Mixed manual/semi-automated traffic: a macroscopic analysis

The use of advanced technologies and intelligence in vehicles and infrastructure could make the current highway transportation system much more efficient. Semi-automated vehicles with the capability of automatically following a vehicle in front as long as it is in the same lane and in the vicinity of the forward looking ranging sensor are expected to be deployed in the near future. Their penetration into the current manual traffic will give rise to mixed manual/semi-automated traffic. In this paper, we analyze the fundamental flow–density curve for mixed traffic using flow–density curves for 100% manual and 100% semi-automated traffic. Assuming that semi-automated vehicles use a time headway smaller than today’s manual traffic average due to the use of sensors and actuators, we have shown using the flow–density diagram that the traffic flow rate will increase in mixed traffic. We have also shown that the flow–density curve for mixed traffic is restricted between the flow–density curves for 100% manual and 100% semi-automated traffic. We have presented in a graphical way that the presence of semi-automated vehicles in mixed traffic propagates a shock wave faster than in manual traffic. We have demonstrated that the presence of semi-automated vehicles does not change the total travel time of vehicles in mixed traffic. Though we observed that with 50% semi-automated vehicles a vehicle travels 10.6% more distance than a vehicle in manual traffic for the same time horizon and starting at approximately the same position, this increase is marginal and is within the modeling error. Lastly, we have shown that when shock waves on the highway produce stop-and-go traffic, the average delay experienced by vehicles at standstill is lower in mixed traffic than in manual traffic, while the average number of vehicles at standstill remains unchanged.     

船舶柴油机-调距桨自动控制推进系统实时仿真

本文论述了基于微型电子计算机(笔者使用TRS-80微型计算机)对船舶柴油机-调距桨联合自动控制系统进行实时仿真的方法。并用该实时仿真系统获得了船舶推迸系统的动态特性曲线。解决了这个多输入、多输出、非线性的复杂自动控制系统的动态特性求解问题。本文重点探讨建立该实时仿真系统的两个关键问题:(1)对象的非线性;(2)微型计算机速度慢与实时仿真要求速度快的矛盾。     

Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model

Providing reliable travel time prediction is very much needed for commuters for their upcoming trips to reduce travel time and relieve traffic congestion. This article proposes an integrated model for path and multi-step-ahead travel time prediction on freeways using both historical and real-time heterogeneous traffic and weather data. The model's performance is investigated in a case study under various traffic scenarios. Results indicate that the proposed model provides satisfactory prediction results in various performance tests. For practical purposes, general guidelines for selecting the model's parameter sets as well as the efficient size of historical data are also presented.     

深水刚性跨接管安装过程中关键因素分析

结合中国南海1500m水深海况,使用非线性时域分析软件Orcaflex建立“M”型深水刚性跨接管有限元模型。分析跨接管在波浪及海流作用下,三种典型安装工况：提升工况,通过飞溅区工况以及着陆工况中,不同浪向和绞车下放速度对跨接管强度的影响。并根据API RP 2RD规范校核不同安装工况中的牵引力和结构应力响应是否满足要求。结果表明：提升过程中,绞车需要控制适宜的提升速度以避免发生支撑框架结构碰撞;通过飞溅区过程中, 45°浪向角时跨接管的应力响应最小,为理想施工浪向角;着陆过程中,绞车牵引力达到最大值,数值仿真可以预报安装施工船所需的基本起重能力。分析结果对南海深水跨接管安装具有一定参考意义。     

带时间窗的动态车辆路径问题的局部搜索算法

为有效求解带时间窗的动态车辆路径问题,建立了该问题的数学模型,通过计划周期分片,将动态问题转换为一系列的静态子问题,采用插入法构造初始解,并将重定位法、节点交换法和2-opt*法3种线路间局部搜索方法,以及2-opt法和Or-opt法2种线路内局部搜索方法的不同组合应用于初始解的改进,分析了客户出现时间、地理位置分布与不同客户时间窗范围对线路选择的影响,比较了标准算例的求解结果。结果表明:在线路间进行局部搜索时,重定位法的效果最好,2-opt*法次之,节点交换法的最差;在线路内进行局部搜索时,2-opt法优于Or-opt法;当客户请求出现时间越早,客户比较集中,客户时间窗较宽的情况下,使用的车辆数量较少,整个线路的行驶距离较短,客户延迟时间也较短。     

50000 DWT半潜船压载系统设计

压载系统是半潜船的关键性系统,设计需要满足半潜船潜装、滚装和吊装等多种装卸货方式的要求。为此以中远航运50 000 DWT半潜船为例,介绍半潜船压载系统的设计要点。包括半潜船上浮时间的确定、半潜船常用排/压载方式的选择、大型排/压载设备的介绍、压载系统的设计和优化以及压载系统满足BWM公约D-2规则要求的研究等。     

FRP筋在自动化集装箱港区AGV重载道路中的应用

由于自动化集装箱港区AGV重载道路布设磁钉防磁的需要,混凝土面层中的传力杆采用了防磁性能良好的FRP筋。目前FRP筋尚无应用于道路的具体设计方法,借鉴传统钢制传力杆计算理论,通过建立有限元模型进行数值分析,并结合现场传荷系数试验结果,对相关设计方法的适用性进行验证并提出优化意见。     

舰载机作战任务要求到保障要求转换研究

定义任务周期内舰载机装备保障的相关参数,分析舰载机作战单元任务要求到保障要求相关参数之间的约束关系,建立作战任务到保障要求的转换模型。通过最小叉熵原理构造了指数型的舰载机机群恢复度函数M(t),计算舰载机平均保障准备时间,对于进一步的保障任务优化和调度具有重要意义。     

散货船三维时域波浪载荷计算研究

采用三维势流理论进行船舶运动和波浪载荷预报时,有2种Green函数可供选择:自由Green函数和Rankine源.混合源法,同时使用这2种Green函数,结合了两者的优点.本文对三维时域混合源法开展研究,为了验证该方法的可行性,本文对一艘散货船进行了时域上运动和波浪载荷的计算,并将计算结果转化到频域上以得到RAO.最终把有航速随浪和顶浪工况下该散货船垂向运动和载荷的RAO与WASIM计算结果以及模型试验结果进行比较.比较结果表明,混合源法计算结果与WASIM结果大体一致;在波长较短时,两者计算结果和试验值结果吻合较好,波长较长时两者计算结果都较试验值偏大.随浪海况下2种计算结果较顶浪更贴近试验值.     

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.