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Data Analytics for Intelligent Transportation Systems [Pehme köide]

Edited by (Eugene Douglas Mays Professor of Transportation, Clemson University, USA.), Edited by (Associate Professor, Michigan State University, USA), Edited by (Professor, School of Computing, Clemson University, USA)
  • Formaat: Paperback / softback, 344 pages, kõrgus x laius: 235x191 mm, kaal: 630 g
  • Ilmumisaeg: 04-Apr-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128097159
  • ISBN-13: 9780128097151
Teised raamatud teemal:
Data Analytics for Intelligent Transportation SystemsSuurem pilt
  • Formaat: Paperback / softback, 344 pages, kõrgus x laius: 235x191 mm, kaal: 630 g
  • Ilmumisaeg: 04-Apr-2017
  • Kirjastus: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128097159
  • ISBN-13: 9780128097151
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128097151.html
Märksõnad:

Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques.

The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies.

Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.

  • Includes case studies in each chapter that illustrate the application of concepts covered
  • Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies
  • Contains contributors from both leading academic and commercial researchers

Muu info

This informative guide demonstrates how data analytics can improve transportation-related management decisions, mobility, efficiency, and environmental impacts
About the Editors xv
About the Contributors xvii
Preface xxiii
Acknowledgments xxvii
Chapter 1 Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics
1(30)
Sakib M. Khan
Mizanur Rahman
Amy Apon
Mashrur Chowdhury
1.1 Intelligent Transportation Systems as Data-Intensive Applications
1(3)
1.1.1 ITS Data System
2(1)
1.1.2 ITS Data Sources and Data Collection Technologies
3(1)
1.2 Big Data Analytics and Infrastructure to Support ITS
4(5)
1.3 ITS Architecture: The Framework of ITS Applications
9(5)
1.3.1 User Services and User Service Requirements
10(1)
1.3.2 Logical Architecture
11(1)
1.3.3 Physical Architecture
11(1)
1.3.4 Service Packages
12(1)
1.3.5 Standards
13(1)
1.3.6 Security
14(1)
1.4 Overview of ITS Applications
14(7)
1.4.1 Types of ITS Applications
15(3)
1.4.2 ITS Application and Its Relationship to Data Analytics
18(3)
1.5 Intelligent Transportation Systems Past, Present, and Future
21(3)
1.5.1 1960's and 1970's
21(1)
1.5.2 1980's and 1990's
21(1)
1.5.3 2000's
22(1)
1.5.4 2010's and Beyond
23(1)
1.6 Overview of Book: Data Analytics for ITS Applications
24(7)
Exercise Problems
26(1)
References
27(4)
Chapter 2 Data Analytics: Fundamentals
31(38)
Venkat N. Gudivada
2.1 Introduction
31(1)
2.2 Functional Facets of Data Analytics
32(13)
2.2.1 Descriptive Analytics
33(8)
2.2.2 Diagnostic Analytics
41(2)
2.2.3 Predictive Analytics
43(2)
2.2.4 Prescriptive Analytics
45(1)
2.3 Evolution of Data Analytics
45(10)
2.3.1 SQL Analytics: RDBMS, OLTP, and OLAP
46(1)
2.3.2 Business Analytics: Business Intelligence, Data Warehousing, and Data Mining
47(6)
2.3.3 Visual Analytics
53(1)
2.3.4 Big Data Analytics
54(1)
2.3.5 Cognitive Analytics
54(1)
2.4 Data Science
55(5)
2.4.1 Data Lifecycle
56(1)
2.4.2 Data Quality
57(1)
2.4.3 Building and Evaluating Models
58(2)
2.5 Tools and Resources for Data Analytics
60(2)
2.6 Future Directions
62(1)
2.7
