Intelligent Transportation Related Complex Systems and Sensors

• Autore: Kyamakya Kyandoghere
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (494 p.)
• Soggetto topico: Technology: general issues
• Soggetto non controllato: abnormal road surface; acceleration sensor; adaptive feature extractor; advanced traveler information systems (ATIS); amplitude modulation; artificial neural networks; artificial potential field; automatic rail-surface-scratch recognition and computation; autonomous vehicle; bicycle sharing systems; BSS underlying network; complete closed mesh model; connected vehicles; contrast enhancement; convolutional neural networks; crash risk modeling; dark channel prior; data visualization; data-driven classification of trips; decentralized control; deep neural network; descriptive analytics; driving emotions; driving safety; driving simulator studies; driving stress; EKF; expectation maximization; frequency modulation; game theory; Gaussian background model; hazardous materials; heart rate; highway safety; human-like; hyper parameter optimization; image dehazing; in-cylinder pressure identification; intelligent transportation systems; Intelligent Transportation Systems (ITS); ITS; ITS services; Kalman filter; large-scale network control; level of market penetration rate; license plate data; lifestyle; low-cost sensor; machine learning; matrix inversion; metro; microscopic traffic data; model predictive control; n/a; neural artistic extraction; neural network; noise problem in time-varying matrix inversion; objectification; obstacle avoidance; online rail-repair; operations research; particle filter; plate scanning; predictive analytics; prescriptive analytics; probe vehicle; public transport systems; railway intrusion detection; railway maintenance; real world experiment; real-time estimation; real-time fast computing; recurrent neural network (RNN); ride comfort; RNN-based solver; road safety; road surface recognition; route choice behavior; safety performance function; scene recognition; scene segmentation; sensor location problem; sensor synchronization; sensors; shortest path problem; simulation; smartphone sensing; spatial-temporal correlation; spatio-temporal traffic modeling; speed iteration model; subjective evaluation; surrogate safety measures; time-varying matrix; traffic density; traffic flow estimation; traffic modelling; traffic signal control; traffic state prediction; traffic video dehazing; trajectory reconstruction; transportation; travel time information system; triangulation algorithm; trip chain; trip index; trucking; turning state transit; urban rail transit interior noise; user flow forecast; variable speed limits; vehicle matching; vehicle routing problem; vehicle trajectory prediction; XGBoost classifier
• Persona (resp. second.): Al-MachotFadi; MosaAhmad Haj; ChedjouJean Chamberlain; BagulaAntoine; KyamakyaKyandoghere
• Sommario/riassunto: 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 coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data.
• Titolo autorizzato: Intelligent Transportation Related Complex Systems and Sensors
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557345603321
• Lo trovi qui: Univ. Federico II