基于节点重要度理论的轨道交通线路建设时序

根据城市近期建设线网规模,结合线网分级理论,从轨道交通线网规划中选择适当的线路进行建设;利用排序理论,安排近期的线路建设计划。以石家庄市作为实例对方法进行验证,并与实际建设方案进行对比。     

基于PSO-BP神经网络的船体分段任务包工时定额模型

为解决船体分段任务包工时定额的计算过度依赖线性公式而忽略工时定额与工艺参数之间的非线性关系的问题,提高工时定额计算的效率和精确度,将PSO-BP神经网络技术应用到船体分段任务包工时定额中。通过对影响船体分段中间产品额定工时的工艺参数进行分析,建立多输入单输出的PSO-BP神经网络模型,并应用实际数据对PSO-BP神经网络进行训练,测试仿真结果与实际值之间的误差在允许范围内。验证结果表明,采用PSO-BP神经网络建立船体分段任务包工时定额模型,能对任务包作业工时进行准确预测。     

基于组合预测模型的铁路货运量预测研究

为了进一步提高铁路货运量的预测精度,提出基于乘积季节模型与引入注意力机制(Attention Mechanism)的长短期记忆(Long Short-Term Memory)模型的组合预测模型。首先建立乘积季节模型、LSTM模型与引入注意力机制的LSTM模型,然后利用误差修正法分别将2种LSTM模型与乘积季节模型组合起来进行预测,最后将预测结果分别与单一模型进行对比。采用2005年至2018年全国铁路月度货运量进行预测分析,结果表明2种组合预测模型的预测精度均高于单一预测模型的预测精度,其中基于乘积季节模型与引入注意力机制的LSTM模型的组合预测模型精度最高,具有研究和实用价值。     

施工场景下基于YOLOv3的安全帽佩戴状态检测

针对现有安全帽检测研究中采用的两阶段检测法存在检测效率偏低,累积误差对精度影响较大的问题,提出一种对安全帽的单阶段检测法。将安全帽和工人头部视为一个整体,将检测目标分为2类,即佩戴安全帽的头部和未佩戴安全帽的头部,同时对2类目标进行检测,避免了冗余的计算步骤及累积误差的影响。同时,针对施工场景安全帽佩戴状态检测特点,对YOLOv3的网络结构、损失函数及先验框尺寸进行改进,提出YOLOv3-C模型。研究结果表明:改进后的YOLOv3-C模型的检测性能大幅提升,在本文建立的样本集中模型的mAP达到93.84%,对安全帽检测平均精度达到97.01%,对工人头部检测平均精度达到90.67%,同时YOLOv3-C对本文的检测场景表现出良好的鲁棒性。     

某地铁列车无人自动折返卡顿故障分析及解决方案

针对某城市地铁车辆在试运行期间频繁发生的列车无人自动折返时的卡顿故障进行了分析研究。简要介绍了列车无人自动折返功能,并从列车网络系统、牵引系统和车辆电路3个角度深入分析故障原因,提出了解决方案。按此方案整改后故障得以完全消除。     

大城市城市轨道交通线网规划关键技术探讨

目前,城市轨道交通线网规划的技术已相对成熟,指导了我国超大、特大城市的城市轨道交通建设。随着城市化进程的不断加快,人口在100万至500万的大城市已成为城市轨道交通规划和建设的重要参与者。大城市在空间、人口、经济、交通出行特征等方面与超大、特大城市存在较大的差异,使得目前城市轨道交通线网规划的技术对大城市的适用性存在一定的局限。通过分析大城市与城市轨道交通的互动关系,提出大城市城市轨道交通线网规划的需求。基于现有的城市轨道交通线网编制技术流程,结合大城市的需求,从前提研究、方案研究、实施规划三个层面,对城市轨道交通线网规划编制中服务范围分析、线网规模确定、近期建设方案制定等技术内容提出优化建议,以适应大城市城市轨道交通线网规划的编制需求。     

Transport network criticality metrics: a comparative analysis and a guideline for selection

ABSTRACT

Transport network criticality analysis aims at ranking transport infrastructure elements based on their contribution to the performance of the overall infrastructure network. Despite the wide variety of transport network criticality metrics, little guidance is available on selecting metrics that are fit for the specific purpose of a study. To address this gap, this study reviews, evaluates and compares seventeen criticality metrics. First, we conceptually evaluate these metrics in terms of the functionality of the transport system that the metrics try to represent (either maintaining connectivity, reducing travel cost, or improving accessibility), the underlying ethical principles (either utilitarianism or egalitarianism), and the spatial aggregation considered by the metrics (either network-wide or localised). Next, we empirically compare the metrics by calculating them for eight transport networks. We define the empirical similarity between two metrics as the degree to which they yield similar rankings of infrastructure elements. Pairs of metrics that have high empirical similarity highlight the same set of transport infrastructure elements as critical. We find that empirical similarity is partly dependent on the network’s topology. We also observe that metrics that are conceptually similar do not necessarily have high empirical similarity. Based on the insights from the conceptual and empirical comparison, we propose a five-step guideline for transport authorities and analysts to identify the set of criticality metrics to use which best aligns with the nature of their policy questions.     

FXD3-J型动力集中动车组动力车网络控制系统

文章详细介绍了FXD3-J型动力集中动车组动力车网络控制系统的结构与组成,对网络设备、系统控制功能、显示界面、冗余特性等进行了阐述。运用考核试验表明,该网络控制系统性能稳定、可靠。     

基于全以太网的列车网络控制系统在美国地铁列车上的应用

从列车网络控制系统的拓扑结构、通信协议架构、冗余措施及功能描述等几个方面对基于全以太网的列车网络控制系统在美国地铁列车上的应用进行了详细分析与试验验证。在装车调试之前进行系统集成测试可尽早地发现列车网络控制系统或子系统在硬件和软件方面存在的各种问题,大大节省装车调试所需要的人力和时间。     

A new logit‐artificial neural network ensemble for mode choice modeling: a case study for border transport

Logit model is one of the statistical techniques commonly used for mode choice modeling, while artificial neural network (ANN) is a very popular type of artificial intelligence technique used for mode choice modeling. Ensemble learning has evolved to be very effective approach to enhance the performance for many applications through integration of different models. In spite of this advantage, the use of ANN‐based ensembles in mode choice modeling is under explored. The focus of this study is to investigate the use of aforementioned techniques for different number of transportation modes and predictor variables. This study proposes a logit‐ANN ensemble for mode choice modeling and investigates its efficiency in different situations. Travel between Khobar‐Dammam metropolitan area of Saudi Arabia and Kingdom of Bahrain is selected for mode choice modeling. The travel on this route can be performed mainly by air travel or private vehicle through King Fahd causeway. The results show that the proposed ensemble gives consistently better accuracies than single models for multinomial choice problems irrespective of number of input variables. Copyright © 2015 John Wiley & Sons, Ltd.