Chapter Summary and Conclusions
63(1)
2.8 Questions and Exercise Problems
64(5)
References
65(4)
Chapter 3 Data Science Tools and Techniques to Support Data Analytics in Transportation Applications
69(22)
Linh B. Ngo
3.1 Introduction
69(1)
3.2 Introduction to the R Programming Environment for Data Analytics
70(2)
3.3 Research Data Exchange
72(1)
3.4 Fundamental Data Types and Structures: Data Frames and List
72(3)
3.4.1 Data Frame
73(2)
3.4.2 List
75(1)
3.5 Importing Data from External Files
75(9)
3.5.1 Delimited
75(3)
3.5.2 XML
78(5)
3.5.3 SQL
83(1)
3.6 Ingesting Online Social Media Data
84(3)
3.6.1 Static Search
85(1)
3.6.2 Dynamic Streaming
86(1)
3.7 Big Data Processing: Hadoop MapReduce
87(3)
3.8 Summary
90(1)
3.9 Exercises
90(1)
References
90(1)
Chapter 4 The Centrality of Data: Data Lifecycle and Data Pipelines
91(22)
Beth Plate
Inna Kouper
4.1 Introduction
91(1)
4.2 Use Cases and Data Variability
92(3)
4.3 Data and Its Lifecycle
95(7)
4.3.1 The USGS Lifecycle Model
95(1)
4.3.2 Digital Curation Center (DCC) Curation Model
96(2)
4.3.3 DataONE Model
98(1)
4.3.4 SEAD Research Object Lifecycle Model
99(3)
4.4 Data Pipelines
102(5)
4.5 Future Directions
107(1)
4.6
Chapter Summary and Conclusions
108(1)
4.7 Exercise Problems and Questions
108(5)
4.7.1 Exercise
1. Defining and Describing Research Data
108(1)
4.7.2 Exercise
2. Mapping Research Project Onto the Lifecycle
109(1)
4.7.3 Exercise
3. Data Organization
109(1)
4.7.4 Exercise
4. Data Pipelines
109(1)
References
110(3)
Chapter 5 Data Infrastructure for Intelligent Transportation Systems
113(18)
Andre Luckow
Ken Kennedy
5.1 Introduction
113(1)
5.2 Connected Transport System Applications and Workload Characteristics
114(1)
5.3 Infrastructure Overview
115(2)
5.4 Higher-Level Infrastructure
117(5)
5.4.1 MapReduce and Beyond: Scalable Data Processing
117(2)
5.4.2 Data Ingest and Stream Processing
119(1)
5.4.3 SQL and Dataframes
120(1)
5.4.4 Short-Running and Random Access Data Management
121(1)
5.4.5 Search-Based Analytics
121(1)
5.4.6 Business Intelligence and Data Science
121(1)
15.4.7 Machine Learning
122(1)
5.5 Low-Level Infrastructure
122(3)
5.5.1 Hadoop: Storage and Compute Management
123(1)
5.5.2 Hadoop in the Cloud
123(2)
5.6
Chapter Summary and Conclusions
125(6)
Exercise Problems and Questions
125(1)
References
126(5)
Chapter 6 Security and Data Privacy of Modern Automobiles
131(34)
Juan Deng
Lu Yu
Yu Fu
Oluwakemi Hambolu
Richard R. Brooks
6.1 Introduction
131(1)
6.2 Connected Vehicle Networks and Vehicular Applications
132(3)
6.2.1 In-Vehicle Networks
132(1)
6.2.2 External Networks
133(1)
6.2.3 Innovative Vehicular Applications
133(2)
6.3 Stakeholders and Assets
135(2)
6.4 Attack Taxonomy
137(1)
6.5 Security Analysis
137(9)
6.5.1 Network and Protocol Vulnerability Analysis
138(2)
6.5.2 Attacks
140(6)
6.6 Security and Privacy Solutions
146(12)
6.6.1 Cryptography Basics
147(1)
6.6.2 Security Solutions for Bus Communications
148(4)
6.6.3 WPAN Security and Privacy
152(1)
6.6.4 Secure VANETs
153(2)
6.6.5 Secure OTA ECU Firmware Update
155(2)
6.6.6 Privacy Measurement of Sensor Data
157(1)
6.6.7 Secure Handover
158(1)
6.7 Future Research Directions
158(1)
6.8 Summary and Conclusions
159(1)
6.9 Exercises
159(6)
References
159(6)
Chapter 7 Interactive Data Visualization
165(26)
Chad A. Steed
7.1 Introduction
165(2)
7.2 Data Visualization for Intelligent Transportation Systems
