Data Analytics for Intelligent Transportation Systems / edited by Mashrur Chowdhury, Kakan Dey, and Amy Apon.

Format:

Book

Contributor:

Chowdhury. Mashrur, editor.

Dey, Kakan, editor.

Apon, Amy, editor.

Language:

English

Subjects (All):

Intelligent transportation systems--Data processing.

Intelligent transportation systems.

Physical Description:

1 online resource (xxiv, 448 pages)

Edition:

Second edition.

Place of Publication:

Amsterdam, Netherlands : Elsevier, [2025]

Summary:

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

Contents:

Front Cover

Data Analytics for Intelligent Transportation Systems

Copyright Page

Dedication

Contents

List of contributors

Preface

1 Characteristics of intelligent transportation systems and its relationship with data analytics

1.1 Intelligent transportation systems as data-intensive applications

1.1.1 Intelligent transportation system data system

1.1.2 Intelligent transportation system data sources and data collection technologies

1.2 Big Data analytics and infrastructure to support intelligent transportation system

1.3 Intelligent transportation system architecture: the framework of intelligent transportation system applications

1.3.1 Enterprise view

1.3.2 Functional view

1.3.3 Physical view

1.3.4 Communication view

1.3.5 Service packages

1.3.6 Security

1.4 Overview of intelligent transportation system applications

1.4.1 Types of intelligent transportation system applications

1.4.2 Intelligent transportation system application and its relationship to data analytics

1.5 Intelligent transportation systems past, present, and future

1.5.1 1960s and 1970s

1.5.2 1980s and 1990s

1.5.3 2000s

1.5.4 2010s and beyond

1.6 Overview of book: data analytics for intelligent transportation system 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 Correlation coefficient

2.2.3.2 Linear regression

2.2.3.3 Predictive analytics use cases

2.2.4 Prescriptive analytics

2.3 Evolution of data analytics

2.3.1 SQL analytics: relational database management systems, online transaction processing, and online analytic processing

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 online analytic processing cubes

2.3.2.3 Extract, transform, and load tools

2.3.2.4 Online analytic processing servers

2.3.2.5 Data mining

2.3.2.5.1 Frequent patterns

2.3.2.5.2 Classification

2.3.2.5.3 Cluster analysis

2.3.2.5.4 Outlier detection

2.3.2.5.5 Evolution analysis

2.3.2.5.6 Graph data mining application

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 Recent advances and trends in data analytics

2.7 Conclusion

2.8 Questions and exercise problems

3 Data science tools and techniques to support data analytics in transportation applications

3.1 Introduction to the R programming environment for data analytics

3.2 Research data exchange

3.3 Fundamental data types and structures: data frames and list

3.3.1 Data frame

3.3.2 List

3.4 Importing data from external files

3.4.1 Delimited

3.4.2 XML

3.4.3 SQL

3.5 Ingesting online social media data

3.5.1 Static search

3.5.2 Dynamic streaming

3.6 Data mining and machine learning techniques and libraries in Python

3.6.1 Python libraries for data analytics

3.6.2 Unsupervised learning with clustering

3.6.3 Supervised learning with regression.

3.6.4 Supervised learning with classification

3.7 Big data processing: Hadoop MapReduce

3.8 Summary

Exercises

4 Lifecycle and data pipelines: the centrality of data

4.1 Introduction

4.2 Use cases and data variability

4.3 Data and its life cycle

4.3.1 The USGS life cycle model

4.3.2 Digital Curation Center (DCC) curation model

4.3.3 Dataone model

4.3.4 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

5 Data infrastructure for connected transport 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 Scalable data processing

5.4.2 SQL

5.4.3 Dataframes

5.4.4 Machine learning

5.4.5 Foundation models

5.4.6 Data ingest and stream processing

5.4.7 Short-running and random access data management

5.4.8 Search-based analytics

5.4.9 Business intelligence and visualizations

5.5 Low-level infrastructure

5.5.1 On-premise: Hadoop and Kubernetes

5.5.2 Cloud

5.5.3 Edge-to-cloud continuum

5.5.4 Vehicles and roadside sensors

5.6 Quantum infrastructure

5.6.1 Quantum annealing

5.6.2 Quantum computing

5.6.3 Quantum communication

5.7 Conclusion

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 Electronic control unit attacks

6.5.2.2.1 Fuzzing techniques for controller area network versus robot operating systems

6.5.2.3 Tire pressure monitoring system attacks

6.5.2.4 Vehicular ad hoc networks/vehicle-to-everything attacks

6.6 Security and privacy solutions

6.6.1 Cryptography basics

6.6.2 Security solutions for automotive 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 Wireless personal area networks security and privacy

6.6.3.1 Bluetooth security checklist

6.6.3.2 Secure wireless personal area networks

6.6.3.3 Enabling data privacy in wireless personal area network

6.6.4 Secure vehicular ad hoc networks

6.6.5 Secure over-the-air electronic control unit firmware update

6.6.6 Privacy measurement of sensor data

6.6.7 Secure handover

6.7 Future research directions and conclusions

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 items of interest

7.8.2 Linking selections in multiple views

7.8.3 Filtering data.

7.8.4 Rearranging and remapping views

7.9 Principles for designing effective data visualizations

7.10 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 summaries via embedded visualizations

7.10.4 Multiple coordinated views

7.11 Chapter summary and conclusions

Sources for more information

Journals

Conferences

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 (CE)

8.2.3 Architecture Analysis &amp

Design Language

8.2.3.1 Language overview

8.2.3.2 Behavior annex

8.2.3.3 Error annex

8.2.3.4 Assume guarantee reasoning environment

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

Appendix A

EMV2 error ontology

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 for modeling big and heterogeneous safety data

9.2.8 Real-time traffic operation and safety monitoring

9.3 Connected vehicles and traffic safety

9.3.1 Surrogate safety measures

9.3.2 Safety analysis methods.

9.4 Statistical methods.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9780443138782

0443138788

OCLC:

1472979678