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Data analytics for intelligent transportation systems
Hundreds of interconnected electric devices and sensors are embedded in modern vehicles and the transportation infrastructure. They produce massive amounts of data, however a key challenge is how to use this data in real time. Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems, along with detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. Data Analytics for Intelligent Transportation Systems examines the major characteristics of connected transportation systems along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing and distributing the data, designing data infrastructures, data management and data delivery systems, and the required hardware and software technologies. The book shows how to design effective data visualizations, as well as planning, designing, and evaluating alternative data analytics for different connected transportation applications. The book discusses the key safety and environmental applications for both commercial and passenger vehicles, the data privacy and security issues, and concludes with the role of social media data in traffic planning. Case studies in each chapter illustrate application of concepts covered.Extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. Contributors from both leading academic and commercial researchers.
eBook
Computer Vision and Imaging in Intelligent Transportation Systems
<p>Acts as a single source reference providing readers with an overview of how computer vision can contribute to the different applications in the field of road transportation</p> <p>This book presents a survey of computer vision techniques related to three key broad problems in the roadway transportation domain: safety, efficiency, and law enforcement. The individual chapters present significant applications within these problem domains, each presented in a tutorial manner, describing the motivation for and benefits of the application, and a description of the state of the art. <p>Key features <ul> <li>Surveys the applications of computer vision techniques to road transportation systems for the purposes of improving safety and efficiency and to assist law enforcement</li> <li>Offers a timely discussion as computer vision is reaching a point of being useful in the field of transportation systems</li> <li>Available as an enhanced eBook ISBN 9781118971635 with integrated video demonstrations to further explain the concepts discussed in the book, as well as links to publicly available software and data sets for testing and algorithm development</li> </ul> <p>The book will benefit the many researchers, engineers and practitioners of computer vision, digital imaging, automotive and civil engineering working in intelligent transportation systems. Given the breadth of topics covered, the text will present the reader with new possibilities for application within their communities.
eBook
Computer vision and imaging in intelligent transportation systems
This reference provides readers with an overview of how computer vision can contribute to the different applications in the field of road transportation. It presents a survey of computer vision techniques related to three key broad problems in the roadway transportation domain: safety, efficiency, and law enforcement. The individual chapters present significant applications within those problem domains, each presented in a tutorial manner, describing the motivation for and benefits of the application, and a description of the state of the art.
Book
Smart Transportation: An Overview of Technologies and Applications
As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.
Journal Article
Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory
Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.
Journal Article
Fog Computing for Smart Cities’ Big Data Management and Analytics: A Review
Demographic growth in urban areas means that modern cities face challenges in ensuring a steady supply of water and electricity, smart transport, livable space, better health services, and citizens’ safety. Advances in sensing, communication, and digital technologies promise to mitigate these challenges. Hence, many smart cities have taken a new step in moving away from internal information technology (IT) infrastructure to utility-supplied IT delivered over the Internet. The benefit of this move is to manage the vast amounts of data generated by the various city systems, including water and electricity systems, the waste management system, transportation system, public space management systems, health and education systems, and many more. Furthermore, many smart city applications are time-sensitive and need to quickly analyze data to react promptly to the various events occurring in a city. The new and emerging paradigms of edge and fog computing promise to address big data storage and analysis in the field of smart cities. Here, we review existing service delivery models in smart cities and present our perspective on adopting these two emerging paradigms. We specifically describe the design of a fog-based data pipeline to address the issues of latency and network bandwidth required by time-sensitive smart city applications.
Journal Article