Daily activity pattern recognition by using support vector machines with multiple classes

The focus of this paper is to learn the daily activity engagement patterns of travelers using Support Vector Machines (SVMs), a modeling approach that is widely used in Artificial intelligence and Machine Learning. It is postulated that an individual’s choice of activities depends not only on socio-demographic characteristics but also on previous activities of individual on the same day. In the paper, Markov Chain models are used to study the sequential choice of activities. The dependencies among activity type, activity sequence and socio-demographic data are captured by employing hidden Markov models. In order to learn model parameters, we use sequential multinomial logit models (MNL) and multiclass Support Vector Machines (K-SVM) with two different dependency structures. In the first dependency structure, it is assumed that type of activity at time ‘t’ depends on the last previous activity and socio-demographic data, whereas in the second structure we assume that activity selection at time ‘t’ depends on all of the individual’s previous activity types on the same day and socio-demographic characteristics. The models are applied to data drawn from a set of California households and a comparison of the accuracy of estimation of activity types and their sequence in the agenda, indicates the superiority of K-SVM models over MNL. Additionally, we show that accuracy in estimating activity patterns increases using different sets of explanatory variables or tuning parameters of the kernel function in K-SVM.     

沥青路面不平整产生的原因及预防措施

结合工程实践．分析了沥青路面不平整产生的原因，并提出了提高路面平整度的工程措施。     

备件管理和决策支持系统的开发

介绍了新开发的备件管理和决策支持系统软件的功能，体系结构和特点。     

基于GA和SVM的水火弯板焰道布置优化方法

水火弯板是目前船舶建造中普遍采用的曲型外板加工方法,由于机理复杂,其加工过程参数确定往往依赖于工人的加工经验,难以实现标准化和规范化。文章提出基于遗传算法(GA)和支持向量机(SVM)的水火弯板焰道布置优化方法,以成形曲面和设计曲面之间的形状误差最小为优化目标,将焰道布置优化问题分解成计算最小形状误差和搜寻最佳匹配位置两个问题,并分别运用嵌套GA和SVM来实现这两个问题的求解。实例验证表明该方法能快速高效确定最优的加热线布置方案。     

Analyzing passenger train arrival delays with support vector regression

We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.     

Categorizing bicycling environments using GPS-based public bicycle speed data

A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.     

轨道车辆孔板送风特性的试验研究

轨道车辆客室内的气流组织直接影响乘客乘坐的舒适性。静压箱孔板送风具有结构简单、送风温度和速度均匀等优点,适用于区域温差和工作区风速要求严格的场所。但是由于轨道车辆空间有限,静压箱稳压层的高度受限,容易造成静压箱内静压不均匀,影响孔板送风的均匀性;而顶棚两侧由于存在一定的密封区域,对宽度方向上的送风均匀性也有一定的影响。本文采用1∶1等比例模型试验的方法,针对一种运用在磁悬浮列车上的孔板,在等温条件下,对轨道车辆孔板送风规律进行研究,考察其阻力特性、孔板上方静压分布及孔板送风均匀性。     

基于支持向量回归机和粒子群算法的船舶操纵运动模型辨识

基于仿真的Z形试验数据,应用支持向量回归机对船舶操纵运动响应模型进行了机理建模,从核函数结构中得到了模型中的操纵性指数,并利用建立的响应模型进行了Z形试验的运动预报,同时引入粒子群算法对惩罚因子C值进行寻优,以减少惩罚因子C值选择的任意性对船舶操纵运动模型辨识精度产生的不利影响。通过将运动预报结果同仿真试验数据进行比较,验证了文中方法的有效性。     

公路隧道洞口段支护结构加强设计优化研究

结合目前公路隧道洞口段支护结构加强设计存在的问题,文章从洞口段与洞身浅埋段的围岩特性、荷载作用特点入手,基于超前管棚、初期支护、二次衬砌的作用机理,分析了支护结构在不同围岩条件下的作用特点,给出了洞口加强段和洞身浅埋段衬砌设计的合理化建议。主要得到以下结论:(1)洞口段围岩破碎,自稳能力差,超前支护为围岩早期稳定提供条件,初期支护为主要承载结构,二次衬砌承担部分荷载并提供一定的安全储备;由于洞口段衬砌上覆荷载较洞身浅埋段小得多,不宜加强二次衬砌,因此洞口加强段衬砌设计应以加强超前支护与初期支护为主。(2)洞身浅埋段围岩较完整,有一定的自稳能力,二次衬砌为主要承载结构,初期支护主要发挥与围岩粘附的协同作用;洞身浅埋段衬砌设计应以加强二次衬砌为主,并要控制施作时机。     

大伙房输水工程特长隧洞TBM选型及施工关键技术研究

大伙房输水工程隧洞长85.32 km,开挖洞径8.03 m,属于深埋特长隧洞.隧洞施工采用以TBM为主、钻爆法为辅的联合施工方式.隧洞穿越区域水文地质条件复杂,开挖中面临的主要工程地质问题有围岩稳定性、隧洞涌水、石英砂岩的掘进效率及岩爆等三大关键技术难题.文章通过对大量文献资料和工程实例的研究,概述了TBM近一个世纪的发展及其在隧道建设中的应用现状和主要问题.通过对大伙房特长隧洞所穿越区域的地质条件以及主要工程地质问题进行分析,并结合国内外已有的TBM施工经验,以及对特长隧洞TBM选型进行分析,决定选用锚喷支护+模筑混凝土复合衬砌结构支护系统代替管片衬砌结构,采用连续皮带机高速度出碴、TBM掘进独头长距离施工通风以及TBM洞内组装和检修等技术,对大伙房输水工程特长隧洞高速度、高标准、高质量的建成发挥了重要的保证作用,也可为今后类似工程提供参考和借鉴.