青沙山隧道地温场测试与分析

采用埋入式铂金属热敏电阻测试方法,对地处青藏高原东部的青沙山公路隧道地温场进行现场实测与分析。结果表明:隧道洞口段,当环境温度较低时,地温差值分布明显,隧道进出口段衬砌背后出现相当长时间的负温;隧道洞内段,11月至次年3月,环境温度与所测地温相差很大,4月至7月,地温不稳定,围岩内各测点的温度曲线按位置重新进行调整,低温曲线上升到最高温处,高温的曲线则到达最低温处;7月至11月,隧道进口端地温下降曲线同隧道区环境温度变化同步,而隧道出口端地温比该处隧道内环境温度滞后;在冬季,衬砌内温度始终呈现倒U型,从隧道口向隧道中间,地温是上升的,地温上升呈现进口幅度小,出口幅度大,在低温度月份进口温度稍高于出口温度,在较高温月份进口温度低于出口温度;随着围岩深度的加深,温度是增加的,浅处的温度增量要大于深处;围岩内隧道径向存在某一比较稳定的温度边界条件。     

桥梁施工过程中监控数据管理系统设计思路

随着改革开放和经济建设的前进步伐,监控数据管理系统的研究工作面临着新的挑战和机遇。数据管理技术应用在桥梁施工监控领域,主要对大量的监控数据进行加工处理,为不同的施工监控提供各种有用的信息,保证桥梁各阶段的结构状态最大限度地接近设计理想状态。桥梁施工监控数据管理系统是以Access数据库为平台,运用VB的对象处理技术进行设计,主要实现了数据编辑、查询、数据分析、数据可视化、桥梁信息简介。     

关于铁路综合IP数据网建设方案的设想

介绍了中国铁路数据通信网现状，以及建设铁路综合数据网的必要性。提出了建设铁路综合数据网的方案，并从网络功能结构、规划原则以及工程实施等方面进行论述。     

铁路货票信息的数据挖掘

从三个方面对货票数据进行挖掘。即:运用集合理论构造关系数据库特征关联模型,描述了特征规则知识的表达:rule(E,T,Cp),给出rule(E,T,Cp)的发现算法和算法的应用举例,实例表明铁路多年来把标重作为计算整车费率唯一因素的办法应作适当调整;运用聚类方法对货票数据进行聚类,描述了聚类的步骤,对某公司2000年度的运距和收入进行聚类,分析聚类结果对铁路生产经营、市场营销的启示;运用ARIMA模型对货票数据进行季节性知识挖掘,描述挖掘的过程,用1992年～2001年的历史数据预报2002年货运量。     

数据生产管理特点分析

以数据信息生产企业为例 ,对数据生产管理特点进行分析 ,为此类企业建设有效的企业管理系统提出相应的建议。     

铁路局车站间数据交换系统的设计与实现

在铁路运输生产中,车站起着重要的作用.讨论铁路局调度系统中铁路局与车站数据交换的实现方法,利用Socket技术使得数据交换变得准确和快捷.实践表明,该系统能够很好地满足日常运输生产要求.     

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.     

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.     

Adaptive data dissemination for time-constrained messages in dynamic vehicular networks

With the advent of emerging wireless communication technologies, tremendous efforts have been put on promoting the safety and efficiency of transportation services by developing innovative applications. In particular, there has been significant interest in accessing information stored at RSUs (Roadside Units). The unique characteristics in vehicular networks, such as dynamic traffic factors including vehicle arrival rate, dwell time and data access patterns, bring us new challenges on data dissemination. This work dedicates to the investigation of timely and adaptive data dissemination in the dynamically changing traffic environment. Firstly, we derive an analytical model to explore and examine the effects of the dynamic traffic factors. In light of the theoretical results, an on-line scheduling algorithm is proposed for adaptive data dissemination. Finally, we evaluate performance of the new algorithm in a variety of circumstances. The simulation results demonstrate satisfactory performance of the proposed algorithm.     

地下管线测漏仪中数据的采集和预处理

在对管线渗漏信号进行分析的基础上 ,利用DSP芯片TMS32 0VC5 40 2和模数转换器CS5 36 0实现了数据的高精度采集。同时设计了基于渗漏信号特征的数字滤波器 ,对采集后的信号进行有效预处理。