Kooperativer Bibliotheksverbund

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Umfang: 1 online resource (404 pages) : , illustrations (some color)

Ausgabe: 1st ed.

ISBN: 0-12-809851-1

Anmerkung: Front Cover -- Data Analytics for Intelligent Transportation Systems -- Copyright Page -- Dedication -- Contents -- About the Editors -- About the Contributors -- Preface -- Acknowledgments -- 1 Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics -- 1.1 Intelligent Transportation Systems as Data-Intensive Applications -- 1.1.1 ITS Data System -- 1.1.2 ITS Data Sources and Data Collection Technologies -- 1.2 Big Data Analytics and Infrastructure to Support ITS -- 1.3 ITS Architecture: The Framework of ITS Applications -- 1.3.1 User Services and User Service Requirements -- 1.3.2 Logical Architecture -- 1.3.3 Physical Architecture -- 1.3.4 Service Packages -- 1.3.5 Standards -- 1.3.6 Security -- 1.4 Overview of ITS Applications -- 1.4.1 Types of ITS Applications -- 1.4.2 ITS Application and Its Relationship to Data Analytics -- 1.5 Intelligent Transportation Systems Past, Present, and Future -- 1.5.1 1960'S and 1970'S -- 1.5.2 1980'S and 1990'S -- 1.5.3 2000'S -- 1.5.4 2010'S and Beyond -- 1.6 Overview of Book: Data Analytics for ITS Applications -- Exercise Problems -- References -- 2 Data Analytics: Fundamentals -- 2.1 Introduction -- 2.2 Functional Facets of Data Analytics -- 2.2.1 Descriptive Analytics -- 2.2.1.1 Descriptive Statistics -- 2.2.1.2 Exploratory Data Analysis -- 2.2.1.3 Exploratory Data Analysis Illustration -- 2.2.1.4 Exploratory Data Analysis Case Studies -- 2.2.2 Diagnostic Analytics -- 2.2.2.1 Diagnostic Analytics Case Studies -- 2.2.2.1.1 Student Success System -- 2.2.2.1.2 COPA -- 2.2.2.1.3 Diagnostic Analytics in Teaching and Learning -- 2.2.3 Predictive Analytics -- 2.2.3.1 Predictive Analytics Use Cases -- 2.2.4 Prescriptive Analytics -- 2.3 Evolution of Data Analytics -- 2.3.1 SQL Analytics: RDBMS, OLTP, and OLAP. , 2.3.2 Business Analytics: Business Intelligence, Data Warehousing, and Data Mining -- 2.3.2.1 Business Intelligence -- 2.3.2.2 Data Warehouses, Star Schema, and OLAP Cubes -- 2.3.2.3 ETL Tools -- 2.3.2.4 OLAP Servers -- 2.3.2.5 Data Mining -- 2.3.3 Visual Analytics -- 2.3.4 Big Data Analytics -- 2.3.5 Cognitive Analytics -- 2.4 Data Science -- 2.4.1 Data Lifecycle -- 2.4.2 Data Quality -- 2.4.3 Building and Evaluating Models -- 2.5 Tools and Resources for Data Analytics -- 2.6 Future Directions -- 2.7 Chapter Summary and Conclusions -- 2.8 Questions and Exercise Problems -- References -- 3 Data Science Tools and Techniques to Support Data Analytics in Transportation Applications -- 3.1 Introduction -- 3.2 Introduction to the R Programming Environment for Data Analytics -- 3.3 Research Data Exchange -- 3.4 Fundamental Data Types and Structures: Data Frames and List -- 3.4.1 Data Frame -- 3.4.2 List -- 3.5 Importing Data from External Files -- 3.5.1 Delimited -- 3.5.2 XML -- 3.5.3 SQL -- 3.6 Ingesting Online Social Media Data -- 3.6.1 Static Search -- 3.6.2 Dynamic Streaming -- 3.7 Big Data Processing: Hadoop MapReduce -- 3.8 Summary -- 3.9 Exercises -- References -- 4 The