城市轨道交通车辆最高运行速度的选择

研究目的:通过对影响列车最高运行速度的几大要素进行分析,寻找轨道交通车辆选型时确定列车最高运行速度等级的一般规律,从而达到节约能源、减少车底数的目的.研究结论:确定城市轨道交通车辆最高运行速度等级时一般以平均车站间距作为首要依据,车站间距约为3.4 km时,推荐选择列车最高运行速度120 km/h;当车站站间距约为2.3 km时,推荐选择列车最高运行速度100 km/h;当车站站间距约为1.5 km时,推荐选择列车最高运行速度80 km/h.     

强夯法施工锤重参数的合理选用

如何选择强夯处理工程地基中的有关施工参数，直接关系到地基的加固效果。在对常规方法确定参数的全面分析后，通过对下落锤体对地基土体的冲力作用研究，提出了在同一单击能量（机械提升能量）情况下，采用重锤低落距处理地基工程优于轻锤高落距强夯方案。     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

浅谈三角高程测量在麦地河电站中的应用

随着电磁波测距仪、全站仪的普及,EDM三角高程测量越来越受到关注,应用范围也不断扩大。本文仅结合一些工作中的实例作了简略的阐述。     

路网车流径路选择随机相关机会规划模型及其混合遗传算法

研究随机环境下车流径路的选择问题,首先给出路网车流径路方案可靠性的定义,并在此基础上建立随机环境下车流径路选择问题的随机相关机会多目标规划模型。模型考虑了车流具有不同权重的情况,目标为极大化车流径路方案的可靠性及极小化期望总费用。用C＋＋语言编写K短路算法,并在Visual Studio 6.0上基于该算法开发了软件,用于计算网络上任意两点之间的K短路。以该软件计算出的K短路作为节点间的可选径路集,提出一种基于随机模拟的混合遗传算法。算例表明,在不同交叉和变异概率的条件下算法均可在给定进化代数内收敛至相同的最优解,有较强的适应性。     

应用模糊数学优化潭邵高速公路软基处理方案

地基方案的优化选择,就是使所选的地基方案满足多种要求。地基方案的优化受技术、经济、环保等多项指标的影响。聚类分析是模糊数学中研究物以类聚的一种多元分析方法。针对潭邵高速公路软土地基处理的特殊性,应用模糊数学理论对工程设计方案中的模糊性进行了分析,最终确定,潭邵高速公路软土地基,采用砂桩处理方案进行加固。     

IEEE802．16d无线MESH网络路由协议研究

无线Mesh网络是一种新型的无线通信网络,具有自组织、自管理和自愈的特点,是一种多跳的宽带无线接入网络结构,也是一种高容量、高速率的分布式网络。在介绍WMN基本概念的基础上,针对制约MESH网络发展的路由技术问题,在传统AODV路由协议的基础上提出了新的路由协议OLRP,并给出仿真结果。     

The Pollution-Routing Problem

The amount of pollution emitted by a vehicle depends on its load and speed, among other factors. This paper presents the Pollution-Routing Problem (PRP), an extension of the classical Vehicle Routing Problem (VRP) with a broader and more comprehensive objective function that accounts not just for the travel distance, but also for the amount of greenhouse emissions, fuel, travel times and their costs. Mathematical models are described for the PRP with or without time windows and computational experiments are performed on realistic instances. The paper sheds light on the tradeoffs between various parameters such as vehicle load, speed and total cost, and offers insight on economies of ‘environmental-friendly’ vehicle routing. The results suggest that, contrary to the VRP, the PRP is significantly more difficult to solve to optimality but has the potential of yielding savings in total cost.     

The multinomial logit model revisited: A semi-parametric approach in discrete choice analysis

The multinomial logit model in discrete choice analysis is widely used in transport research. It has long been known that the Gumbel distribution forms the basis of the multinomial logit model. Although the Gumbel distribution is a good approximation in some applications such as route choice problems, it is chosen mainly for mathematical convenience. This can be restrictive in many other scenarios in practice. In this paper we show that the assumption of the Gumbel distribution can be substantially relaxed to include a large class of distributions that is stable with respect to the minimum operation. The distributions in the class allow heteroscedastic variances. We then seek a transformation that stabilizes the heteroscedastic variances. We show that this leads to a semi-parametric choice model which links the linear combination of travel-related attributes to the choice probabilities via an unknown sensitivity function. This sensitivity function reflects the degree of travelers’ sensitivity to the changes in the combined travel cost. The estimation of the semi-parametric choice model is also investigated and empirical studies are used to illustrate the developed method.     

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.