Internet of Things and Artificial Intelligence in Transportation Revolution

The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have s...

Full description

Bibliographic Details
Main Author: Lytras, Miltiadis
Other Authors: Chui, Kwok Tai, Liu, Ryan Wen
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
N/a
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
LEADER 04264nma a2200901 u 4500
001 EB001991865
003 EBX01000000000000001154767
005 00000000000000.0
007 cr|||||||||||||||||||||
008 210512 ||| eng
020 |a 9783036503110 
020 |a books978-3-0365-0311-0 
020 |a 9783036503103 
100 1 |a Lytras, Miltiadis 
245 0 0 |a Internet of Things and Artificial Intelligence in Transportation Revolution  |h Elektronische Ressource 
260 |a Basel, Switzerland  |b MDPI - Multidisciplinary Digital Publishing Institute  |c 2021 
300 |a 1 electronic resource (232 p.) 
653 |a at-risk driving 
653 |a automatic license plate recognition 
653 |a unknown inputs observer 
653 |a maritime vessel flows 
653 |a crowdsourcing 
653 |a driver drowsiness 
653 |a multiple kernel learning 
653 |a n/a 
653 |a vehicle arrival time 
653 |a data fusion 
653 |a deep learning 
653 |a DDPG 
653 |a state transition 
653 |a History of engineering and technology / bicssc 
653 |a decision-making 
653 |a road transportation 
653 |a Inertial Measurement Units 
653 |a end-to-end 
653 |a urban freeway 
653 |a driver stress 
653 |a deep reinforcement learning 
653 |a autonomous path planning 
653 |a histogram of oriented gradients 
653 |a vehicle density 
653 |a scene division 
653 |a collision avoidance 
653 |a hybrid dynamic system 
653 |a traffic signal control 
653 |a indoor localization 
653 |a autonomous navigation 
653 |a security 
653 |a convolutional neural networks 
653 |a authentication 
653 |a deep support vector machine 
653 |a intelligent transportation systems 
653 |a internet of things 
653 |a road anomalies 
653 |a connected vehicle 
653 |a intelligent vehicle access 
653 |a multi-objective genetic algorithm 
653 |a unmanned ships 
653 |a time-frequency 
653 |a artificial neural networks 
653 |a speed guidance 
653 |a maritime autonomous surface ships 
653 |a Inertial Measurement Unit (IMU) 
700 1 |a Chui, Kwok Tai 
700 1 |a Liu, Ryan Wen 
700 1 |a Lytras, Miltiadis 
041 0 7 |a eng  |2 ISO 639-2 
989 |b DOAB  |a Directory of Open Access Books 
500 |a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/ 
028 5 0 |a 10.3390/books978-3-0365-0311-0 
856 4 0 |u https://www.mdpi.com/books/pdfview/book/3591  |7 0  |x Verlag  |3 Volltext 
856 4 2 |u https://directory.doabooks.org/handle/20.500.12854/68570  |z DOAB: description of the publication 
082 0 |a 900 
082 0 |a 380 
082 0 |a 700 
082 0 |a 600 
082 0 |a 620 
520 |a The advent of Internet of Things offers a scalable and seamless connection of physical objects, including human beings and devices. This, along with artificial intelligence, has moved transportation towards becoming intelligent transportation. This book is a collection of eleven articles that have served as examples of the success of internet of things and artificial intelligence deployment in transportation research. Topics include collision avoidance for surface ships, indoor localization, vehicle authentication, traffic signal control, path-planning of unmanned ships, driver drowsiness and stress detection, vehicle density estimation, maritime vessel flow forecast, and vehicle license plate recognition. High-performance computing services have become more affordable in recent years, which triggered the adoption of deep-learning-based approaches to increase the performance standards of artificial intelligence models. Nevertheless, it has been pointed out by various researchers that traditional shallow-learning-based approaches usually have an advantage in applications with small datasets. The book can provide information to government officials, researchers, and practitioners. In each article, the authors have summarized the limitations of existing works and offered valuable information on future research directions.