Call for Book Chapters

Book Title: Deep Learning and Big Data for Intelligent Transportation: Enabling Technologies and Future Trends

Published by Springer, Studies in Computational Intelligence series in Year 2020

Deep learning and big data are very dynamic, grooming and important research topics of today’s technology. They are contributing to the progress towards intelligent transportation such as fully autonomous vehicles. Transportation generated massive amount of data collected from multiple sources including road sensors, UAVs, probe, GPS, CCTV and incident reports. The collected data are highly needed to make serious traffic decisions such as rerouting, safe-driving decision, etc. With this rich volume and velocity of data, it is challenging to build reliable prediction models based on traditional relational database and machine learning methods. Recently, big data, deep learning and reinforcement learning are new state-of-the-art data management and machine learning approaches which have been of great interest in both academic research and industrial applications.
In general, the use of big data, deep learning and reinforcement learning in transportation is still limited. The main aim of this book is to encourage recent studies of deep learning and reinforcement learning for intelligent transportation and focus on popular topics including processing traffic data, transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviors. It is expected the research submitted to this book will answer the following question: How big data and deep learning should be used to build intelligent transportation systems to achieve safety and optimize performance and economy?
Target Audience:
This book would serve a broad audience including researchers, academicians, students and working professional in the field of utilities, manufacturing, health, environmental services, government, defense and networking companies.
Recommended Topics:
 Big data Technologies
Deep Learning Techniques
Big data, Deep learning, Safety in surveillance applications
IoT-driven intelligence and incorporate deep learning models
 Big data and autonomous vehicles
 Deep learning for transportation models
 Reinforcement learning for intelligent transportation
 Detection of Vulnerable Road Users and Animals Air, Road, and Rail
 Deep learning models for achieving pedestrians and cyclist safety
 Practical issues in building Safe transports applications
 Vision, Image Processing and Environment Perception
 Vehicle localization and autonomous navigation
 Vehicle Platooning and Automated Highways
 Performance and Traffic Management Issues
 Intelligent Automation
 Operational and Policy issues in Automation
 Cyber-physical transportation systems
 Advanced Public Transportation Management
 Air, Road, and Rail Traffic Management
 Smart Driver and Traveler Support Systems
 Big Data & Vehicle Analytics
 Big Data Analytics for Intelligent Transportation
 Big Data and Naturalistic Datasets
 Infrastructure and Platform for Big Data and Intelligent transportation

Submission Procedure
All book chapters proposal must be electronically submitted by using Easychair link below, following these guidelines:
• Researchers and practitioners are kindly invited to submit full chapter.
• The length of the book chapter should be between 15 to 20 pages (including reference).
• All submitted chapters will be reviewed by at least two reviewers on a double-blind review basis
• Submission link:


https://easychair.org/conferences/?conf=dlits2020
Important Dates:
August 31, 2020: Full chapter submission due
Sept 16, 2020: Review results including notification of acceptance of chapter
Sept 30, 2020: Camera ready submission due