Download or read book Large scale Traffic Flow Prediction Using Deep Learning in the Context of Smart Mobility written by Arief Koesdwiady and published by . This book was released on 2018 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing and developing a new generation of cities around the world (termed as smart cities) is fast becoming one of the ultimate solutions to overcome cities' problems such as population growth, pollution, energy crisis, and pressure demand on existing transportation infrastructure. One of the major aspects of a smart city is smart mobility. Smart mobility aims at improving transportation systems in several aspects: city logistics, info-mobility, and people-mobility. The emergence of the Internet of Car (IoC) phenomenon alongside with the development of Intelligent Transportation Systems (ITSs) opens some opportunities in improving the traffic management systems and assisting the travelers and authorities in their decision-making process. However, this has given rise to the generation of huge amount of data originated from human-device and device-device interaction. This is an opportunity and a challenge, and smart mobility will not meet its full potential unless valuable insights are extracted from these big data. Although the smart city environment and IoC allow for the generation and exchange of large amounts of data, there have not been yet well de ned and mature approaches for mining this wealth of information to benefit the drivers and traffic authorities. The main reason is most likely related to fundamental challenges in dealing with big data of various types and uncertain frequency coming from diverse sources. Mainly, the issues of types of data and uncertainty analysis in the predictions are indicated as the most challenging areas of study that have not been tackled yet. Important issues such as the nature of the data, i.e., stationary or non-stationary, and the prediction tasks, i.e., short-term or long-term, should also be taken into consideration. Based on this observation, a data-driven traffic flow prediction framework within the context of big data environment is proposed in this thesis. The main goal of this framework is to enhance the quality of traffic flow predictions, which can be used to assist travelers and traffic authorities in the decision-making process (whether for travel or management purposes). The proposed framework is focused around four main aspects that tackle major data-driven traffic flow prediction problems: the fusion of hard data for traffic flow prediction; the fusion of soft data for traffic flow prediction; prediction of non-stationary traffic flow; and prediction of multi-step traffic flow. All these aspects are investigated and formulated as computational based tools/algorithms/approaches adequately tailored to the nature of the data at hand. The first tool tackles the inherent big data problems and deals with the uncertainty in the prediction. It relies on the ability of deep learning approaches in handling huge amounts of data generated by a large-scale and complex transportation system with limited prior knowledge. Furthermore, motivated by the close correlation between road traffic and weather conditions, a novel deep-learning-based approach that predicts traffic flow by fusing the traffic history and weather data is proposed. The second tool fuses the streams of data (hard data) and event-based data (soft data) using Dempster Shafer Evidence Theory (DSET). One of the main features of the DSET is its ability to capture uncertainties in probabilities. Subsequently, an extension of DSET, namely Dempsters conditional rules for updating belief, is used to fuse traffic prediction beliefs coming from streams of data and event-based data sources. The third tool consists of a method to detect non-stationarities in the traffic flow and an algorithm to perform online adaptations of the traffic prediction model. The proposed detection approach is developed by monitoring the evolution of the spectral contents of the traffic flow. Furthermore, the approach is specfi cally developed to work in conjunction with state-of-the-art machine learning methods such as Deep Neural Network (DNN). By combining the power of frequency domain features and the known generalization capability and scalability of DNN in handling real-world data, it is expected that high prediction performances can be achieved. The last tool is developed to improve multi-step traffic flow prediction in the recursive and multi-output settings. In the recursive setting, an algorithm that augments the information about the current time-step is proposed. This algorithm is called Conditional Data as Demonstrator (C-DaD) and is an extension of an algorithm called Data as Demonstrator (DaD). Furthermore, in the multi-output setting, a novel approach of generating new history-future pairs of data that are aggregated with the original training data using Conditional Generative Adversarial Network (C-GAN) is developed. To demonstrate the capabilities of the proposed approaches, a series of experiments using artificial and real-world data are conducted. Each of the proposed approaches is compared with the state-of-the-art or currently existing approaches.
Download or read book Cognitive Internet of Things Frameworks Tools and Applications written by Huimin Lu and published by Springer. This book was released on 2019-02-18 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides insights into the research in the fields of artificial intelligence in combination with Internet of Things (IoT) technologies. Today, the integration of artificial intelligence and IoT technologies is attracting considerable interest from both researchers and developers from academic fields and industries around the globe. It is foreseeable that the next generation of IoT research will focus on artificial intelligence/beyond artificial intelligence approaches. The rapidly growing numbers of artificial intelligence algorithms and big data solutions have significantly increased the number of potential applications for IoT technologies, but they have also created new challenges for the artificial intelligence community. This book shares the latest scientific advances in this area.