Centrality of Data: Data Lifecycle and Data Pipelines -- 4.1 Introduction -- 4.2 Use Cases and Data Variability -- 4.3 Data and its Lifecycle -- 4.3.1 The USGS Lifecycle Model -- 4.3.2 Digital Curation Center (DCC) Curation Model -- 4.3.3 DataONE Model -- 4.3.4 SEAD Research Object Lifecycle Model -- 4.4 Data Pipelines -- 4.5 Future Directions -- 4.6 Chapter Summary and Conclusions -- 4.7 Exercise Problems and Questions -- 4.7.1 Exercise 1. Defining and Describing Research Data -- 4.7.2 Exercise 2. Mapping Research Project onto the Lifecycle -- 4.7.3 Exercise 3. Data Organization -- 4.7.4 Exercise 4. Data Pipelines -- References. , 5 Data Infrastructure for Intelligent Transportation Systems -- 5.1 Introduction -- 5.2 Connected Transport System Applications and Workload Characteristics -- 5.3 Infrastructure Overview -- 5.4 Higher-Level Infrastructure -- 5.4.1 MapReduce and Beyond: Scalable Data Processing -- 5.4.2 Data Ingest and Stream Processing -- 5.4.3 SQL and Dataframes -- 5.4.4 Short-Running and Random Access Data Management -- 5.4.5 Search-Based Analytics -- 5.4.6 Business Intelligence and Data Science -- 5.4.7 Machine Learning -- 5.5 Low-Level Infrastructure -- 5.5.1 Hadoop: Storage and Compute Management -- 5.5.2 Hadoop in the Cloud -- 5.6 Chapter Summary and Conclusions -- Exercise Problems and Questions -- References -- 6 Security and Data Privacy of Modern Automobiles -- 6.1 Introduction -- 6.2 Connected Vehicle Networks and Vehicular Applications -- 6.2.1 In-Vehicle Networks -- 6.2.2 External Networks -- 6.2.3 Innovative Vehicular Applications -- 6.3 Stakeholders and Assets -- 6.4 Attack Taxonomy -- 6.5 Security Analysis -- 6.5.1 Network and Protocol Vulnerability Analysis -- 6.5.2 Attacks -- 6.5.2.1 Antitheft system attacks -- 6.5.2.2 ECU attacks -- 6.5.2.3 TPMS attacks -- 6.5.2.4 VANETs attacks -- 6.6 Security and Privacy Solutions -- 6.6.1 Cryptography Basics -- 6.6.2 Security Solutions for Bus Communications -- 6.6.2.1 Code obfuscation -- 6.6.2.2 Authentication, confidentiality, and integrity -- 6.6.2.2.1 Authentication -- 6.6.2.2.2 Confidentiality -- 6.6.2.2.3 Integrity -- 6.6.2.3 Rootkit traps -- 6.6.2.4 Intrusion detection system -- 6.6.2.5 Gateway firewall -- 6.6.3 WPAN Security and Privacy -- 6.6.3.1 Bluetooth security checklist -- 6.6.3.2 Secure WPAN -- 6.6.3.3 Enabling data privacy in WPAN -- 6.6.4 Secure VANETs -- 6.6.5 Secure OTA ECU Firmware Update -- 6.6.6 Privacy Measurement of Sensor Data -- 6.6.7 Secure Handover -- 6.7 Future Research Directions. , 6.8 Summary and Conclusions -- 6.9 Exercises -- References -- 7 Interactive Data Visualization -- 7.1 Introduction -- 7.2 Data Visualization for Intelligent Transportation Systems -- 7.3 The Power of Data Visualization -- 7.4 The Data Visualization Pipeline -- 7.5 Classifying Data Visualization Systems -- 7.6 Overview Strategies -- 7.6.1 Data Quantity Reduction -- 7.6.2 Miniaturizing Visual Glyphs -- 7.7 Navigation Strategies -- 7.7.1 Zoom and Pan -- 7.7.2 Overview+Detail -- 7.7.3 Focus+Context -- 7.8 Visual Interaction Strategies -- 7.8.1 Selecting -- 7.8.2 Linking -- 7.8.3 Filtering -- 7.8.4 Rearranging and Remapping -- 7.9 Principles