图书超期原因的矩阵模型研究

在对数据进行分析的基础上,运用统计方法建立超期数的分布矩阵模型,然后根据各种图书的种类和超期天数对超期原因的影响,运用了加权的思想,利用经验数据分别对不同类别和超期天数的3种原因进行加权赋值,从而得到超期原因矩阵模型。     

平阳高速公路穿越东村小窑煤矿采空区设计

近年来山西省的高速公路穿越煤矿采空区和铝土矿采空区的工程很多,采空区的处治设计直接影响到高速公路的运营安全和使用寿命。结合工程实践,针对平阳高速公路煤矿采空区的特点,阐述穿越东村小窑煤矿采空区的三种处治设计方案,并对方案进行对比,可供同行参考。     

我国铁路运输系统节能问题的研究分析

从技术、管理和应用3个层面总结归纳了国内铁路运输系统节能问题的研究现状以及应用的研究方法,分析探讨了铁路运输系统节能研究面临的理论问题和现实问题,最后从应用角度提出了基于数据挖掘的能耗模型研究思路和基于组织管理的节能研究的主要内容.     

热力隧道在非降水施工中的力学特性监测

城市热力供水工程逐渐由近地层明挖向深地层暗挖转移,通过对北京市两个典型地质热力隧道的现场原位的监测,分析浅埋暗挖热力隧道在非降水施工过程中对于砂卵石地质和粉质黏土地质条件下的初支结构内力、围岩性状、水文特征的变化及对地层的影响.试验监测结果表明,隧道围岩的土压力分布不同,水压力分布相同,初衬钢筋内力分布不同,砂卵石地质条件下的隧道初衬受力情况较好,隧道开挖对地表沉降影响较小,研究结果可在开挖城市供热、供水及供气等小断面隧道时参考.     

重型货运车辆出行时空差异性和影响因素分析

重型货运车辆能促进城市经济生产和物流效率,但也因显著的排放、噪声和拥堵等外部属性对城市空间品质有明显影响,对其活动时空特征和影响因素分析是当前城市空间品质提升和交通管理精细化的迫切需求。基于深圳注册的12 t以上重型货车GPS轨迹数据,考虑不同类型货运车辆作业差异,提出利用统计抽样中最小样本量的观察结果确定不同类型重型货车轨迹数据出行切割阈值水平的方法;针对5类重型货车,分析出车强度和出行时段的差异性,与小客车进行对比分析发现,重型货车出行活动与小客车出行在时间上存在错峰特征,且夜间出行比例较高;重型货车的活动空间具有组内差异性,集装箱卡车具有显著的跨城市活动需求,更多承担中长距离运输功能,而土方车和重型罐式车更多承担本地化运输服务功能;重型货车的活动空间具有集聚特征,出行活动的节点强度具有无标度幂律分布特征;利用广义加性模型分析两类典型货车活动空间的影响因素,集装箱卡车活动与港口及交通仓储园区等物流设施呈现非线性关系,而普通大货车与工业园区等产业用地具有紧密关系。本文可以为重型货车交通精细化管理和交通需求模型建设提供新的思路和方法。     

西江长洲枢纽库尾段采砂工程对航道通航水流条件的影响

西江航道桂平航运枢纽—长洲水利枢纽之间存在约34 km的水位未衔接段,而大藤峡水利枢纽建设选取的江口料场位于长洲枢纽库区回水末端和上游附近滩段。为研究库尾段采砂工程对上游通航水流条件的影响,须同步考虑上游来水和下游库区回水的变化,建立桂平三江口上游黔江河段16 km和桂平枢纽—长洲枢纽约156 km的长河段二维水流数学模型,分析采砂工程对航道通航水流条件的影响。结果表明,采砂工程实施后上游桂平航运枢纽引航道水位降落0.09 m,布岭沙弯道凸岸侧边滩挖除引起主流一定左偏。整体上,除设计流量外,库尾段采砂工程对航道水流条件影响较小。     

数据挖掘技术在深度防御网络安全体系中的运用

随着攻击技术的日新月异及攻击数量的与日俱增,提高网络安全深度防御体系的效力是一个亟待解决的问题。将数据挖掘技术应用于网络安全深度防御体系中,在海量数据中挖掘有效信息、提高防御能力是一个新的思路。文章介绍了网络安全深度防御体系的概念及数据挖掘技术在网络安全深度防御体系构建中的应用。     

Classification of Livebus arrivals user behavior

With the increasing use of Intelligent Transport Systems, large amounts of data are created. Innovative information services are introduced and new forms of data are available, which could be used to understand the behavior of travelers and the dynamics of people flows. This work analyzes the requests for real-time arrivals of bus routes at stops in London made by travelers using Transport for London's LiveBus Arrivals system. The available dataset consists of about one million requests for real-time arrivals for each of the 28 days under observation. These data are analyzed for different purposes. LiveBus Arrivals users are classified based on a set of features and using K-Means, Expectation Maximization, Logistic regression, One-level decision tree, Decision Tree, Random Forest, and Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO). The results of the study indicate that the LiveBus Arrivals requests can be classified into six main behaviors. It was found that the classification-based approaches produce better results than the clustering-based ones. The most accurate results were obtained with the SVM-SMO methodology (Precision of 97%). Furthermore, the behavior within the six classes of users is analyzed to better understand how users take advantage of the LiveBus Arrivals service. It was found that the 37% of users can be classified as interchange users. This classification could form the basis of a more personalized LiveBus Arrivals application in future, which could support management and planning by revealing how public transport and related services are actually used or update information on commuters.     

A mathematical programming technique for matching time-stamped records in logistics and transportation systems

Time-stamped data for transportation and logistics are essential for estimating times on transportation legs and times between successive stages in logistic processes. Often these data are subject to recording errors and omissions. Matches must then be inferred from the time stamps alone because identifying keys are unavailable, suppressed to preserve confidentiality, or ambiguous because of missing observations. We present an integer programming (IP) model developed for matching successive events in such situations and illustrate its application in three problem settings involving (a) airline operations at an airport, (b) taxi service between an airport and a train station, and (c) taxi services from an airport. With data from the third setting (where a matching key was available), we illustrate the robustness of estimates for median and mean times between events under different random rates for “failure to record”, different screening criteria for outliers, and different target times used in the IP objective. The IP model proves to be a tractable and informative tool for data matching and data cleaning, with a wide range of potential applications.     

Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.