Design and Practice of Cloud Computing Platform for TBM Operation Information北大核心CSCD

The problems of TBM construction data loss, information barriers and absence of data mining have con⁃ strained the advancement of basic technologies in TBM field. Aimed at building a cloud computation platform for TBM operation information, a new concept of 3B, i. e. Born by digit, Born in format and Born to the cloud, was pre⁃ sented, the issues of information acquisition, transmission and storage during TBM operation were solved, and massive heterogeneous information intelligent transmission system and big data warehouse of TBM group were established. A structure model of cloud computation platform was designed by taking Hadoop system as ecosphere, and a cloud computation platform was built to deploy related algorithm, realizing on-line monitoring and data sharing, further⁃ more the data law mining of interaction of rock mass versus TBM machine was conducted based on big data tech⁃ nique, exploring the development direction of the information platform. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

An integrated framework for embedding large-scale dynamic traffic assignment models in advanced traveler information systems

In recent years, increasing attention has been drawn to the development of various applications of intelligent transportation systems (ITS), which are credited with the amelioration of traffic conditions in urban and regional environments. Advanced traveler information systems (ATIS) constitute an important element of ITS by providing potential travelers with information on the network's current performance both en-route and pre-trip. In order to tackle the complexity of such systems, derived from the difficulty of providing real-time estimations of current as well as forecasts of future traffic conditions, a series of models and algorithms have been initiated. This paper proposes the development of an integrated framework for real-time ATIS and presents its application on a large-scale network, that of Thessaloniki, Greece, concluding with a discussion on development and implementation challenges as well as on the advantages and limitations of such an effort.     

Estimation of origin-destination matrices from link counts and sporadic routing data

Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

基于深度学习的智能车辆视觉里程计技术发展综述

针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题,利用深度学习可以弥补其不足。首先,简略介绍了基于模型的里程计的研究现状,然后对比了常用的智能车数据集,将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种,从网络结构、输入和输出特征、鲁棒性等方面进行分析,最后,讨论了基于深度学习的智能车辆视觉里程计研究热点,从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。     

面向移动终端的实时交通信息服务分析

为了进一步提高我国交通信息服务水平,缓解拥堵,对比研究了现有服务终端的特点,并对用户交通信息需求展开调查.结合服务终端特点及调查结果,构建了面向移动终端的实时交通信息服务系统.系统可向移动终端提供信息查询、信息发布、服务下载等功能,为用户提供出行全程的交通信息服务.     

多平台间信息传输测试分析方法

文章根据电子信息系统的试验需求，结合工程实际，从具体应用人手，讲述一种多平台间信息传输性能测评方法，并对如何实现数据采集与记录、信息传输实时监测、系统性能分析等方面进行了详尽描述，该方法为多平台综合试验提供了有效的评估手段。     

广东省高职院校图书馆信息资源共建共享问题探讨

分析广东省高职院校图书馆信息资源共建共享的必要性、可行性及当前所面临的问题，并提出信息资源共建共享的基本对策。     

基于城市轨道交通线网级的乘客信息系统建设研究

针对城市轨道交通乘客信息系统线网化后仍按传统方式构建存在资源浪费、管理困难、接口复杂繁多等诸多问题,提出建设线网级的乘客信息发布中心,实现对线网内PIS系统播出信息控制的思路,对线网级乘客信息系统系统架构、乘客信息发布中心构成进行重点研究。