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This work presents a full generic approach to the detection and recognition of traffic signs. The approach, originally developed for a mobile mapping application, is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categorization, the text focuses on the problem of detection, and presents recent developments in this field. The text then surveys a specific methodology for the problem of traffic sign categorization – Error-Correcting Output Codes – and presents several algorithms, performing experimental validation on a mobile mapping application. The work ends with a discussion on future lines of research, and continuing challenges for traffic sign recognition.

Image processing and computer vision. --- Pattern recognition systems. --- Traffic signs and signals -- Data processing. --- Computer vision --- Pattern recognition systems --- Traffic signs and signals --- Driver assistance systems --- Engineering & Applied Sciences --- Civil & Environmental Engineering --- Applied Physics --- Transportation Engineering --- Data processing --- Control systems. --- Traffic signal control systems --- Computer science. --- Image processing. --- Computer Science. --- Image Processing and Computer Vision. --- Sociotechnical systems --- Computer vision. --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks.

Film, TV & radio --- flying ad hoc network (FANET) --- mobile ad hoc network (MANET) --- highly dynamic --- periodical --- topology change awareness --- routing protocol --- mobility management --- architecture --- implementation --- experimentation --- EU-Korea testbed --- SDN --- DASH --- QoE --- HTTP/3 --- QUIC --- traffic signal control --- shared lane --- control strategy --- vehicle-to-infrastructure --- variable lane line --- cloud computing --- management --- middle box --- placement --- resource --- vendor-agnostic --- virtual machine --- VM --- affordable playground --- hyper-converged SmartX Box --- distributed resources --- multi-site and virtualized cloud --- software-defined networking --- DevOps automation --- ILNP --- identifier-locator --- mobile network --- roaming --- flying ad hoc network (FANET) --- mobile ad hoc network (MANET) --- highly dynamic --- periodical --- topology change awareness --- routing protocol --- mobility management --- architecture --- implementation --- experimentation --- EU-Korea testbed --- SDN --- DASH --- QoE --- HTTP/3 --- QUIC --- traffic signal control --- shared lane --- control strategy --- vehicle-to-infrastructure --- variable lane line --- cloud computing --- management --- middle box --- placement --- resource --- vendor-agnostic --- virtual machine --- VM --- affordable playground --- hyper-converged SmartX Box --- distributed resources --- multi-site and virtualized cloud --- software-defined networking --- DevOps automation --- ILNP --- identifier-locator --- mobile network --- roaming

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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.

History of engineering & technology --- decision-making --- autonomous navigation --- collision avoidance --- scene division --- deep reinforcement learning --- maritime autonomous surface ships --- internet of things --- crowdsourcing --- indoor localization --- data fusion --- security --- authentication --- Inertial Measurement Units --- road transportation --- traffic signal control --- speed guidance --- vehicle arrival time --- connected vehicle --- unmanned ships --- DDPG --- autonomous path planning --- end-to-end --- at-risk driving --- deep support vector machine --- driver drowsiness --- driver stress --- multi-objective genetic algorithm --- multiple kernel learning --- urban freeway --- hybrid dynamic system --- state transition --- unknown inputs observer --- vehicle density --- maritime vessel flows --- intelligent transportation systems --- deep learning --- automatic license plate recognition --- intelligent vehicle access --- histogram of oriented gradients --- artificial neural networks --- convolutional neural networks --- time-frequency --- Inertial Measurement Unit (IMU) --- road anomalies --- n/a

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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.

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 --- 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

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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.

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