Railway systems have a long history of train protection and control, as to reduce the risk of train accidents. Many train control systems include automated communication between train and trackside equipment. But several different national systems are still facing cross-border rail traffic. Today, trains for cross-border traffic need to be equipped with train control systems that are installed on the tracks.
This book covers the latest advances in Communication Based Train Control (CBTC) research in on-board components locomotive messaging systems, GPS sensors, communications wayside and switching networks. It also focuses on architecture and methodology using data fusion techniques. New wireless sensor integrated modeling techniques for tracking trains in satellite visible and low satellite visible environments are discussed. With a Tunnel Surveillance Integration model, the use of optimal control is necessary to improve train control performance, considering both train–ground communication and train control.
The book begins with the background and evolution of train signaling and train control systems. It introduces the main features and architecture of CBTC systems and describes current challenging methods and successful implementations.
This introductory book is very useful for Signal & Telecommunication engineers to get them acquainted with the technology used in CBTC, and help them in implementing the system suitable for Indian Railways. As this is a new technology, the information provided in this book is generic and will be subsequently revised after gaining further experience.
1. Vision of Intelligent Control and Tracking Rail System: Global Evident Data 2. Train Navigation Control and Information Management System 3. Hybrid System for Train Tracking and Monitoring Model 4. Locomotive Tracking in SatelliteVisible And Low Satellite Visible Area 5. Train Trajectory Optimization Based On Di-Filter Theory 6. Heterogeneous Sensor Data Fusion DGPS-WSN-RFID-Based Train Tracking Model 7. Wireless Locomotive Real-Time Surveillance Model 8. Predictive Analysis of Intelligent Rail Trip Detection Service Using Machine Learning