Intelligent Transportation Related Complex Systems and Sensors
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...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (494 p.) |
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| 100 | 1 | |a Kyamakya, Kyandoghere |4 edt | |
| 245 | 1 | 0 | |a Intelligent Transportation Related Complex Systems and Sensors |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (494 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 653 | |a image dehazing | ||
| 653 | |a traffic video dehazing | ||
| 653 | |a dark channel prior | ||
| 653 | |a spatial-temporal correlation | ||
| 653 | |a contrast enhancement | ||
| 653 | |a traffic signal control | ||
| 653 | |a game theory | ||
| 653 | |a decentralized control | ||
| 653 | |a large-scale network control | ||
| 653 | |a railway intrusion detection | ||
| 653 | |a scene segmentation | ||
| 653 | |a scene recognition | ||
| 653 | |a adaptive feature extractor | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a in-cylinder pressure identification | ||
| 653 | |a speed iteration model | ||
| 653 | |a EKF | ||
| 653 | |a frequency modulation | ||
| 653 | |a amplitude modulation | ||
| 653 | |a sensor synchronization | ||
| 653 | |a microscopic traffic data | ||
| 653 | |a trajectory reconstruction | ||
| 653 | |a expectation maximization | ||
| 653 | |a vehicle matching | ||
| 653 | |a artificial neural networks | ||
| 653 | |a metro | ||
| 653 | |a transportation | ||
| 653 | |a user flow forecast | ||
| 653 | |a matrix inversion | ||
| 653 | |a time-varying matrix | ||
| 653 | |a noise problem in time-varying matrix inversion | ||
| 653 | |a recurrent neural network (RNN) | ||
| 653 | |a RNN-based solver | ||
| 653 | |a real-time fast computing | ||
| 653 | |a real-time estimation | ||
| 653 | |a probe vehicle | ||
| 653 | |a traffic density | ||
| 653 | |a neural network | ||
| 653 | |a level of market penetration rate | ||
| 653 | |a deep neural network | ||
| 653 | |a neural artistic extraction | ||
| 653 | |a objectification | ||
| 653 | |a ride comfort | ||
| 653 | |a subjective evaluation | ||
| 653 | |a road surface recognition | ||
| 653 | |a Gaussian background model | ||
| 653 | |a abnormal road surface | ||
| 653 | |a acceleration sensor | ||
| 653 | |a traffic state prediction | ||
| 653 | |a spatio-temporal traffic modeling | ||
| 653 | |a simulation | ||
| 653 | |a machine learning | ||
| 653 | |a hyper parameter optimization | ||
| 653 | |a ITS | ||
| 653 | |a crash risk modeling | ||
| 653 | |a hazardous materials | ||
| 653 | |a highway safety | ||
| 653 | |a operations research | ||
| 653 | |a prescriptive analytics | ||
| 653 | |a shortest path problem | ||
| 653 | |a trucking | ||
| 653 | |a vehicle routing problem | ||
| 653 | |a data visualization | ||
| 653 | |a descriptive analytics | ||
| 653 | |a predictive analytics | ||
| 653 | |a urban rail transit interior noise | ||
| 653 | |a smartphone sensing | ||
| 653 | |a XGBoost classifier | ||
| 653 | |a railway maintenance | ||
| 653 | |a vehicle trajectory prediction | ||
| 653 | |a license plate data | ||
| 653 | |a trip chain | ||
| 653 | |a turning state transit | ||
| 653 | |a route choice behavior | ||
| 653 | |a real world experiment | ||
| 653 | |a Intelligent Transportation Systems (ITS) | ||
| 653 | |a advanced traveler information systems (ATIS) | ||
| 653 | |a connected vehicles | ||
| 653 | |a particle filter | ||
| 653 | |a Kalman filter | ||
| 653 | |a road safety | ||
| 653 | |a travel time information system | ||
| 653 | |a safety performance function | ||
| 653 | |a bicycle sharing systems | ||
| 653 | |a public transport systems | ||
| 653 | |a data-driven classification of trips | ||
| 653 | |a BSS underlying network | ||
| 653 | |a trip index | ||
| 653 | |a automatic rail-surface-scratch recognition and computation | ||
| 653 | |a triangulation algorithm | ||
| 653 | |a complete closed mesh model | ||
| 653 | |a online rail-repair | ||
| 653 | |a autonomous vehicle | ||
| 653 | |a obstacle avoidance | ||
| 653 | |a artificial potential field | ||
| 653 | |a model predictive control | ||
| 653 | |a human-like | ||
| 653 | |a variable speed limits | ||
| 653 | |a intelligent transportation systems | ||
| 653 | |a ITS services | ||
| 653 | |a driving simulator studies | ||
| 653 | |a traffic modelling | ||
| 653 | |a surrogate safety measures | ||
| 653 | |a driving safety | ||
| 653 | |a driving emotions | ||
| 653 | |a driving stress | ||
| 653 | |a lifestyle | ||
| 653 | |a sensors | ||
| 653 | |a heart rate | ||
| 653 | |a plate scanning | ||
| 653 | |a low-cost sensor | ||
| 653 | |a sensor location problem | ||
| 653 | |a traffic flow estimation | ||
| 776 | |z 3-0365-0848-1 | ||
| 776 | |z 3-0365-0849-X | ||
| 700 | 1 | |a Al-Machot, Fadi |4 edt | |
| 700 | 1 | |a Mosa, Ahmad Haj |4 edt | |
| 700 | 1 | |a Chedjou, Jean Chamberlain |4 edt | |
| 700 | 1 | |a Bagula, Antoine |4 edt | |
| 700 | 1 | |a Kyamakya, Kyandoghere |4 oth | |
| 700 | 1 | |a Al-Machot, Fadi |4 oth | |
| 700 | 1 | |a Mosa, Ahmad Haj |4 oth | |
| 700 | 1 | |a Chedjou, Jean Chamberlain |4 oth | |
| 700 | 1 | |a Bagula, Antoine |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 06:02:17 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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