Innovative Applications of Big Data in the Railway Industry

Use of big data has proven to be beneficial within many different industries, especially in the field of engineering; however, infiltration of this type of technology into more traditional heavy industries, such as the railways, has been limited.

Innovative Applications of Big Data in the Railway Industry is a pivotal reference source for the latest research findings on the utilization of data sets in the railway industry. Featuring extensive coverage on relevant areas such as driver support systems, railway safety management, and obstacle detection, this publication is an ideal resource for transportation planners, engineers, policymakers, and graduate-level engineering students seeking current research on a specific application of big data and its effects on transportation.

Table of Contents

Section 1: Concepts and Approaches
Chapter 1: Big Data in Railway O&M
Chapter 2: Blockchains
Chapter 3: Visual and LIDAR Data Processing and Fusion as an Element of Real Time Big Data Analysis for Rail Vehicle Driver Support Systems
Chapter 4: Wayside Train Monitoring Systems

Section 2: Innovations and Technologies
Chapter 5: Scalable Software Framework for Real-Time Data Processing in the Railway Environment
Chapter 6: Predicting Behavior of Passengers Using Data Collected Through Smart Cards
Chapter 7: Dynamic Behavior Analysis of Railway Passengers
Chapter 8: Intelligent Transport Systems Services in VANETs and Case Study in Urban Environment

Section 3: Big Data and Text Mining
Chapter 9: Study and Analysis of Delay Factors of Delhi Metro Using Data Sciences and Social Media
Chapter 10: Social Media as a Tool to Understand Behaviour on the Railways
Chapter 11: Big Data and Natural Language Processing for Analysing Railway Safety

Section 4: Applications and Use Cases
Chapter 12: Evolution of Indian Railways Through IoT
Chapter 13: Application of Big Data Technologies for Quantifying the Key Factors Impacting Passenger Journey in a Multi-Modal Transportation Environment
Chapter 14: Big Data Analytics for Train Delay Prediction