各种评标方法在轨道交通设备招标中的运用

城市轨道交通设备系统投资巨大、系统众多、技术含量高,招标模式、供应市场情况各不相同,评标成为难点。介绍轨道交通设备系统常用的三种评标方法,从评比标准、流程、重点控制、适用范围等方面全面剖析;同时针对不同标段,推荐各种评标方法,并指出运用中可能的主要控制点。     

浅谈天津港的纳泥区与泥土利用问题

天津港作为需要常年进行航道、泊位清淤维护的港口,在新建、改建项目实施的同时,应尽快考虑解决能较长期保证清淤维护需要的纳泥区问题。本研究结合天津港南疆港区和北疆港区泊位的清淤纳泥区现状,探讨解决港口纳泥区的必要性,并提出清淤泥土开发利用的建议。     

中国到港船舶压载水中致病菌分析及检测

船舶压载水是海洋外来致病细菌传播的主要途径。对国内各沿海港口到港国际船舶的压载水中致病细菌的组成和检测方法进行对比分析。结果表明:国内各港口到港船舶的压载水中都有致病菌检出,主要类群为弧菌属(vibrio)、气单胞菌属(aeromonas)和大肠埃希氏菌(escherichia coli)等;船舶压载水和沉积物均在不同程度上携有致病菌。此外,综合分析船舶压载水致病菌的各种检测方法及其优缺点。     

电动二轮车驾驶人头部损伤再现不确定性方法

为了减少交通事故中不确定性信息在受伤害者损伤再现中造成的不利影响,采用拉丁超立方(LHS)试验设计和响应面蒙特卡洛相结合的方法对电动二轮车驾驶人头部损伤再现进行损伤不确定性分析。首先采用多体系统动力学方法对具有详细事故信息(含视频信息)和损伤记录的2起电动二轮车事故中的汽车碰撞速度进行再现和不确定性分析,并对比事故信息(视频信息、最终位置、电动二轮车驾驶人的运动学),进而验证事故再现结果的有效性。在此基础上,应用有限元方法将获得的电动二轮车驾驶人头部碰撞的边界条件加载至THUMS人体头部有限元模型,分析电动二轮车驾驶人头部损伤参数的不确定性与简明损伤准则AIS累计频率的分布关系,并对比电动二轮车事故案例中电动二轮车驾驶人的头部损伤法医鉴定记录。研究结果表明:蒙特卡洛不确定性分析方法能够较准确地预测电动二轮车事故中的汽车碰撞车速,采用该分析方法获得的电动二轮车驾驶人头部损伤等级与法医鉴定的脑部损伤记录高度吻合;蒙特卡洛不确定性分析方法可以适用于评估电动二轮车事故中电动二轮车驾驶人的头部损伤等级,研究结果可为电动二轮车驾驶人头部损伤研究提供理论依据和实证方法。     

基于数据预处理的铁路客运量灰色预测模型

铁路是国家的基础设施,对铁路的客运量进行准确地预测具有重要的理论意义和实际应用价值。首先对传统预测方法进行了分析,指出它们在运量预测中的不足,进而提出应用灰预测进行运量预测的优势。结合滑动平均法对灰预测方法进行了改进,同时考虑初始条件的改变。在预测2006-2010年全社会客运总量的基础上,根据铁路客运在各种运输方式中所占的份额预测2006-2010年的铁路客运总量。     

高速公路计重收费系统质量检验评定办法的研究

计重收费是一种新的收费管理模式,研究制定高速公路计重收费系统的质量检验评定办法及标准,对顺利实施计重收费具有指导性作用.     

Multi-criteria analysis of transport infrastructure projects

Planning, construction and operation of transport infrastructure are associated with a multitude of adverse effects on the environment. The Strategic Environmental Assessment (SEA) and Environmental Impact Assessment (EIA) are important legal instruments of the European Union's environmental policy that allows for identifying, predicting, preventing, and mitigating and or compensating for these adverse effects. As part of the environmental impact assessment, variants of planned activities and investment projects are considered in order to select the option, which is the most favourable from the environmental point of view. The primary goal of this work is to examine the possibility of using multi-criteria methods in order to select the route variant most favourable for the environment. In the first stage, a review of global literature from 2010 to 2019 was conducted on the subject of MCDM/MCDA (Multi-Criteria Decision Making/Multi-Criteria Decision Analysis) methods used in transportation. Based on the review, it was proven that the most popular methods used to solve multi-criteria decision problems in the field of transport are respectively: AHP with modifications, TOPSIS, DEMATEL, as well as methods encompassed in the so-called European trend, i.e. PROMETHEE and ELECTRE. Four selected methods were used in the empirical part of this work. They were used to select the variant of the expressway section in north-eastern Poland and compare the result of the analysis with the choice made in the analyzed environmental impact report.     

铜管路件焊缝致密度检验方法研究

铜管路件主要由铜管及三通接头焊接而成,焊接的质量对水冷系统有很大的影响。本文研究了铜管路件焊缝致密度的检验方法,介绍了铜管路件焊接方法、影响焊接质量的各种因素及焊接工艺过程。提出了铜管路件焊缝致密度检验方法,该方法首先对检验样本进行制备,接着通过由光学显微镜与计算机组成的成像系统对样本焊缝位置进行显微图像采集,对采集到的焊缝显微图像在图像软件中进行处理,获取焊缝缺陷的长度,从而计算焊缝位置的致密度。     

On the use of Lagrangian observations from public transport and probe vehicles to estimate car space-mean speeds in bi-modal urban networks

The Macroscopic Fundamental Diagram (MFD) has been recognized as a powerful framework to develop network-wide control strategies. Recently, the concept has been extended to the three-dimensional MFD, used to investigate traffic dynamics of multi-modal urban cities, where different transport modes compete for, and share the limited road infrastructure. In most cases, the macroscopic traffic variables are estimated using either loop detector data (LDD) or floating car data (FCD). Taking into account that none of these data sources might be available, in this study we propose novel estimation methods for the space-mean speed of cars based on: (i) the automatic vehicle location (AVL) data of public transport where no FCD is available; and (ii) the fused FCD and AVL data sources where both are available, but FCD is not complete. Both methods account for the network configuration layout and the configuration of the public transport system. The first method allows one to derive either uni-modal or bi-modal macroscopic fundamental relationships, even in the extreme cases where no LDD nor FCD exist. The second method does not require a priori knowledge about FCD penetration rates and can significantly improve the estimation accuracy of the macroscopic fundamental relationships. Using empirical data from the city of Zurich, we demonstrate the applicability and validate the accuracy of the proposed methods in real-life traffic scenarios, providing a cross-comparison with the existing estimation methods. Such empirical comparison is, to the best of our knowledge, the first of its kind. The findings show that the proposed AVL-based estimation method can provide a good approximation of the average speed of cars at the network level. On the other hand, by fusing the FCD and AVL data, especially in case of sparse FCD, it is possible to obtain a more representative outcome regarding the performance of multi-modal traffic.     

Big data in public transportation: a review of sources and methods

ABSTRACT

The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.