Download or read book Short Term Traffic Flow Prediction Using Deep Learning written by Pregya Poonia and published by . This book was released on 2023-12-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The economy of a country or region relies vigorously on an efficient and dependable transportation system to provide accessibility and promote the safe and efficient movement of individuals and merchandise. In fact, the transportation framework has been identified by (Nicholson and Du 1997) as the most significant lifesaver in case of natural disasters, for example, earth shudders, floods, hurricanes, and others. Rebuilding of different life savers (for example water supply, electrical power system, sewer system, communication, and numerous others) depends emphatically on the capacity to ship individuals and equipment to harmed destinations. The real travel requests and street limit do differ over time, in this manner, adding to the vulnerability of travel times. With the expanded estimation of time, great loss is incurred by the drivers because of the unexpected schedule (either early or late) delay. A stable transportation system would give a serious edge in the worldwide economy. Therefore, the significance of the reliability of a transportation system cannot be overemphasized. Anticipating the traffic stream is an unpredictable procedure that is influenced by a few parameters, for example, traffic designs, information accumulation, applied zones, and so forth the rightness of traffic stream expectation can acquire preferred position to the smart traffic the executives, it can help in improving rush hour gridlock productivity and diminishing traffic blockage. Fundamentally, stream forecast targets is assessed the absolute number of vehicles given a particular district and a period interim. According to Boris S. [6] and Wei Shenet al. [69], the real-time speed of traffic flow is available to everyone thorough GPS. The traffic data providers use machine learning to predict speed for each road segment. Forecasting the real-time traffic knowledge is really helpful for traveler, it gives the potential of choosing better routes and helps in managing the transportation system.
Download or read book Proceedings of the 11th International Conference on Computer Engineering and Networks written by Qi Liu and published by Springer Nature. This book was released on 2021-11-11 with total page 1700 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference proceeding is a collection of the papers accepted by the CENet2021 – the 11th International Conference on Computer Engineering and Networks held on October 21-25, 2021 in Hechi, China. The topics focus but are not limited to Internet of Things and Smart Systems, Artificial Intelligence and Applications, Communication System Detection, Analysis and Application, and Medical Engineering and Information Systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings. This will enable them to produce, maintain, and manage systems with high levels of trustworthiness and complexity.
Download or read book Urban Informatics written by Wenzhong Shi and published by Springer Nature. This book was released on 2021-04-06 with total page 941 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.
Download or read book Vehicular Traffic Flow Prediction Model Using Machine Learning Based Model written by Jiahao Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Transportation Systems (ITS) have attracted an increasing amount of attention in recent years. Thanks to the fast development of vehicular computing hardware, vehicular sensors and citywide infrastructures, many impressive applications have been proposed under the topic of ITS, such as Vehicular Cloud (VC), intelligent traffic controls, etc. These applications can bring us a safer, more efficient, and also more enjoyable transportation environment. However, an accurate and efficient traffic flow prediction system is needed to achieve these applications, which creates an opportunity for applications under ITS to deal with the possible road situation in advance. To achieve better traffic flow prediction performance, many prediction methods have been proposed, such as mathematical modeling methods, parametric methods, and non-parametric methods. It is always one of the hot topics about how to implement an efficient, robust and accurate vehicular traffic prediction system. With the help of Machine Learning-based (ML) methods, especially Deep Learning-based (DL) methods, the accuracy of the prediction model is increased. However, we also noticed that there are still many open challenges under ML-based vehicular traffic prediction model real-world implementation. Firstly, the time consumption for DL model training is relatively huge compared to parametric models, such as ARIMA, SARIMA, etc. Second, it is still a hot topic for the road traffic prediction that how to capture the special relationship between road detectors, which is affected by the geographic correlation, as well as the time change. The last but not the least, it is important for us to implement the prediction system in the real world; meanwhile, we should find a way to make use of the advanced technology applied in ITS to improve the prediction system itself. In our work, we focus on improving the features of the prediction model, which can be helpful for implementing the model in the real word. Firstly, we introduced an optimization strategy for ML-based models' training process, in order to reduce the time cost in this process. Secondly, We provide a new hybrid deep learning model by using GCN and the deep aggregation structure (i.e., the sequence to sequence structure) of the GRU. Meanwhile, in order to solve the real-world prediction problem, i.e., the online prediction task, we provide a new online prediction strategy by using refinement learning. In order to further improve the model's accuracy and efficiency when applied to ITS, we provide a parallel training strategy by using the benefits of the vehicular cloud structure.
