基于集成法的高速铁路运营安全综合评价

针对高速铁路安全运营问题,采用集成法进行了综合研究.从“人员—车体—轨道及设备—环境”角度对影响高速铁路安全的因素进行分析,建立高速铁路运营安全的评价指标体系,对高速铁路运营安全进行了评价研究.针对高速铁路运营过程中影响因素进行系统分析,结合结构熵权法、灰色聚类法、模糊评判法的适应性特点,将不同的方法运用到高速铁路运营安全评价的不同步骤中,构建了高速铁路运营安全集成评价方法.将该方法应用于京沪高速铁路运营安全评价,分别对2011~2013年铁路运营安全数据进行采集和计算,结果显示各年京沪高速铁路运营安全值分别为6.141 8,6.314 9,6.694 1,表明京沪高速铁路运营安全状况处于安全等级,且安全值逐年上升,评价结果较为直观的反映了高速铁路运营安全的态势,表明该方法在高速铁路运营安全综合评价应用中有较好的实用性.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

信息融合技术在交通流量预测中的应用

交通流量预测是智能运输系统的一个重要组成内容，但传统的数学方法一直未能取得令人满意的预测效果。信息融合技术是最近十多年来新兴的技术。它通过合理协调多源数据，充分综合有用信息，在较短的时间内，以较小的代价获得对未来交通流量的预测。实验证明，借助信息融合理论建立的聚类分析模型和神经网络模型对未来交通流量的预测比较准确，有实际意义。     

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.     

低等级公路路段交通安全服务水平分级量化研究

安全服务水平用来描述道路的交通安全状况和道路为交通参与者提供交通安全服务的一种质量指标.为了客观评价低等级公路路段的交通安全状况.提出了路段安全服务水平的基本概念,分析了影响路段安全服务水平的因素,选取线形、路侧危险度、交通量、货车比例及大小车速度差作为评价指标,将路段安全服务水平划分为4个等级,建立灰类白化函数,确定相应阈值,运用灰色聚类评价法评价路段的安全服务水平.应用灰色聚类法评价实际低等级公路路段的安全服务水平,结果表明该方法是合理可行的.     

基于两阶段K-means聚类的道路运行状况评价方法

为有效评价道路运行状况,通过分析车辆在行驶过程中运行状态的变化,研究了一种基于两阶段K-means聚类(TSKC)的道路运行状况评价方法.针对K-means聚类数选取的任意性和聚类中心选取的随机性问题,提出基于遍历的K-means聚类方法,采用类吸引度确定聚类数和初始中心,并以此为初始条件进行第二阶段K-means聚类,得到交通模式.提出模式吸引度、路段评价指数、分布均衡度,并用这些指标来评价路段交通运行状况.以北京市朝阳区北辰东路为例进行验证,结果表明,该方法比传统道路评价方法更细致、全面、直观地描绘了车辆状态的演变过程和交通模式的分布情况,具有良好的实用性.     

一种基于数据挖掘的GIS及在航海中的应用

根据聚类分析方法中密度凝聚的思想，提出一种新的复合聚类分析算法，进一步将这种算法用于地理信息系统的数据挖掘，并应用于船舶航线的自动设计。     

船用柴油机智能报警监控系统的设计

  目的  船舶轴系监测系统具有监控对象多、测试数据量大和存储空间需求大等特点,如果存储方案不合理,数据未预先统计和分类存储,会导致数据不便于检索、计算和分析。为此,提出针对测试数据的批量统计特征数据及分表存储的方法。  方法  在现有数据库的基础上,设计时间序列特征数据表和汇总表,以及统计测试数据的均值、极值、标准差、偏度等的流程;采用仿真对比,选择均值和偏度作为特征向量;通过DBSCAN聚类设计传感器数据异常识别算法,验证对传感器系统异常数据的识别效果。  结果  结果表明,所提方法对异常数据的识别效果较好,  结论  可适用于实际测试系统的特征数据提取。     

A two-phase model for dry port location with an application to the port of Dalian in China

This paper provides guidance for an optimal and reasonable dry port layout for the port of Dalian in China. We present a two-phase framework on the location of dry ports, which solves the selection of candidate inland cities and optimal dry port location choice, respectively. Fuzzy C-Means Clustering is applied to select alternative cities in the vast hinterland of the seaport of Dalian, with a view to identify evaluation factors that affect the location selection decision. A cost-minimisation linear programming solution is proposed, with the aid of a genetic algorithm, to choose the optimal location as well as capacity level among the candidate inland cities.