LEADER 04236nam 2200901z- 450 001 9910557134703321 005 20231214132854.0 035 $a(CKB)5400000000040706 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68570 035 $a(EXLCZ)995400000000040706 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInternet of Things and Artificial Intelligence in Transportation Revolution 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (232 p.) 311 $a3-0365-0310-2 311 $a3-0365-0311-0 330 $aThe 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. 606 $aHistory of engineering & technology$2bicssc 610 $adecision-making 610 $aautonomous navigation 610 $acollision avoidance 610 $ascene division 610 $adeep reinforcement learning 610 $amaritime autonomous surface ships 610 $ainternet of things 610 $acrowdsourcing 610 $aindoor localization 610 $adata fusion 610 $asecurity 610 $aauthentication 610 $aInertial Measurement Units 610 $aroad transportation 610 $atraffic signal control 610 $aspeed guidance 610 $avehicle arrival time 610 $aconnected vehicle 610 $aunmanned ships 610 $aDDPG 610 $aautonomous path planning 610 $aend-to-end 610 $aat-risk driving 610 $adeep support vector machine 610 $adriver drowsiness 610 $adriver stress 610 $amulti-objective genetic algorithm 610 $amultiple kernel learning 610 $aurban freeway 610 $ahybrid dynamic system 610 $astate transition 610 $aunknown inputs observer 610 $avehicle density 610 $amaritime vessel flows 610 $aintelligent transportation systems 610 $adeep learning 610 $aautomatic license plate recognition 610 $aintelligent vehicle access 610 $ahistogram of oriented gradients 610 $aartificial neural networks 610 $aconvolutional neural networks 610 $atime-frequency 610 $aInertial Measurement Unit (IMU) 610 $aroad anomalies 615 7$aHistory of engineering & technology 700 $aLytras$b Miltiadis$4edt$01149833 702 $aChui$b Kwok Tai$4edt 702 $aLiu$b Ryan Wen$4edt 702 $aLytras$b Miltiadis$4oth 702 $aChui$b Kwok Tai$4oth 702 $aLiu$b Ryan Wen$4oth 906 $aBOOK 912 $a9910557134703321 996 $aInternet of Things and Artificial Intelligence in Transportation Revolution$93037427 997 $aUNINA