A comprehensive introduction to the theory and practice of
contemporary data science analysis for railway track engineering
Featuring a practical introduction to state-of-the-art data analysis for
railway track engineering, Big Data and Differential Privacy: Analysis
Strategies for Railway Track Engineering addresses common issues with
the implementation of big data applications while exploring the
limitations, advantages, and disadvantages of more conventional methods.
In addition, the book provides a unifying approach to analyzing large
volumes of data in railway track engineering using an array of proven
methods and software technologies.
Dr. Attoh-Okine considers some of today's most notable applications and
implementations and highlights when a particular method or algorithm is
most appropriate. Throughout, the book presents numerous real-world
examples to illustrate the latest railway engineering big data
applications of predictive analytics, such as the Union Pacific
Railroad's use of big data to reduce train derailments, increase the
velocity of shipments, and reduce emissions.
In addition to providing an overview of the latest software tools used
to analyze the large amount of data obtained by railways, Big Data and
Differential Privacy: Analysis Strategies for Railway Track Engineering
- Features a unified framework for handling large volumes of data in
railway track engineering using predictive analytics, machine learning,
and data mining
- Explores issues of big data and differential privacy and discusses
the various advantages and disadvantages of more conventional data
analysis techniques
- Implements big data applications while addressing common issues in
railway track maintenance
- Explores the advantages and pitfalls of data analysis software such
as R and Spark, as well as the Apache(TM) Hadoop(R) data collection
database and its popular implementation MapReduce
Big Data and Differential Privacy is a valuable resource for
researchers and professionals in transportation science, railway track
engineering, design engineering, operations research, and railway
planning and management. The book is also appropriate for graduate
courses on data analysis and data mining, transportation science,
operations research, and infrastructure management.
NII ATTOH-OKINE, PhD, PE is Professor in the Department of Civil and
Environmental Engineering at the University of Delaware. The author of
over 70 journal articles, his main areas of research include big data
and data science; computational intelligence; graphical models and
belief functions; civil infrastructure systems; image and signal
processing; resilience engineering; and railway track analysis. Dr.
Attoh-Okine has edited five books in the areas of computational
intelligence, infrastructure systems and has served as an Associate
Editor of various ASCE and IEEE journals.