Download or read book Advanced Intelligent Predictive Models for Urban Transportation written by R. Sathiyaraj and published by CRC Press. This book was released on 2022-03-27 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book emphasizes the predictive models of Big Data, Genetic Algorithm, and IoT with a case study. The book illustrates the predictive models with integrated fuel consumption models for smart and safe traveling. The text is a coordinated amalgamation of research contributions and industrial applications in the field of Intelligent Transportation Systems. The advanced predictive models and research results were achieved with the case studies, deployed in real transportation environments. Features: Provides a smart traffic congestion avoidance system with an integrated fuel consumption model. Predicts traffic in short-term and regular. This is illustrated with a case study. Efficient Traffic light controller and deviation system in accordance with the traffic scenario. IoT based Intelligent Transport Systems in a Global perspective. Intelligent Traffic Light Control System and Ambulance Control System. Provides a predictive framework that can handle the traffic on abnormal days, such as weekends, festival holidays. Bunch of solutions and ideas for smart traffic development in smart cities. This book focuses on advanced predictive models along with offering an efficient solution for smart traffic management system. This book will give a brief idea of the available algorithms/techniques of big data, IoT, and genetic algorithm and guides in developing a solution for smart city applications. This book will be a complete framework for ITS domain with the advanced concepts of Big Data Analytics, Genetic Algorithm and IoT. This book is primarily aimed at IT professionals. Undergraduates, graduates and researchers in the area of computer science and information technology will also find this book useful.
Download or read book The 2021 International Conference on Smart Technologies and Systems for Internet of Things written by Ishfaq Ahmad and published by Springer Nature. This book was released on 2022-07-02 with total page 818 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains papers presented at the 2021 International Conference on Smart Technologies and Systems for Internet of Things, held on November 26–27, 2021, in Shanghai, China. It covers topics like distributed processing for sensor data in CPS networks, approximate reasoning and pattern recognition for CPS networks, distributed processing in mobile networking, data analytics for social media sensor data integration, data platforms for efficient integration with CPS networks, virtualized and cloud-oriented resources for data processing for CPS networks, machine learning algorithms for CPS networks, data security and privacy in CPS networks, sensor fusion algorithms, sensor signal processing, data acquisition and preprocessing technology, intelligent computing, data mining methods and algorithms, big data system solutions and tools platform, intelligent control and intelligent management, and operational situation awareness utilizing big data-driven intelligence. It caters to postgraduate students, researchers, and practitioners specializing and working in related areas.
Download or read book Intelligent Vehicular Networks and Communications written by Anand Paul and published by Elsevier. This book was released on 2016-09-02 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues. The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes. Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles. Proposes cooperative, cognitive, intelligent vehicular networks Examines how intelligent transportation systems make more efficient transportation in urban environments Outlines next generation vehicular networks technology
Download or read book Traffic Control in Large scale Urban Networks written by Liudmila Tumash and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research is done in the context of European Research Council's Advanced Grant project Scale-FreeBack. The aim of Scale-FreeBack project is to develop a holistic scale-free control approach to complex systems, and to set new foundations for a theory dealing with complex physical networks with arbitrary dimension. One particular case is intelligent transportation systems that are capable to prevent the occurrence of congestions in rush hours. The contributions of the present PhD work are mainly related to traffic boundary control design and modelling on large-scale urban networks. We consider traffic from the macroscopic viewpoint describing it in terms of aggregated variables such as flow and density of vehicles, i.e., traffic is seen as a fluid whose motion is described using the concept of kinematic waves. The corresponding dynamic equation corresponds to a first-order hyperbolic partial differential equation. Within this PhD thesis, we propose control design techniques that completely rely on the intrinsic properties of the model. First of all, we solve one-dimensional (1D) boundary control problems, i.e., one road traffic. Thereby, the traffic state is driven to a space- and time-dependent desired trajectory that admits traffic regimes switching, i.e., both states can be partially congested and partially in the free-flow regime. This introduces non-linearities into the state equation, which we can handle and achieve the target by acting only from road's boundaries. Then, we extend the problem to a urban network of arbitrary size. The large-scale traffic dynamics are described by a two-dimensional (2D) conservation law model. The model parameters are defined everywhere in the continuum plane from its values on physical roads that are further interpolated as a function of distance to these roads. The traffic flow direction is determined by network's geometry (location of roads and intersections) and infrastructure parameters (speed limits, number of lanes, etc). This 2D model assumes that there exists a preferred direction of motion. For this case, we elaborate a unique method that considerably simplifies control design for traffic systems evolving in large-scale networks. In particular, we present a coordinate transformation that translates a 2D continuous traffic model into a continuous set of 1D systems equations. This enables an explicit elaboration of strategies for various control tasks to solve on large-scale networks: we design boundary control for 2D density in a mixed traffic regime, apply variable speed limit control to drive traffic to any space-dependent equilibrium, and calculate steady-states. Finally, we also present a new multi-directional two-dimensional continuous traffic model. This model is formally derived by solely using the demand-supply concept at one intersection (classical Cell Transmission Model). Our new model is called the NSWE-model, since it consists of four partial differential equations that describe the evolution of vehicle density with respect to cardinal directions: North, South, West and East. The traffic flow direction is determined by turning ratios at intersections. For this model, we design a boundary control that drives multi-directional congested traffic to a desired equilibrium vehicle density mitigating the congestion level. The effectiveness of our contributions were tested using simulated and real data. In the first case, the results are verified by using the well-known commercial traffic Aimsun, which produces microsimulations of vehicles' trajectories in a modelled network. In the second case, real data are obtained from sensors measuring traffic flow in the city of Grenoble, and collected using the Grenoble Traffic Lab.
