. Docking for cargo handling shows the lowest level of GHG emissions.     

The Application of Communicative Approach in College English Teaching

Ⅰ.Introduction Thanks to the reform and opening- up policies, more and more op- portunities have been created for Chinese to communicate with people from all over the world, especially from the English- speaking countries, and as a result, the demand for oral proficiency of English has been greatly increased. However, our English teaching does not seem to follow this trend well, especially in high schools. Pressured by the College Entrance Ex- amination, high school English teachers still …     

山区河流船闸引航道布置型式

介绍山区河流的特点及船闸布置存在的问题,提出适应山区河流特点的船闸引航道布置型式。     

Cluster-based correlation of severe driving events with time and location

In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.