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Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technically-oriented researchers
Book Synopsis Data-Driven Solutions to Transportation Problems by : Yinhai Wang
Download or read book Data-Driven Solutions to Transportation Problems written by Yinhai Wang and published by Elsevier. This book was released on 2018-12-04 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technically-oriented researchers
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book 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 delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Includes case studies in each chapter that illustrate the application of concepts covered Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies Contains contributors from both leading academic and commercial researchers Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
Book Synopsis Data Analytics for Intelligent Transportation Systems by : Mashrur Chowdhury
Download or read book Data Analytics for Intelligent Transportation Systems written by Mashrur Chowdhury and published by Elsevier. This book was released on 2017-04-05 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book 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 delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. Includes case studies in each chapter that illustrate the application of concepts covered Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies Contains contributors from both leading academic and commercial researchers Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
Book Synopsis Mobility Data-Driven Urban Traffic Monitoring by : Zhidan Liu
Download or read book Mobility Data-Driven Urban Traffic Monitoring written by Zhidan Liu and published by Springer Nature. This book was released on 2021-05-18 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
With the rising popularity of ride-sharing and alternative modes of transportation, there has been a renewed interest in transit planning to improve service quality and stem declining ridership. However, it often takes months of manual planning for operators to redesign and reschedule services in response to changing needs. To this end, we provide four models of transportation planning that are based on data and driven by optimization. A key aspect is the ability to provide certificates of optimality, while being practical in generating high-quality solutions in a short amount of time. We provide approaches to combinatorial problems in transit planning that scales up to city-sized networks. In transit network design, current tractable approaches only consider edges that exist, resulting in proposals that are closely tethered to the original network. We allow new transit links to be proposed and account for commuters transferring between different services. In integrated transit scheduling, we provide a way for transit providers to synchronize the timing of services in multimodal networks while ensuring regularity in the timetables of the individual services. This is made possible by taking the characteristics of transit demand patterns into account when designing tractable formulations. We also advance the state of the art in demand models for transportation optimization. In emergency medical services, we provide data-driven formulations that outperforms their probabilistic counterparts in ensuring coverage. This is achieved by replacing independence assumptions in probabilistic models and capturing the interactions of services in overlapping regions. In transit planning, we provide a unified framework that allows us to optimize frequencies and prices jointly in transit networks for minimizing total waiting time.
Book Synopsis Advances in Data-driven Models for Transportation by : Yee Sian Ng
Download or read book Advances in Data-driven Models for Transportation written by Yee Sian Ng and published by . This book was released on 2019 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rising popularity of ride-sharing and alternative modes of transportation, there has been a renewed interest in transit planning to improve service quality and stem declining ridership. However, it often takes months of manual planning for operators to redesign and reschedule services in response to changing needs. To this end, we provide four models of transportation planning that are based on data and driven by optimization. A key aspect is the ability to provide certificates of optimality, while being practical in generating high-quality solutions in a short amount of time. We provide approaches to combinatorial problems in transit planning that scales up to city-sized networks. In transit network design, current tractable approaches only consider edges that exist, resulting in proposals that are closely tethered to the original network. We allow new transit links to be proposed and account for commuters transferring between different services. In integrated transit scheduling, we provide a way for transit providers to synchronize the timing of services in multimodal networks while ensuring regularity in the timetables of the individual services. This is made possible by taking the characteristics of transit demand patterns into account when designing tractable formulations. We also advance the state of the art in demand models for transportation optimization. In emergency medical services, we provide data-driven formulations that outperforms their probabilistic counterparts in ensuring coverage. This is achieved by replacing independence assumptions in probabilistic models and capturing the interactions of services in overlapping regions. In transit planning, we provide a unified framework that allows us to optimize frequencies and prices jointly in transit networks for minimizing total waiting time.
