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This book presents the new development of computation intelligence for traffic, transportation and mobility, the main contents include traffic safety, mobility analysis, intelligent transportation system, smart vehicle, transportation behavior, driver modeling and assistance, transportation risk analysis and reliability system analysis, vehicle operation and active safety, urban traffic management and planning.
Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword ``urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443 (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS. This means that there is a big and diverse community of computer scientists and computer engineers who tackle research that is connected to the development of intelligent traffic and transportation systems. It is also possible to see that this community is growing, and that research projects are getting more and more interdisciplinary. To foster the cooperation among the involved communities, this book aims at giving a broad introduction into the basic but relevant concepts related to transportation systems, targeting researchers and practitioners from computer science and information technology. In addition, the second part of the book gives a panorama of some of the most exciting and newest technologies, originating in computer science and computer engineering, that are now being employed in projects related to car-to-car communication, interconnected vehicles, car navigation, platooning, crowd sensing and sensor networks, among others. This material will also be of interest to engineers and researchers from the traffic and transportation community.
New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas. This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends. Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. Key Features: - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas - Covers classification of traffic behavior - Demonstrates the application of artificial immune system algorithms for traffic prediction - Covers traffic density estimation using deep learning models - Covers Fog and edge computing for intelligent transportation systems - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers - Presents a current perspective on an urban hyperloop system for India
The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.
The book at hand presents a careful selection of relevant applications of CI methods for transport, logistics, and supply chain management problems. The chapters illustrate the current state-of-the-art in the application of CI methods in these fields and should help and inspire researchers and practitioners to apply and develop efficient methods. A few contributions in this book are extended versions of papers presented at EvoTransLog2007: The First European Workshop on Evolutionary Computation in Transportation and Logistics which was held in Valencia, Spain, in 2007. The majority of contributions are from additional, specially selected researchers, who have done relevant work in different areas of transport, logistics, and supply chain management. The goal is to broadly cover representative applications in these fields as well as different types of solution approaches. On the application side, the contributions focus on design of traffic and transportation networks, vehicle routing, and other important aspects of supply chain management such as inventory management, lot sizing, and lot scheduling. On the method side, the contributions deal with evolutionary algorithms, local search approaches, and scatter search combined with other CI techniques such as neural networks or fuzzy approaches. The book is structured according to the application domains. Thus, it has three parts dealing with traffic and transportation networks, vehicle routing, and supply chain management.
This two-volume set (CCIS 873 and CCIS 874) constitutes the thoroughly refereed proceedings of the 9th International Symposium, ISICA 2017, held in Guangzhou, China, in November 2017.The 101 full papers presented in both volumes were carefully reviewed and selected from 181 submissions. This first volume is organized in topical sections on neural networks and statistical learning: neural architecture search, transfer of knowledge; evolutionary multi-objective and dynamic optimization: optimal control and design, hybrid methods; data mining: association rule learning, data management platforms; Cloud computing and multiagent systems: service models, Cloud engineering; everywhere connectivity: IoT solutions, wireless sensor networks.
This book presents the proceedings of the 9th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2021), held at NIT Mizoram, Aizwal, Mizoram, India, during June 25 – 26, 2021. FICTA conference aims to bring together researchers, scientists, engineers, and practitioners to exchange their new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. This volume covers broad areas of Evolution in Computational Intelligence. The conference papers included herein presents both theoretical as well as practical aspects of different areas like ANN and genetic algorithms, human-computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, multi-agent systems, etc. The volume will also serve as a knowledge centre for students of post-graduate level in various engineering disciplines.
With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.
The new book presents a valuable selection of state-of-the-art technological advancements using the concepts of AI and machine learning, highlighting the use of predictive analytics of data to find timely solutions to real-time problems. It helps to identify applicable approaches in order to enhance, automate, and develop effective solutions to challenges in data science and artificial intelligence. The various novel approaches include applications in healthcare, natural language processing, and smart cities. As such, the book is divided into sections that address: Computational Intelligence in Image Processing Computational Intelligence in Healthcare Techniques for Natural Language Processing Computational Intelligence in Smart Cities The very diverse range of topics include AI and machine learning applications for In security: For using digital image processing for image fusion (face recognition, feature extraction, object detection as well tracking, moving object identification), for person re-identification for security purposes. In healthcare and medicine: For diagnosis and prediction of breast cancer, other cancers, diabetes, heart disease; for predicting susceptibility to COVID-19; for prediction of mood and anxiety disorders. In agriculture: For prediction of crop profit; for prediction of cropping patterns and recommendation for crop cultivation. In traffic science/smart cities: For understanding road scene images, for detection of traffic signs, for devising a fog-based intelligent traffic phase timing regulation system In language/speech/text: For automatic text summarization, for document indexing for unstructured data, for speech/accent recognition, for sound separation, for American Sign Language interpretation for nonsigners, for emotional recognition and analysis through speech, body postures with facial expressions, and other body movements (to improve the performance of virtual personal assistants / emotion recognition using speech, body postures with facial expressions and other body movements. This volume offers valuable information for researchers working in interdisciplinary or multidisciplinary areas of healthcare, image analysis, natural language processing, and smart cities. This includes academicians, people in industry, and students with engineering background with research interest in these areas. These peer-review chapters were selected from the International Conference on Computational Intelligence in Analytics and Information Systems (CIAIS- 2021), held in April 2021 at Manav Rachna University, India. Together with Volume 2: Advances in Digital Transformation, this 2-volume set offers an abundacne of valuable information on emerging technologies in computational intelligence in information systems focusing on data science and artificial intelliegence.