Deep Learning and Big Data for Intelligent Transportation - Enabling Technologies and Future Trends (Paperback, 1st ed. 2021)

This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today's technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle's speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 945
Release date:	April 2022
First published:	2021
Editors:	Khaled R. Ahmed • Aboul Ella Hassanien
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	264
Edition:	1st ed. 2021
ISBN-13:	978-3-03-065663-8
Categories:	Books > Professional & Technical > Civil engineering, surveying & building > Highway & traffic engineering Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-03-065663-2
Barcode:	9783030656638

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