Intelligent Transportation Related Complex Systems and Sensors

• Muut tekijät: Kyamakya, Kyandoghere (Toimittaja), Al-Machot, Fadi (Toimittaja), Mosa, Ahmad Haj (Toimittaja), Chedjou, Jean Chamberlain (Toimittaja), Bagula, Antoine (Toimittaja)
• Aineistotyyppi: Elektroninen Kirjan osa
• Kieli: englanti
• Julkaistu: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Aiheet: Technology: general issues; image dehazing; traffic video dehazing; dark channel prior; spatial-temporal correlation; contrast enhancement; traffic signal control; game theory; decentralized control; large-scale network control; railway intrusion detection; scene segmentation; scene recognition; adaptive feature extractor; convolutional neural networks; in-cylinder pressure identification; speed iteration model; EKF; frequency modulation; amplitude modulation; sensor synchronization; microscopic traffic data; trajectory reconstruction; expectation maximization; vehicle matching; artificial neural networks; metro; transportation; user flow forecast; matrix inversion; time-varying matrix; noise problem in time-varying matrix inversion; recurrent neural network (RNN); RNN-based solver; real-time fast computing; real-time estimation; probe vehicle; traffic density; neural network; level of market penetration rate; deep neural network; neural artistic extraction; objectification; ride comfort; subjective evaluation; road surface recognition; Gaussian background model; abnormal road surface; acceleration sensor; traffic state prediction; spatio-temporal traffic modeling; simulation; machine learning; hyper parameter optimization; ITS; crash risk modeling; hazardous materials; highway safety; operations research; prescriptive analytics; shortest path problem; trucking; vehicle routing problem; data visualization; descriptive analytics; predictive analytics; urban rail transit interior noise; smartphone sensing; XGBoost classifier; railway maintenance; vehicle trajectory prediction; license plate data; trip chain; turning state transit; route choice behavior; real world experiment; Intelligent Transportation Systems (ITS); advanced traveler information systems (ATIS); connected vehicles; particle filter; Kalman filter; road safety; travel time information system; safety performance function; bicycle sharing systems; public transport systems; data-driven classification of trips; BSS underlying network; trip index; automatic rail-surface-scratch recognition and computation; triangulation algorithm; complete closed mesh model; online rail-repair; autonomous vehicle; obstacle avoidance; artificial potential field; model predictive control; human-like; variable speed limits; intelligent transportation systems; ITS services; driving simulator studies; traffic modelling; surrogate safety measures; driving safety; driving emotions; driving stress; lifestyle; sensors; heart rate; plate scanning; low-cost sensor; sensor location problem; traffic flow estimation; n/a
• Linkit: DOAB: download the publication; DOAB: description of the publication

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more c...