Transportation systems involve complex interactions with traffic demand, loading, and environmental factors, which result in non-linearities in system performance. The structural and functional conditions of a system determine its efficiency in meeting mobility demands. However, budget constraints impose serious limitations on actively monitoring these system responses to maintain reliability. Due to the intrinsic complexity of system responses and limited data availability, it is necessary to develop robust machine-learning models that can accurately characterize system performance and predict future states, based on which actions can be undertaken to maximize their performance under optimal settings. This dissertation focuses on the development and application of machine-learning strategies in evaluating and predicting the conditions of transportation systems like infrastructures using non-destructive evaluation (NDE) techniques, and road networks using real-time traffic data. Multi-dimensional NDE data that capture damage-specific signatures are interpreted to quantify the degree of damage and structural integrity in terms of condition ratings. Several spectral-based autonomous signal classification mechanisms and probabilistic sequential models like hidden Markov models, which perform well with limited data availability, have also been explored. Additionally, this dissertation contributes to the functional performance estimation of networks in terms of macroscopic traffic variables by analyzing real-time traffic datasets. In particular, it focuses on solving problems like traffic prediction and uncertainty quantification using advanced deep learning models, which are essential for efficient traffic operations and optimal control. Data-driven modeling-specific issues like data scarcity, synthetic data generation and transferability, and generalizability of the models on out-of-distribution datasets have been discussed in the context of both NDE and traffic data.
Book Synopsis Data-driven Condition Evaluation of Transportation Systems by : Agnimitra Sengupta
Download or read book Data-driven Condition Evaluation of Transportation Systems written by Agnimitra Sengupta and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation systems involve complex interactions with traffic demand, loading, and environmental factors, which result in non-linearities in system performance. The structural and functional conditions of a system determine its efficiency in meeting mobility demands. However, budget constraints impose serious limitations on actively monitoring these system responses to maintain reliability. Due to the intrinsic complexity of system responses and limited data availability, it is necessary to develop robust machine-learning models that can accurately characterize system performance and predict future states, based on which actions can be undertaken to maximize their performance under optimal settings. This dissertation focuses on the development and application of machine-learning strategies in evaluating and predicting the conditions of transportation systems like infrastructures using non-destructive evaluation (NDE) techniques, and road networks using real-time traffic data. Multi-dimensional NDE data that capture damage-specific signatures are interpreted to quantify the degree of damage and structural integrity in terms of condition ratings. Several spectral-based autonomous signal classification mechanisms and probabilistic sequential models like hidden Markov models, which perform well with limited data availability, have also been explored. Additionally, this dissertation contributes to the functional performance estimation of networks in terms of macroscopic traffic variables by analyzing real-time traffic datasets. In particular, it focuses on solving problems like traffic prediction and uncertainty quantification using advanced deep learning models, which are essential for efficient traffic operations and optimal control. Data-driven modeling-specific issues like data scarcity, synthetic data generation and transferability, and generalizability of the models on out-of-distribution datasets have been discussed in the context of both NDE and traffic data.
Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality. Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data Helps develop policy innovations beneficial to citizens, businesses, and society Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage
Book Synopsis Handbook of Mobility Data Mining, Volume 3 by : Haoran Zhang
Download or read book Handbook of Mobility Data Mining, Volume 3 written by Haoran Zhang and published by Elsevier. This book was released on 2023-01-29 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality. Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data Helps develop policy innovations beneficial to citizens, businesses, and society Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage
This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.
Book Synopsis Transportation Analytics in the Era of Big Data by : Satish V. Ukkusuri
Download or read book Transportation Analytics in the Era of Big Data written by Satish V. Ukkusuri and published by Springer. This book was released on 2018-07-28 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.
Zadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education. This book: Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets. Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality. It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.
Book Synopsis Data-Driven Modelling with Fuzzy Sets by : Said Broumi
Download or read book Data-Driven Modelling with Fuzzy Sets written by Said Broumi and published by CRC Press. This book was released on 2024-07-17 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Zadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education. This book: Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets. Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality. It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.