167(1)
7.3 The Power of Data Visualization
167(2)
7.4 The Data Visualization Pipeline
169(2)
7.5 Classifying Data Visualization Systems
171(1)
7.6 Overview Strategies
172(3)
7.6.1 Data Quantity Reduction
173(1)
7.6.2 Miniaturizing Visual Glyphs
174(1)
7.7 Navigation Strategies
175(2)
7.7.1 Zoom and Pan
176(1)
7.7.2 Overview + Detail
176(1)
7.7.3 Focus + Context
177(1)
7.8 Visual Interaction Strategies
177(2)
7.8.1 Selecting
177(1)
7.8.2 Linking
178(1)
7.8.3 Filtering
178(1)
7.8.4 Rearranging and Remapping
179(1)
7.9 Principles for Designing Effective Data Visualizations
179(2)
7.10 A Case Study: Designing a Multivariate Visual Analytics Tool
181(4)
7.10.1 Multivariate Visualization Using Interactive Parallel Coordinates
182(1)
7.10.2 Dynamic Queries Through Direct Manipulation
182(1)
7.10.3 Dynamic Variable Summarization via Embedded Visualizations
183(1)
7.10.4 Multiple Coordinated Views
183(2)
7.11
Chapter Summary and Conclusions
185(1)
7.12 Exercises
186(1)
7.13 Sources for More Information
187(4)
7.13.1 Journals
187(1)
7.13.2 Conferences
187(1)
References
187(4)
Chapter 8 Data Analytics in Systems Engineering for Intelligent Transportation Systems
191(24)
Ethan T. McGee
John D. McGregor
8.1 Introduction
191(1)
8.2 Background
192(10)
8.2.1 Systems Development V Model
192(2)
8.2.2 Continuous Engineering
194(1)
8.2.3 AADL
195(7)
8.3 Development Scenario
202(7)
8.3.1 Data Analytics in Architecture
202(1)
8.3.2 The Scenario
203(6)
8.4 Summary and Conclusion
209(1)
8.5 Exercises
209(2)
8.6 Appendix A
211(4)
8.6.1 EMV2 Error Ontology
211(2)
References
213(2)
Chapter 9 Data Analytics for Safety Applications
215(26)
Yuanchang Xie
9.1 Introduction
215(1)
9.2 Overview of Safety Research
215(6)
9.2.1 Human Factors
215(1)
9.2.2 Crash Count/Frequency Modeling
216(1)
9.2.3 Before and After Study
217(1)
9.2.4 Crash Injury Severity Modeling
217(1)
9.2.5 Commercial Vehicle Safety
218(1)
9.2.6 Data Driven Highway Patrol Plan
218(1)
9.2.7 Deep Learning from Big and Heterogeneous Data for Safety
219(1)
9.2.8 Real-Time Traffic Operation and Safety Monitoring
219(1)
9.2.9 Connected Vehicles and Traffic Safety
220(1)
9.3 Safety Analysis Methods
221(6)
9.3.1 Statistical Methods
221(4)
9.3.2 Artificial Intelligence and Machine Learning
225(2)
9.4 Safety Data
227(6)
9.4.1 Crash Data
228(1)
9.4.2 Traffic Data
228(1)
9.4.3 Roadway Data
229(1)
9.4.4 Weather Data
230(1)
9.4.5 Vehicle and Driver Data
230(1)
9.4.6 Naturalistic Driving Study
230(1)
9.4.7 Big Data and Open Data Initiatives
231(2)
9.4.8 Other Data
233(1)
9.5 Issues and Future Directions
233(2)
9.5.1 Issues With Existing Safety Research
233(1)
9.5.2 Future Directions
234(1)
9.6
Chapter Summary and Conclusions
235(1)
9.7 Exercise Problems and Questions
236(5)
References
237(4)
Chapter 10 Data Analytics for Intermodal Freight Transportation Applications
241(22)
Nathan Huynh
Majbah Uddin
Chu Cong Minh
10.1 Introduction
241(1)
10.1.1 ITS-Enabled Intermodal Freight Transportation
241(1)
10.1.2 Data Analytics for ITS-Enabled Intermodal Freight Transportation
242(1)
10.2 Descriptive Data Analytics
242(7)
10.2.1 Univariate Analysis
242(5)
10.2.2 Bivariate Analysis
247(2)
10.3 Predictive Data Analytics
249(10)
10.3.1 Bivariate Analysis
249(4)
10.3.2 Multivariate Analysis
253(3)
10.3.3 Fuzzy Regression
256(3)
10.4 Summary and Conclusions
259(1)
10.5 Exercise Problems
260(1)
10.6 Solution to Exercise Problems
