Handbook of Mobility Data Mining, Volume One: Data Preprocessing and Visualization 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. Further, the book introduces how to design MDM platforms that adapt to the evolving mobility environment, 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 volume focuses on how to efficiently pre-process mobile big data to extract and utilize critical feature information of high-dimensional city people flow. The book first provides a conceptual theory and framework, then discusses data sources, trajectory map-matching, noise filtering, trajectory data segmentation, data quality assessment, and more, concluding with a chapter on privacy protection in mobile big data mining. - Introduces the characteristics of different mobility data sources, like GPS, CDR, and sensor-based mobility data - Summarizes existing visualization technologies of the current transportation system into a multi-view frame, covering the perspective of the three leading actors - Provides recommendations for practical open-source tools and libraries for system visualization - 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

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

Handbook of Mobility Data Mining, Volume Two: Mobility Analytics and Prediction 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 introduces how to design MDM platforms that adapt to the evolving mobility environment and new types of transportation and users. This helpful guide provides a basis for how to simulate and predict mobility data. After an introductory theory chapter, the book then covers crucial topics such as long-term mobility pattern analytics, mobility data generators, user information inference, Grid-based population density prediction, and more. The book concludes with a chapter on graph-based mobility data analytics. The information in this work is crucial for researchers, engineers, operators, company administrators, and policymakers in related fields, to comprehensively understand current technologies' infra-knowledge structure and limitations. - Discusses how to efficiently simulate massive and large-scale people movement and predict mobility at an urban scale - Introduces both online detection methods, which can sequentially process data, and offline detection methods, which are usually more robust - 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

Mobile communications and ubiquitous computing generate large volumes of data. Mining this data can produce useful knowledge, yet individual privacy is at risk. This book investigates the various scientific and technological issues of mobility data, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, and this book relates their findings in 13 chapters covering all related subjects.

Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling, Second Edition provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns-a key aspect of transportation modeling. It features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. The fields covered by this book are evolving rapidly and this new edition updates the existing material and provides new chapters that reflect recent developments in the field (such as the emergence of active, transfer and reinforcement learning). Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis that are related to complex processes and phenomena. It bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.

This handbook covers the fundamental principles and theory, and the state-of-the-art research, systems and applications, in the area of mobility data privacy. It is primarily addressed to computer science and statistics researchers and educators, who are interested in topics related to mobility privacy. This handbook will also be valuable to industry developers, as it explains the state-of-the-art algorithms for offering privacy. By discussing a wide range of privacy techniques, providing in-depth coverage of the most important ones, and highlighting promising avenues for future research, this handbook also aims at attracting computer science and statistics students to this interesting field of research. The advances in mobile devices and positioning technologies, together with the progress in spatiotemporal database research, have made possible the tracking of mobile devices (and their human companions) at very high accuracy, while supporting the efficient storage of mobility data in data warehouses, which this handbook illustrates. This has provided the means to collect, store and process mobility data of an unprecedented quantity, quality and timeliness. As ubiquitous computing pervades our society, user mobility data represents a very useful but also extremely sensitive source of information. On one hand, the movement traces that are left behind by the mobile devices of the users can be very useful in a wide spectrum of applications such as urban planning, traffic engineering, and environmental pollution management. On the other hand, the disclosure of mobility data to third parties may severely jeopardize the privacy of the users whose movement is recorded, leading to abuse scenarios such as user tailing and profiling. A significant amount of research work has been conducted in the last 15 years in the area of mobility data privacy and important research directions, such as privacy-preserving mobility data management, privacy in location sensing technologies and location-based services, privacy in vehicular communication networks, privacy in location-based social networks, privacy in participatory sensing systems which this handbook addresses.. This handbook also identifies important privacy gaps in the use of mobility data and has resulted to the adoption of international laws for location privacy protection (e.g., in EU, US, Canada, Australia, New Zealand, Japan, Singapore), as well as to a large number of interesting technologies for privacy-protecting mobility data, some of which have been made available through open-source systems and featured in real-world applications.

Determinants of firm and market organization; Analysis of market behavior; Empirical methods and results; International issues and comparision; government intervention in the Marketplace.

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.

A guide to the continually evolving field of labour economics.

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains - Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book - Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book - Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface - Includes open-access online courses that introduce practical applications of the material in the book