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  • ISBN:9787548752936
  • 装帧:一般胶版纸
  • 册数:暂无
  • 重量:暂无
  • 开本:26cm
  • 页数:104页
  • 出版时间:2023-03-01
  • 条形码:9787548752936 ; 978-7-5487-5293-6

内容简介

本书共分六个部分。交通大数据的种类;交通大数据分析的发展现状和应用前景;交通大数据分析技术简介。交通地理信息数据、车辆GPS数据、智能卡刷卡数据、车辆识别类数据、手机数据等交通大数据的数据结构与特征;交通大数据时空分辨率与其应用场景的关联关系。交通地理信息数据的预处理技术(坐标系同化、位置关系判断);车辆GPS数据、智能卡刷卡数据、车辆识别类数据、手机数据等交通大数据的预处理技术(数据清洗、数据集成、数据变换、数据归约);缺失值、异常值、重复值数据的处理。交通网络数据的采集与建模方法;复杂网络分析技术(度分布、连通性、路径距离、聚类系数、介数中心度、同配性);交通二分网络建模及分析技术(定义、交通拥堵溯源二分网络);个体移动网络建模及分析技术(熵和可预测性、出行模体);交通网络渗流相变分析技术。交通大数据融合思维;多源车辆GPS数据融合技术;智能卡刷卡数据融合技术;车辆GPS数据与车牌识别数据融合技术;手机数据与多源交通数据融合技术。基于拥堵车源信息的交通管控;基于拥堵车源信息的路径诱导;基于异常移动网络的人群聚集预警。

目录

Chapter 1 Introduction 1.1 Big data technology 1.2 A brief introduction of transportation big data 1.3 Challenges in the transportation big data analysis 1.4 The applications of transportation big data 1.5 Brief introduction of the following chapters Chapter 2 Data structures and properties of transportation big data 2.1 Geographic information data 2.2 Vehicle GPS data 2.3 Smart card data 2.4 Vehicle identification data 2.5 Mobile phone data Chapter 3 Transportation big data preprocessing 3.1 Basic steps of data preprocessing 3.2 Data preprocessing of geographic information data 3.3 Data preprocessing of vehicle GPS data 3.4 Data preprocessing of smart card data 3.5 Data preprocessing of license plate recognition data 3.6 Data preprocessing of mobile phone data for transportation 4.1 A method for generating transportation networks 4.2 Fundamental network analysis tools 4.3 Network modeling and analysis for individual mobility 4.4 Network modeling and analysis of collective mobility 4.5 Network percolation analysis Chapter 5 Multi-source trans~rtation big data fusion and analysis 5.1 Characteristics of different transportation big data 5.2 Insights for developing transportation big data fusion approaches 5.3 Data fusion of multi-source vehicle GPS data 5.4 Data fusion of LPR data and taxi GPS data 5.5 Data fusion of MS data, subway smart card data and taxi GPS data Chapter 6 Applications tran~ortation big data 6.1 Application of CDR data in estimating travel demand 6.2 Application of CDR data in identifying vehicle sources 6.3 Applications of transportation big data in deploying congestion mitigation strategies 6.4 Application of transportation big data in anticipating large crowd gatherings 6.5 Application of open-source data in predicting subway passenger flow References
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