材料性能数据管理平台开发及应用

材料性能参数是设计研发阶段产品设计仿真的基础,也是产品最终使用性能的保障。基于此,文章通过对国内外材料数据管理系统及发展趋势研究,基于目前流行的B/S架构,采用MVC分层设计模式,进行了材料数据发布、分析、应用和废止的全生命周期管理平台系统的开发,能够支持材料数据管理、对比分析,并能输出分析软件直接使用的材料卡片,实现同工程分析的无缝对接。材料性能数据管理平台在材料数据管理和应用层面具有很好的实用价值。     

成像雷达在港口交管系统应用的探讨

成像雷达广泛应用到各个领域.通过对成像雷达在港口交通管理系统中的可行性,以及整个系统的功能、组成的应用分析.探讨了成像雷达的优势,系统的设计与实现.     

基于环形线圈检测器采集信息的交通状态分类方法应用研究

在智能交通系统中,交通状态判别算法通常被用来进行道路环境中实时交通状态的判断。这些算法将外场设备采集到的实时交通流数据与既有的交通状态分类标准特征作比较,来识别交通系统运行的状态。应用聚类分析方法,结合数据预备技术和交通工程技术,对环形线圈监测系统采集的交通流基础特征数据进行挖掘,实现了一种交通状态分类方法,并对交通管理控制系统中实时交通状态的判断识别提供可靠的参照标准。     

绿色运输与物流的发展与现状 ——第16届海外华人国际交通科技年会综述

交通运输的快速发展给人们生活带来便利的同时,也给生态环境带来了严峻的挑战.如何实现运输环境的净化,运输与物流的可持续发展,运输资源的充分利用,运输效率的有效提升等是亟待解决的问题.从环境的角度对运输物流体系进行改进,实施绿色运输,发展多式联运,建立信息网络,形成一个与环境共生型的运输与物流系统是解决以上问题的有效途径.第16届COTA国际交通科技年会是在此背景下由国内外学者共同举办的研讨会,从非机动化交通、交通安全与应急响应、交通与环境、港航运输,以及铁路运输等方面对交通运输行业的研究现状进行了系统的阐述和全面的分析.当前交通运输行业的学术研究热点主要集中在车联网、大数据、无人驾驶、无人机、应急疏散、拥堵管理等方面.并对交通运输行业发展前景进行了展望.     

A comparative study on data segregation for mesoscopic energy modeling

On-road vehicles have been considered as one of the major contributors to energy consumption and air pollutant emissions. In order to quantify the corresponding environmental impacts, great efforts have been dedicated to the microscopic and macroscopic modeling for vehicle energy consumption and emissions. However, the mesoscopic modeling research that is focused on estimating trip-based energy consumption and is critical to some ITS applications (e.g., environmentally-friendly navigation), is relatively deficient. This study aims to investigate the effects of different data segregation methods on the mesoscopic modeling for vehicle energy consumption. A variety of novel methods, including the so-called conditional operating mode based method, have been proposed and evaluated using field data. Based on real-world data, statistical analyses have demonstrated the superior performance of enhanced models (i.e., conditional operating mode/VSP based models) in estimating vehicle energy consumption on a trip basis, compared to the other four models (velocity binning, time snipping, distance snipping and VSP based models) tested in this study.     

An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition,planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.     

User-centric interdependent urban systems: Using time-of-day electricity usage data to predict morning roadway congestion

Urban systems are interdependent as individuals’ daily activities engage using those urban systems at certain time of day and locations. There may exist clear spatial and temporal correlations among usage patterns across all urban systems. This paper explores such a correlation among energy usage and roadway congestion. We propose a general framework to predict congestion starting time and congestion duration in the morning using the time-of-day electricity use data from anonymous households with no personally identifiable information. We show that using time-of-day electricity data from midnight to early morning from 322 households in the City of Austin, can make reliable prediction of congestion starting time of several highway segments, at the time as early as 2 am. This predictor significantly outperforms a time-series predictor that uses only real-time travel time data up to 6 am. We found that 8 out of the 10 typical electricity use patterns have statistically significant affects on morning congestion on highways in Austin. Some patterns have negative effects, represented by an early spike of electricity use followed by a drastic drop that could imply early departure from home. Others have positive effects, represented by a late night spike of electricity use possible implying late night activities that can lead to late morning departure from home.     

Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

Measuring supply chain efficiency based on a hybrid approach

Supply chain management has a tremendous impact on the success of a company. One of the critical issues for gaining competitive advantages for companies is improving supply chain performance. Most studies about the application of Data Envelopment Analysis (DEA) Supply chain models do not identify the benchmarking units for inefficient supply chains. On the other, measuring the short run and long run of the supply chain efficiency is another challenge for decision makers in supply chain management. Hence, we propose a methodology of DEA for measuring of the supply chain. We integrated two approaches as special cases of the hybrid model and compare the short and long run strategies of supply chain and can be identified benchmarking.