|
|
|
|
| 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.
|