Deep learning and big data for intelligent transportation : enabling technologies and future trends / Khaled R. Ahmed, Aboul Ella Hassanien, editors.
Ahmed, Khaled R., editor.; Hassanien, Aboul Ella, editor.
2021
TE228.3
Available Online

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Details

Title
Deep learning and big data for intelligent transportation : enabling technologies and future trends / Khaled R. Ahmed, Aboul Ella Hassanien, editors.
ISBN
9783030656614 (electronic bk.)
3030656616 (electronic bk.)
9783030656607
DOI
https://doi.org/10.1007/978-3-030-65661-4
Published
Cham : Springer, [2021]
Language
English
Description
1 online resource (x, 264 pages) : illustrations (some color)
Item Number
10.1007/978-3-030-65661-4 doi
Call Number
TE228.3
Dewey Decimal Classification
388.3/12
Summary
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.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed April 14, 2021).
Added Author
Ahmed, Khaled R., editor.
Hassanien, Aboul Ella, editor.
Series
Studies in computational intelligence ; v. 945. 1860-949X
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Part I: Big Data and Autonomous Vehicles
Big Data Technologies with Computanational Model Computing using HADOOP with Scheduling Challeges
Big Data for Autonomous Vehicles
Part II: Deep Learning & Object detection for Safe driving
Analysis of Target Detection and Tracking for Intelligent Vision System
Enhanced end-to-end system for autonomous driving using deep convolutional networks.-Deep Learning Technologies to mitigate Deer-Vehicle Collisions
Night-to-Day Road Scene Translation Using Generative Adversarial Network with Structural Similarity Loss for Night Driving Safety
Safer-Driving: Application of Deep Transfer Learning to Build Intelligent Transportation Systems
Leveraging CNN Deep Learning Model for Smart Parking
Estimating Crowd Size for Public Place Surveillance using Deep Learning
Part III: AI & IoT for intelligent transportation
IoT Based Regional Speed Restriction Using Smart Sign Boards
Synergy of Internet of Things with Cloud, Artificial Intelligence and Blockchain for Empowering Autonomous Vehicles
Combining Artificial Intelligence with Robotic Process Automation
An Intelligent Automation Approach.

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