铁路超限超重货物运输决策支持系统设计与实现

构建决策支持系统是实现超限超重货物运输组织信息化管理的有效手段。结合超限超重货物运输特征,提出了限界综合与比较方法,构建了超限超重货物运输径路决策模型。根据超限超重货物运输组织工作流程,采用基于3层B/S模式的网络编程技术,开发了包含铁道部、铁路局和站段3级用户的超限超重货物运输决策支持系统,可以高效管理铁路限界,快速制定超限超重货物运输组织方案。     

A Note on Quantitative Definition of Risk

Risk analysis has become more and more important in practical application，but there has not been a widely accepted flame work and model for the research．Motivated by factor analysis method and Kaplan-Garrick’s quantitative defmition of risk，a general risk model Was established based on analyzing risk situations and employing information system and evidence theory，and Kaplan-Garrick’s result Was improved to introduce a frame word for analyzing and managing risk．     

故障树分析的二元决策图方法

为了克服故障树方法分析复杂系统存在的不足,引入二元决策图理论,把故障树转化成二元决策图;然后自上而下遍历二元决策图,得到最小割集和顶事件发生的概率.二元决策图转化过程中,基本事件的排序直接影响二元决策图的结构大小.在不改变故障树逻辑关系的前提下,先对故障树进行简化,除去一些冗余的部分;然后从故障树结构重要度的角度,对简化后故障树的基本事件进行排序.实例证明所采用的基本事件排序方法是有效的,能够简化故障树定性和定量分析过程.     

高速公路突发事件快速响应与决策平台构建

针对高速公路突发事件应急响应滞后、处置效率不高的问题,从突发事件的自动发现、影响评估、预案生成、资源配置、效果评估等多维角度进行系统全面的研究,推动建立跨区域跨部门应急联动救援机制。采用GIS地图技术、Electron框架技术等方法构建基于分布协作的高速公路重大突发事件快速响应与决策支持平台,实现对突发事件发现、响应、处置、救援及事后分析等环节的全流程管理,提升应急处置效能和效率。     

确定长途电信卡价格的决策支持系统

长途电信卡业务是电信业务市场化程度最高的业务之一,长期以来,卡的价格(折扣)一直由人确定,带有一定的主观局限性.卡的价格受许多因素的制约,如何科学制定价格是电信业务的一个难点.运用计算机软件、决策支持系统和模糊数学技术等工具,提出了将长途电信卡价格这个一直由人工决策的复杂问题,转化为通过决策支持系统来辅助决策的构想,从而提高决策的科学性.     

Onboard monitoring of fatigue damage rates in the hull girder

Most new advanced ships have extensive data collection systems to be used for continuous monitoring of engine and hull performance, for voyage performance evaluation etc. Such systems could be expanded to include also procedures for stress monitoring and for decision support, where the most critical wave-induced ship extreme responses and fatigue damage accumulation can be estimated for hypothetical changes in ship course and speed in the automatically estimated wave environment.The aim of this paper is to outline a calculation procedure for fatigue damage rate prediction in hull girders taking into account whipping stresses. It is conceptually shown how such a method, which integrates onboard estimation of sea states, can be used to deduce decision support with respect to the accumulated fatigue damage in the hull girder.The paper firstly presents a set of measured full-scale wave-induced stress ranges in a container ship, where the associated fatigue damage rates calculated from a combination of the rain-flow counting method and the Palmgren-Miner damage rule are compared with damage predictions obtained from a computationally much faster frequency fatigue analysis using a spectral method. This analysis verifies the applied multi-modal spectral analysis procedure for fatigue estimation for cases where hull girder flexibility plays a role.To obtain an automated prediction method for the fatigue damage rates it is in the second part of the paper shown how a combination of the full-scale onboard acceleration and stress measurements can be used to calculate sea state parameters. These calculated environmental data are verified by a comparison to hindcast data.In the third part of the paper the full-scale fatigue stress ranges are compared to results from an analytical design oriented calculation procedure for flexible ship hulls in short-term estimated sea states.Altogether, it is conceptually shown that by a combination of the onboard estimated sea state parameters with the described analytical fatigue damage prediction procedure a method can be established for real-time onboard decision support which includes estimates of fatigue damage rates.     

A hybrid model of fuzzy and AHP for handling public assessments on transportation projects

Having an effective public participation in transportation planning and project development processes has been a major concern for developed countries. In the United States, for instance, all state Departments of Transportation are subject to the Transportation Equity Act (TEA-21) that formally requires public involvement in transportation planning. Since transportation planning involves public resources and values, judgments by the public should play a key role in determining final decisions. Therefore, all these agencies are required not only to disseminate information to the public, but also to solicit and consider public opinion in forming transportation policy. This work presents a decision support model, with public involvement and public oversight, to help policy makers select appropriate transportation projects for implementation. Since focus groups will face multiple objectives and inexact information in the process, a hybrid model of fuzzy logic and analytical hierarchy process (AHP) is proposed. A set of ‘if–then’ rules based on Weber’s psycho-physical law of 1834 is presented to reason from fuzzy numbers to capture essential subjective preferences, pairwise, among the alternatives. The AHP is then incorporated to estimate preference allotments among alternatives. An example application of the suggested method is provided seeking public approval of an appropriate public bus transportation system choosing between one run by municipal authorities and one run by private agencies to show how this procedure works.

公路工程预防性养护

介绍了公路工程预防性养护的概念,对比了国内外预防性养护的发展现状,介绍了预防性养护的具体方法及其适用条件,并对现存的以及进一步需要研究的问题进行了阐述和展望。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

Local environmental impact assessment as decision support for the introduction of electromobility in urban public transport systems

The urban transport sector offers a noteworthy potential for the reduction of national greenhouse gas emissions as well as local pollutant emissions such as nitrogen oxides and particulate matter if electric drive systems are increasingly used. Owing to the fact that electric busses are still in the development phase, higher investment costs have evolved for public transport providers. Hence, decision making about where to introduce electric bus lines is mainly characterized by economic as well as technological considerations. The integration of local or regional ecological aspects is often neglected. An interdisciplinary approach was applied to the bus network of an urban public transport provider. By combining spatial-analytical techniques and statistical methods, the local environmental relief potential of electric busses has been evaluated. The results show that due to their specific line characteristics and the frequency of service, two bus lines out of 28 are particularly suitable for the introduction of electromobility in Dresden, Germany. The presented scientific work contributes to the extension of environmental assessments and decision making tools by including the spatial dimension of environmental impacts. It increases the practical relevance, especially for management decisions of political and entrepreneurial stakeholders by providing a sensible decision basis for local or regional infrastructure projects.