基于任务系统集成的舰艇作战系统体系结构设计过程

舰艇作战系统已演变为具有功能、地理、控制全分布式特征的复杂系统,系统研制向基于任务系统集成的趋势发展,其体系结构的设计需满足体系化作战需求。按照＂自上而下为主,兼顾自下而上,层次和内容均衡＂的原则,阐述了一种基于任务系统集成的作战系统体系结构设计过程,通过系统分析、建模、评估的迭代设计过程,实现对作战系统体系结构设计、验证以及系统性能评估。     

50t手动液压压力机的设计与维修

为提高工作效率,减轻劳动强度,公司利用液压传动原理设计了一台50t手动液压压力机.介绍了液压压力机的构造工作原理和主要部件的选择.并介绍了机架构造及强度计算.最后介绍了几种常见故障及维修方法.     

Capacity drops at merges: An endogenous model

The Newell-Daganzo merge model is not only very simple but also accurately reproduces experimental findings. However, the capacity downstream of the merge is an exogenous variable in the model. This is a serious limitation for merges that behave as active bottlenecks because their downstream capacity is a direct consequence of the merging behavior. This paper proposes an analytical model that extends the Newell-Daganzo model by incorporating, endogenously, the capacity drop related to the merging process. Two cases are investigated depending on the traffic states on the on-ramp. The model properties are analyzed and a sensitivity analysis is performed to quantify the relative contribution of the each parameter in the capacity drop. Finally, the extended Newell-Daganzo model is validated with experimental data coming from an active merge bottleneck on the M6 freeway in UK.     

大型地下互通式立交型式选择和路线标准研究

文章通过对地下和地面立交对比分析,给出了三路和四路地下立体交叉的几种立交型式建议;并从造价和行车安全的角度,给出了地下立交分岔隧道的部分路线标准确定方法。在地下隧道立交的选型中,应尽量选取少交叉口、少叠交层次和行车视距较好的型式。在路线标准的选择中,尤其要注意满足运营速度下的停车视距,以保障行车安全。在考虑安全因素的同时,经济性和工程造价也是决定隧道型式和路线标准的重要因素。     

高寒隧道保温设计研究及热工计算方法初探

目前国内高寒隧道研究成果还不能完全满足隧道设计要求,文章借鉴我国建设部房建行业和日本国铁的研究成果,提出了适用于季节性冻土隧道洞口保温段长度、衬砌保温及水沟保温的设计计算方法。     

膨胀岩土地层盾构隧道结构力学行为研究

文章在提出膨胀接触压力概念的基础上,采用接触单元对膨胀接触压力和膨胀力的关系进行了研究,得出了膨胀接触压力与膨胀岩土分布、厚度、结构及地层刚度等的关系;同时对膨胀岩土地层中盾构隧道"荷载-结构"模式外荷载及"梁-弹簧"模型的特点进行了分析;并将其分析结果直接应用到成都地铁2号线膨胀岩土地层盾构隧道的结构分析中,得出了盾构隧道在不同膨胀岩土层、膨胀力、埋深和拼装方式条件下的结构内力值,对隧道结构设计起到了指导作用。     

中低速磁浮桥梁结构设计与试验研究

对中低速磁悬浮轨道梁设计和试验进行系统阐述。研究表明:中低速磁悬浮轨道梁适宜采用单箱单室后张预应力混凝土简支梁,强度计算不是轨道梁的控制因素,但是截面扭矩很大;刚度和动力性能成为中低速磁悬浮轨道梁的设计控制因素,通过试验,给出刚度和自振频率限值。专门研制的拉力调高钢支座能保证轨道梁横向稳定,预制架设方法是最适宜的施工方法。     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.     

Multi-objective optimization of a road diet network design

The present study focuses on the development of a model for the optimal design of a road diet plan within a transportation network, and is based on rigorous mathematical models. In most metropolitan areas, there is insufficient road space to dedicate a portion exclusively for cyclists without negatively affecting existing motorists. Thus, it is crucial to find an efficient way to implement a road diet plan that both maximizes the utility for cyclists and minimizes the negative effect on motorists. A network design problem (NDP), which is usually used to find the best option for providing extra road capacity, is adapted here to derive the best solution for limiting road capacity. The resultant NDP for a road diet (NDPRD) takes a bi-level form. The upper-level problem of the NDPRD is established as one of multi-objective optimization. The lower-level problem accommodates user equilibrium (UE) trip assignment with fixed and variable mode-shares. For the fixed mode-share model, the upper-level problem minimizes the total travel time of both cyclists and motorists. For the variable mode-share model, the upper-level problem includes minimization of both the automobile travel share and the average travel time per unit distance for motorists who keep using automobiles after the implementation of a road diet. A multi-objective genetic algorithm (MOGA) is mobilized to solve the proposed problem. The results of a case study, based on a test network, guarantee a robust approximate Pareto optimal front. The possibility that the proposed methodology could be adopted in the design of a road diet plan in a real transportation network is confirmed.     

A network option portfolio management framework for adaptive transportation planning

A real option portfolio management framework is proposed to make use of an adaptive network design problem developed using stochastic dynamic programming methodologies. The framework is extended from Smit’s and Trigeorgis’ option portfolio framework to incorporate network synergies. The adaptive planning framework is defined and tested on a case study with time series origin-destination demand data. Historically, OD time series data is costly to obtain, and there has not been much need for it because most transportation models use a single time-invariant estimate based on deterministic forecasting of demand. Despite the high cost and institutional barriers of obtaining abundant OD time series data, we illustrate how having higher fidelity data along with an adaptive planning framework can result in a number of improved management strategies. An insertion heuristic is adopted to run the lower bound adaptive network design problem for a coarse Iran network with 834 nodes, 1121 links, and 10 years of time series data for 71,795 OD pairs.