for Designing Effective Data Visualizations -- 7.10 A Case Study: Designing a Multivariate Visual Analytics Tool -- 7.10.1 Multivariate Visualization Using Interactive Parallel Coordinates -- 7.10.2 Dynamic Queries Through Direct Manipulation -- 7.10.3 Dynamic Variable Summarization via Embedded Visualizations -- 7.10.4 Multiple Coordinated Views -- 7.11 Chapter Summary and Conclusions -- 7.12 Exercises -- 7.13 Sources for More Information -- 7.13.1 Journals -- 7.13.2 Conferences -- References -- 8 Data Analytics in Systems Engineering for Intelligent Transportation Systems -- 8.1 Introduction -- 8.2 Background -- 8.2.1 Systems Development V Model -- 8.2.1.1 Project initiation -- 8.2.1.2 Preliminary engineering -- 8.2.1.3 Plans, specifications, and estimates -- 8.2.1.4 Construction -- 8.2.1.5 Project closeout -- 8.2.1.6 Operations and maintenance -- 8.2.2 Continuous Engineering -- 8.2.3 AADL -- 8.2.3.1 Language overview -- 8.2.3.2 Behavior annex -- 8.2.3.3 Error annex -- 8.2.3.4 AGREE -- 8.2.3.5 Resolute -- 8.3 Development Scenario -- 8.3.1 Data Analytics in Architecture -- 8.3.2 The Scenario -- 8.4 Summary and Conclusion -- 8.5 Exercises -- 8.6 Appendix A -- 8.6.1 EMV2 Error Ontology -- References. , 9 Data Analytics for Safety Applications -- 9.1 Introduction -- 9.2 Overview of Safety Research -- 9.2.1 Human Factors -- 9.2.2 Crash Count/Frequency Modeling -- 9.2.3 Before and After Study -- 9.2.4 Crash Injury Severity Modeling -- 9.2.5 Commercial Vehicle Safety -- 9.2.6 Data Driven Highway Patrol Plan -- 9.2.7 Deep Learning from Big and Heterogeneous Data for Safety -- 9.2.8 Real-Time Traffic Operation and Safety Monitoring -- 9.2.9 Connected Vehicles and Traffic Safety -- 9.3 Safety Analysis Methods -- 9.3.1 Statistical Methods -- 9.3.1.1 Count data modeling -- 9.3.1.2 Categorical data modeling -- 9.3.2 Artificial Intelligence and Machine Learning -- 9.4 Safety Data -- 9.4.1 Crash Data -- 9.4.2 Traffic Data -- 9.4.3 Roadway Data -- 9.4.4 Weather Data -- 9.4.5 Vehicle and Driver Data -- 9.4.6 Naturalistic Driving Study -- 9.4.7 Big Data and Open Data Initiatives -- 9.4.8 Other Data -- 9.5 Issues and Future Directions -- 9.5.1 Issues With Existing Safety Research -- 9.5.2 Future Directions -- 9.6 Chapter Summary and Conclusions -- 9.7 Exercise Problems and Questions -- References -- 10 Data Analytics for Intermodal Freight Transportation Applications -- 10.1 Introduction -- 10.1.1 ITS-Enabled Intermodal Freight Transportation -- 10.1.2 Data Analytics for ITS-Enabled Intermodal Freight Transportation -- 10.2 Descriptive Data Analytics -- 10.2.1 Univariate Analysis -- 10.2.1.1 Chi-squared test -- 10.2.1.2 K-S test -- 10.2.1.3 A-D test -- 10.2.1.4 Comments on chi-squared, K-S, and A-D tests -- 10.2.2 Bivariate Analysis -- 10.3 Predictive Data Analytics -- 10.3.1 Bivariate Analysis -- 10.3.2 Multivariate Analysis -- 10.3.3 Fuzzy Regression -- 10.4 Summary and Conclusions -- 10.5 Exercise Problems -- 10.6 Solution to Exercise Problems -- References -- 11 Social Media Data in Transportation -- 11.1 Introduction to Social Media. , 11.2 Social Media Data Characteristics.

Weitere Ausg.: ISBN 0-12-809715-9

Sprache: Englisch