261(2)
References
261(2)
Chapter 11 Social Media Data in Transportation
263(20)
Sakib M. Khan
Linh B. Ngo
Eric A. Morris
Kakan Dey
Yan Zhou
11.1 Introduction to Social Media
263(1)
11.2 Social Media Data Characteristics
264(3)
11.2.1 Volume and Velocity
265(1)
11.2.2 Veracity
266(1)
11.2.3 Variety
266(1)
11.2.4 Value
266(1)
11.3 Social Media Data Analysis
267(3)
11.4 Application of Social Media Data in Transportation
270(2)
11.4.1 Transportation Planning
270(1)
11.4.2 Traffic Prediction
270(1)
11.4.3 Traffic Management During Planned Events
271(1)
11.4.4 Traffic Management During Unplanned Events
271(1)
11.4.5 Traffic Information Dissemination
272(1)
11.5 Future Research Issues/Challenges for Data Analytics-Enabled Social Media Data
272(5)
11.5.1 Social Media: A Supplemental Transportation Data Source
272(1)
11.5.2 Potential Data Infrastructure
273(4)
11.6 Summary
277(1)
11.7 Conclusions
277(1)
11.8 Exercise Problems
278(5)
References
278(5)
Chapter 12 Machine Learning in Transportation Data Analytics
283(21)
Parth Bhavsar
Ilya Safro
Nidhal Bouaynaya
Robi Polikar
Dimah Dera
12.1 Introduction
283(1)
12.2 Machine Learning Methods
284(2)
12.2.1 Supervised Learning
284(1)
12.2.2 Unsupervised Learning
285(1)
12.3 Understanding Data
286(4)
12.3.1 Problem Definition
286(1)
12.3.2 Data Collection
287(1)
12.3.3 Data Fusion
288(1)
12.3.4 Data Preprocessing
289(1)
12.4 Machine Learning Algorithms for Data Analytics
290(10)
12.4.1 Regression Methods
290(3)
12.4.2 Decision Trees
293(2)
12.4.3 Neural Networks
295(2)
12.4.4 Support Vector Machine
297(1)
12.4.5 Clustering
298(1)
12.4.6 Evaluation
299(1)
12.5 An Example
300(3)
12.6 Summary
303(1)
12.7 Questions and Solutions
303(1)
References 304(1)
Appendix 305(4)
Index 309
Mashrur Chowdhury is Eugene Douglas Mays Chaired Professor of Transportation in the Glenn Department of Civil Engineering at Clemson University. He is the Director of USDOT Center for Connected Multimodal Mobility and Co-Director of the Complex Systems, Analytics, and Visualization Institute at Clemson. His research focuses on connected and automated vehicles with an emphasis on their integration within smart cities. Dr. Amy Apon has been Professor and Chair of the Computer Science Division in the School of Computing at Clemson University since 2011. She was on leave from Clemson as a Program Officer in the Computer Network Systems Division of the National Science Foundation during 2015, working on research programs in Big Data, EXploiting Parallelism and Scalability, and Computer Systems Research. Apon established the High Performance Computing Center at the University of Arkansas and directed the center from 2005 to 2011. She has more than 100 scholarly publications in areas of cluster computing, performance analysis of high performance computing systems, and scalable data analytics. She is a Senior Member of the Association for Computing Machinery and a Senior Member of the Institute of Electrical and Electronics Engineers. Apon holds a Ph.D. in Computer Science from Vanderbilt University. Kakan Dey is Assistant Professor and Director of the Connected and Automated Transportation Systems (CATS) Lab at the West Virginia University. His primary research area is intelligent transportation systems, which include connected and automated vehicle technology, data science, cyber-physical systems, and smart cities.