Download or read book Deep Learning Applications and Intelligent Decision Making in Engineering written by Senthilnathan, Karthikrajan and published by IGI Global. This book was released on 2020-10-23 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
Download or read book Emerging Technologies for Smart Cities written by Prabin K. Bora and published by Springer Nature. This book was released on 2021-06-11 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the select proceedings of the International Conference on Emerging Global Trends in Engineering and Technology (EGTET 2020), held in Guwahati, India. The chapters in this book focus on the latest cleaner, greener, and efficient technologies being developed for the implementation of smart cities across the world. The broader topical sections include Smart Buildings, Infrastructures and Disaster Management; Smart Governance; Technologies for Smart Cities, and Wireless Connectivity for Smart Cities. This book will cater to students, researchers, industry professionals, and policy making bodies interested and involved in the planning and implementation of smart city projects.
Download or read book Prediction of Large Scale Spatio temporal Traffic Flow Data with New Graph Convolution Model written by Ping Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator to analyze the road occupancy status and formulate dynamic and flexible traffic control in advance to improve the road capacity. It can also provide more precise navigation guidance for the road users in future. However, it is hard to predict spatiotemporal traffic flow data in large scale promptly with high accuracy caused by complex interrelation and nonlinear dynamic nature. With development of deep learning and other technologies, many prediction networks could predict traffic flow with accumulated historical data in time series. In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications. Those successful applications provide a possible way to contribute fast and proper traffic control strategies that could relieve traffic pressure, reduce potential conflict, fasten emergency response, etc.
Download or read book International Conference on Innovative Computing and Communications written by Deepak Gupta and published by Springer Nature. This book was released on 2020-08-01 with total page 1152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes high-quality research papers presented at the Third International Conference on Innovative Computing and Communication (ICICC 2020), which is held at the Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, India, on 21–23 February, 2020. Introducing the innovative works of scientists, professors, research scholars, students and industrial experts in the field of computing and communication, the book promotes the transformation of fundamental research into institutional and industrialized research and the conversion of applied exploration into real-time applications.
Download or read book Intelligent Transportation Related Complex Systems and Sensors written by Kyandoghere Kyamakya and published by MDPI. This book was released on 2021-09-01 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Innovations in Smart Cities Applications Volume 5 written by Mohamed Ben Ahmed and published by Springer Nature. This book was released on 2022-03-03 with total page 1117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets the innovative research contributions, works, and solutions for almost all the intelligent and smart applications in the smart cities. The smart city concept is a relevant topic for industrials, governments, and citizens. Due to this, the smart city, considered as a multi-domain context, attracts tremendously academics researchers and practitioners who provide efforts in theoretical proofs, approaches, architectures, and in applied researches. The importance of smart cities comes essentially from the significant growth of populations in the near future which conducts to a real need of smart applications that can support this evolution in the future cities. The main scope of this book covers new and original ideas for the next generations of cities using the new technologies. The book involves the application of the data science and AI, IoT technologies and architectures, smart earth and water management, smart education and E-learning systems, smart modeling systems, smart mobility, and renewable energy. It also reports recent research works on big data technologies, image processing and recognition systems, and smart security and privacy.