This two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21–24, 2022. The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
Book Synopsis Data Mining and Big Data by : Ying Tan
Download or read book Data Mining and Big Data written by Ying Tan and published by Springer Nature. This book was released on 2023-01-19 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, CCIS 1744 and CCIS 1745 book constitutes the 7th International Conference, on Data Mining and Big Data, DMBD 2022, held in Beijing, China, in November 21–24, 2022. The 62 full papers presented in this two-volume set included in this book were carefully reviewed and selected from 135 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
The Series in Intelligent Systems publishes titles that cover state of the art knowledge and the latest advances in research and development in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The Series publishes titles in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Titles focus on professional and academic reference works and handbooks. The readership for the series is broad, reflecting the wide range of intelligent systems interest and application, but focuses on engineering (in particular automation, control, mechatronics, robotics, transportation, automotive, aerospace), electronics and electronic design, and computer science. Intelligent Transportation Systems (ITS) are rapidly developing in the world and widely used to optimize the transportation management and plan, enhance city and country security, and improve the traffic users’ travel route, safety and health directly or indirectly. To achieve these goals, a large amount of data are collected from a variety of sources related to ITS. Such abundant and various data lead to a revolution of changing ITS to a more powerful and multifunctional Data-driven ITS. As an associate editor in IEEE transactions on Intelligent Transportation Systems and an associate editor in IEEE Intelligent Systems, Junping Zhang surveys the development of Data-driven ITS in detail, discuss some potential issues closely related to ITS and give a roadmap on the future directions, as well as some recent works on the data-driven ITS. An important contribution in this book is that ITS is closely related to up-to-date data-driven techniques such as data mining, machine learning and computer vision, which are less involved in by other previous ITS-related books. For examples, privacy-preserving and people-centric ITS. The first book for the ITS community that focuses on how to utilize data collected from ITS. Data-driven Intelligent Transportation Systems is a rapidly developing topic. This book is the first dedicated to this important topic. • Introduces the latest approaches and techniques I ITYS, including dimension reduction technique such as nonlinear embedding that can address dimensionality, which often impair the effectiveness of ITS. • Includes a range of real word case studies that show how work in ITS, and related areas including machine learning, vision, and data mining can be applied to transportation systems
Book Synopsis Data Driven Intelligent Transportation Systems by : Zhang, Junping
Download or read book Data Driven Intelligent Transportation Systems written by Zhang, Junping and published by Academic Press. This book was released on 2014-02-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Series in Intelligent Systems publishes titles that cover state of the art knowledge and the latest advances in research and development in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The Series publishes titles in three core sub-topic areas: Intelligent Automation, Intelligent Transportation Systems, and Intelligent Computing. Titles focus on professional and academic reference works and handbooks. The readership for the series is broad, reflecting the wide range of intelligent systems interest and application, but focuses on engineering (in particular automation, control, mechatronics, robotics, transportation, automotive, aerospace), electronics and electronic design, and computer science. Intelligent Transportation Systems (ITS) are rapidly developing in the world and widely used to optimize the transportation management and plan, enhance city and country security, and improve the traffic users’ travel route, safety and health directly or indirectly. To achieve these goals, a large amount of data are collected from a variety of sources related to ITS. Such abundant and various data lead to a revolution of changing ITS to a more powerful and multifunctional Data-driven ITS. As an associate editor in IEEE transactions on Intelligent Transportation Systems and an associate editor in IEEE Intelligent Systems, Junping Zhang surveys the development of Data-driven ITS in detail, discuss some potential issues closely related to ITS and give a roadmap on the future directions, as well as some recent works on the data-driven ITS. An important contribution in this book is that ITS is closely related to up-to-date data-driven techniques such as data mining, machine learning and computer vision, which are less involved in by other previous ITS-related books. For examples, privacy-preserving and people-centric ITS. The first book for the ITS community that focuses on how to utilize data collected from ITS. Data-driven Intelligent Transportation Systems is a rapidly developing topic. This book is the first dedicated to this important topic. • Introduces the latest approaches and techniques I ITYS, including dimension reduction technique such as nonlinear embedding that can address dimensionality, which often impair the effectiveness of ITS. • Includes a range of real word case studies that show how work in ITS, and related areas including machine learning, vision, and data mining can be applied to transportation systems
