集装箱班轮到港规律的耦合性特征及其应用研究

指出了集装箱班轮运行方式下船舶到港规律具有耦合性特征,建立了耦合度表达式和相应的爱尔朗分布族模型。通过对大连港集装箱码头连续3年1.56万个班轮到港记录的统计分析,证明了同一航线同一公司编组船队内部各船舶之间在到港规律上具有很强的耦合性。     

接发列车作业与调度指挥模拟演练系统的构建

在铁路车站接发列车作业与列车运行调度指挥模拟演练系统功能设计的基础上,提出系统软件和硬件构成的实施方案.计算机技术和部分现场设备模拟铁路行车作业过程,为铁路运输专业学生实践教学提供接发列车、调度指挥模拟教学和仿真演练的现代化教学手段.     

盾构到达突发涌水处理及水中到达方案分析

盾构到达时易出现涌水、涌砂、上覆土体坍塌等事故,是盾构施工的重大风险点之一.通过盾构到达突发涌水事故解决实例,介绍事故发生后采用钢筋昆凝土接收洞室进行盾构水中到达的施工经验.钢筋混凝土接收洞室基于平衡到达理念,采用密闭的箱型混凝土结构与隧道洞门密闭连接,并在箱体内灌入填充物,以达到与土层压力平衡,模拟盾构在土体中的掘进状态,使盾构安全进入接收洞室内.     

两相位协调控制交叉口延误计算

延误是判断信号配时是否最优以及评价城市交叉口拥挤状况的重要指标之一。和多数文献只是研究孤立交叉口的延误计算不同,主要研究两相位协调控制交叉口,该交叉口由主、次干道相交而成,主干道处于协调控制,进口车流分为未饱和与过饱和2种状态;次干道进口车流只考虑未饱和状态,在此基础上分别对交叉口各进口道在一个周期内的延误计算公式进行推导和分析,并用具体的算例说明了公式的应用。不仅为协调控制交叉口拥挤状态的判别提供了依据,同时也对不同类型交叉口的延误计算给予了有益的补充。     

一种新的公交乘客上车站点确定方法

公交IC卡是一种新的客流信息采集手段,它的广泛使用为公交管理者进行客流分析提供了新的数据源。充分利用公交IC卡刷卡时间信息,根据相邻两站点间的最短刷卡间隔,通过系统聚类法对IC卡刷卡数据按照时间顺序进行聚类分析。在此基础上,将聚类分析的结果与公交运营资料所推算出的车辆到站时间进行时间匹配,确定持卡乘客的上车站点。最后,通过实例验证了该方法的可行性与实用性。     

基于改进MUSIC算法的矢量水听器阵列波达方向估计

介绍了改进的MUSIC算法在矢量水听器阵波达方向估计中的应用,并描述了该算法的原理及应用过程.计算机仿真的结果表明,改进的MUSIC算法既能够有效地估计出独立信号源的DOA,也能有效地估计出相关信号源和相隔比较近的小角度差信号源的DOA,从而证明了这种改进MUSIC算法的合理性.     

基于JADE算法的矢量水听器阵列信号盲估计研究

标量声压水听器线阵无法估计信号的二维到达角（方位角和俯仰角）,并且对来波方向相同的信号无法分辨.针对这一情况,文中引入盲信号处理理论,将JADE（特征矩阵联合近似对角化）算法与矢量水听器阵列技术相结合,提出了一种新的阵列信号盲估计算法.该算法应用在结构简单的矢量水听器线阵上,在信号方向矢量没有任何先验知识情况下,对阵列流型矩阵进行盲估计,进而估计出源信号的频率和二维到达角,并实现对来波方向相同信号的辨识.仿真实验证明了算法的有效性.     

考虑车辆随机到站时间的动态需求响应型接驳公交线路优化

车辆到站时间的不准时性严重影响着需求响应型公交的服务水平和乘客选择公共交通的出行意愿,因此,本文对考虑车辆随机到站时间的动态需求响应型接驳公交线路优化问题进行研究。以运营商成本、乘客乘车时间成本、乘客等待时间成本组成的系统总成本最小为目标建立数学模型,通过优化车辆路径寻求系统总成本最优的需求响应型接驳公交服务方案,其创新之处在于,在服务过程中允许乘客提交实时出行需求;定义车辆到站时间服从已知分布以描述其随机性。提出一种遗传算法和邻域搜索相结合的启发式算法对模型进行求解,该算法融合了遗传算法的全局搜索优势和邻域搜索的局部搜索能力,通过算例测试分析对本文算法的有效性及先进性进行验证。最后,基于西安市延平门地铁站设计数值实验,结果表明,考虑车辆随机到站时间可以在一定程度上减少乘客时间成本和系统总成本。     

Optimizing coordinated transfer with probabilistic vehicle arrivals and passengers' walking time

Supporting efficient connections by synchronizing vehicle arrival time and passengers' walking time at a transfer hub may significantly improve service quality, stimulate demand, and increase productivity. However, vehicle travel times and walking times in urban settings often varies spatially and temporally due to a variety of factors. Nevertheless, the reservation of slack time and/or the justification of vehicle arrival time at the hub may substantially increase the success of transfer coordination. To this end, this paper develops a model that considers probabilistic vehicle arrivals and passengers walking speeds so that the slack time and the scheduled bus arrival time can be optimized by minimizing the total system cost. A case study is conducted in which the developed model is applied to optimize the coordination of multiple bus routes connecting at a transfer station in Xi'an, China. The relationship between decision variables and model parameters, including the mean and the standard deviation of walking time, is explored. It was found that the joint impact of probabilistic vehicle arrivals and passengers' walking time significantly affects the efficiency of coordinated transfer. The established methodology can essentially be applied to any distribution of bus arrival and passenger walking time. Copyright © 2017 John Wiley & Sons, Ltd.     

Optimizing itineraries in public transportation with walks between rides

We study the problem of finding an optimal itinerary to travel from a starting location to a destination location using public transport, where we allow travelers to alternate rides with (short) walks. The main difference with previous research is that we take all possible walks that a traveler can make into consideration. This large number of possible walks poses a potential computational difficulty. However, in this paper we derive theorems for identifying a small subset of walks that only need to be considered. These results are embedded in a solution algorithm, which is tested in a real-life setting for bus transportation in a medium sized city. An extensive numerical study leads to encouraging results. First, only 1% of all possible walks needs to be considered, so that the optimal itinerary can be determined very efficiently. Second, allowing walks has considerable benefits; reducing the travel time in about 6% of all randomly generated examples by more than 10% on average.