半苜蓿叶形互通式立交的应用探讨

有关互通式立交选型的文章在交通行业的刊物上刊载较多,大量统计表明:互通式立交采用单喇叭形占绝大多数,而采用半苜蓿叶形相对较少。针对半苜蓿叶形互通式立交的应用,做浅显的分析、探讨,以期与相关技术人员共勉。     

钩打系统远程管理软件的研究与开发

钩计划传输打印控制系统是编组站综合自动化系统的一个重要组成部分，其主要功能是实现钩计划及相关信息在异型网络上的传输。着重分析的是钩计划传输打印控制系统远程管理软件。此软件按其实现功能主要包含系统IP地址设置和探测、逻辑端口和物理端口的映射管理、各端口独立的打印机配置、初始化参数4大模块。在此对4大功能模块进行详细的分析和说明。     

关于沿海组团式城市公路客运站场规划的探讨——以中山市公路客运站场规划为例

从沿海组团式城市的交通特点出发，探究适合沿海组团式城市的公路客运站场规划的方法，并提出沿海组团式城市公路客运站场规划的原则和规划步骤，同时对中山市公路客运站场进行规划，结果表明效果很理想。     

人工挖孔灌注桩的施工方案

对人工挖孔灌注桩的施工方案进行了详细介绍.     

铁道车辆板梁结构多方案对比分析

为合理处理铁道车辆车体上的板梁组合结构,解决车体结构分析中典型的板梁偏心连接问题,构建了偏心节点的节点位移关系式,利用APDL语言实现了批量约束方程的施加;并根据ANSYS软件中梁单元、板单元、实体单元的基本特征构造了5种板梁组合结构模型,对它们进行了有限元分析及对比.研究结果表明:对于同一典型的板梁结构,用板梁偏心组合建模(单点约束)方案得到的模型与用实体建模方案得到的模型有限元分析结果比较接近,用全板壳建模的两种方案均相对实体单元模型约束稍强,而用板梁偏心组合建模(双点约束)方案得到的模型则约束过强;同时采用板梁组合建模的模型单元数和节点数相对较少,可以节省计算机时,降低计算费用.     

基于智能信号的动态路径选择模型研究

出行车辆的最优路径是随着时间和路况的变化而变化的，为了研究出行车辆的动态路径选择行为，基于有交通信号控制的交叉口，利用有向图理论，建立城市交通网络路径的优化模型，用传统dijkstra算法进行优化，动态获得出行车辆的最优路径。最后引入简单实例说明研究的方法，计算机仿真结果表明该方法是可行、有效的。     

计算机联锁仿真试验自动执行平台研究

介绍车站计算机联锁仿真试验通过计算机技术模拟人工操作,实现仿真试验自动执行平台的方案。通过分析当前计算机联锁仿真试验的现状,提出采用自动执行平台的解决方案,从平台任务设计、功能接口、执行流程等主要方面论述平台的方案实现,最后通过平台初版的示例站应用验证方案的效果,分析平台研究的下一阶段目标及未来价值。     

Real-time bus route state forecasting using particle filter and mesoscopic modeling

In the absence of system control strategies, it is common to observe bus bunching in transit operations. A transit operator would benefit from an accurate forecast of bus operations in order to control the system before it becomes too disrupted to be restored to a stable condition. To accomplish this, we present a general bus prediction framework. This framework relies on a stochastic and event-based bus operation model that provides sets of possible bus trajectories based on the observation of current bus positions, available via global positioning system (GPS) data. The median of the set of possible trajectories, called a particle, is used as the prediction. In particular, this enables the anticipation of irregularities between buses. Several bus models are proposed depending on the dwell and inter-stop running time representations. These models are calibrated and applied to a real case study thanks to the high quality data provided by TriMet (the Portland, Oregon, USA transit district). Predictions are finally evaluated by an a posteriori comparison with the real trajectories. The results highlight that only bus models accounting for the bus load can provide valid forecasts of a bus route over a large prediction horizon, especially for headway variations. Accounting for traffic signal timings and actual traffic flows does not significantly improves the prediction. Such a framework paves the way for further development of refined dynamic control strategies for bus operations.     

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.     

阳安铁路增建第二线山区地质选线

近年来随着我国山区铁路的大量修建,山区铁路地质选线的重要性日益凸显。阳安铁路是典型的山区铁路增建第二线项目,该项目进行地质选线对于降低后期勘察设计难度、减少投资、降低后期维护费用等方面起决定性作用。针对既有河谷线地段,增建第二线线路穿越区域性大断裂等工程地质问题,采取何种地质选线的技术原则进行研究。研究结论:(1)本次地质选线过程中,综合采用遥感解译、地质调绘、钻探、试验等勘探方法,查清沿线的工程地质条件;(2)对线位比选起决定性作用的工程地质条件主要是断层、滑坡等不良地质,线路通过断层应以大角度、简单工程通过,其余不良地质应以绕避为主;(3)在充分总结汲取山区铁路综合选线技术经验的基础上,提出地质选线、定线原则;(4)通过对阳平关至响水、西乡至石泉、石泉至池河三段线路方案的比选,得出推荐方案。