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| business analytics in transportation: Data Analytics for Intelligent Transportation Systems Mashrur Chowdhury, Kakan Dey, Amy Apon, 2024-11-02 Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including 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. It presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. They will discover 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. Data Analytics for Intelligent Transportation Systems will prepare an educated ITS workforce and tool builders to make the vision for safe, reliable, and environmentally sustainable intelligent transportation systems a reality. It serves as a primary or supplemental textbook for upper-level undergraduate and graduate ITS courses and a valuable reference for ITS practitioners. - Utilizes real ITS examples to facilitate a quicker grasp of materials presented - Contains contributors from both leading academic and commercial domains - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications - Includes exercise problems in each chapter to help readers apply and master the learned fundamentals, concepts, and techniques - New to the second edition: Two new chapters on Quantum Computing in Data Analytics and Society and Environment in ITS Data Analytics |
| business analytics in transportation: Transportation Analytics in the Era of Big Data Satish V. Ukkusuri, Chao Yang, 2018-07-28 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. |
| business analytics in transportation: Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities Moridpour, Sara, Toran Pour, Alireza, Saghapour, Tayebeh, 2019-01-11 Recent research reveals that socioeconomic factors of the neighborhoods where road users live and where pedestrian-vehicle crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focusing on the road users, should be targeted at these high risk neighborhoods. Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities is an essential reference source that discusses access to transportation and examines vehicle-pedestrian crashes, specifically in relation to socioeconomic factors that influence them, main predictors, factors that contribute to crash severity, and the enhancement of pedestrian safety measures. Featuring research on topics such as public transport, accessibility, and spatial distribution, this book is ideally designed for policymakers, transportation engineers, road safety designers, transport planners and managers, professionals, academicians, researchers, and public administrators. |
| business analytics in transportation: Mobility Patterns, Big Data and Transport Analytics Constantinos Antoniou, Loukas Dimitriou, Francisco Pereira, 2025-06-01 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. |
| business analytics in transportation: Logistics Management Tan Miller, Matthew J. Liberatore, 2020-04-08 This book illustrates and explains a wide range of practical logistics strategies and analytic techniques to facilitate decision-making across functions such as manufacturing, warehousing, transportation, and inventory management. Logistics professionals must utilize a broad array of analytic techniques and approaches for decision-making. Effective use of analytics requires an understanding of both fundamental and advanced logistics decision-making techniques and methodologies. Further, logistics professionals must organize and view these analytics-based decision support tools through well-structured planning frameworks. In this book, we illustrate and explain a wide range of practical logistics strategies and analytic techniques to facilitate decision-making across functions such as manufacturing, warehousing, transportation and inventory management. We also describe how to organize these analytics-based tools and strategies through logistics frameworks that span strategic, tactical and operational planning and scheduling decisions. This book is intended for logistics professionals to use as a reference document that offers ideas and guidance for addressing specific logistics management decisions and challenges, and it will also serve as a valuable resource or secondary text for graduate and advanced undergraduate students. |
| business analytics in transportation: Logistics, Supply Chain and Financial Predictive Analytics Kusum Deep, Madhu Jain, Said Salhi, 2018-08-06 This book addresses a broad range of problems commonly encountered in the fields of financial analysis, logistics and supply chain management, such as the use of big data analytics in the banking sector. Divided into twenty chapters, some of the contemporary topics discussed in the book are co-operative/non-cooperative supply chain models for imperfect quality items with trade-credit financing; a non-dominated sorting water cycle algorithm for the cardinality constrained portfolio problem; and determining initial, basic and feasible solutions for transportation problems by means of the “supply demand reparation method” and “continuous allocation method.” In addition, the book delves into a comparison study on exponential smoothing and the Arima model for fuel prices; optimal policy for Weibull distributed deteriorating items varying with ramp type demand rate and shortages; an inventory model with shortages and deterioration for three different demand rates; outlier labeling methods for medical data; a garbage disposal plant as a validated model of a fault-tolerant system; and the design of a “least cost ration formulation application for cattle”; a preservation technology model for deteriorating items with advertisement dependent demand and trade credit; a time series model for stock price forecasting in India; and asset pricing using capital market curves. The book offers a valuable asset for all researchers and industry practitioners working in these areas, giving them a feel for the latest developments and encouraging them to pursue further research in this direction. |
| business analytics in transportation: Applying Business Intelligence Initiatives in Healthcare and Organizational Settings Miah, Shah J., Yeoh, William, 2018-07-13 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Applying Business Intelligence Initiatives in Healthcare and Organizational Settings incorporates emerging concepts, methods, models, and relevant applications of business intelligence systems within problem contexts of healthcare and other organizational boundaries. Featuring coverage on a broad range of topics such as rise of embedded analytics, competitive advantage, and strategic capability, this book is ideally designed for business analysts, investors, corporate managers, and entrepreneurs seeking to advance their understanding and practice of business intelligence. |
| business analytics in transportation: BASIC BUSINESS ANALYTICS USING R Dr. Mahavir M. Shetiya, Prof. Snehal V. Bhambure, 2023-11-10 Buy BASIC BUSINESS ANALYTICS USING R e-Book for Mba 2nd Semester in English language specially designed for SPPU ( Savitribai Phule Pune University ,Maharashtra) By Thakur publication. |
| business analytics in transportation: Handbook of Research on Pattern Engineering System Development for Big Data Analytics Tiwari, Vivek, Thakur, Ramjeevan Singh, Tiwari, Basant, Gupta, Shailendra, 2018-04-20 Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. The Handbook of Research on Pattern Engineering System Development for Big Data Analytics is a critical scholarly resource that examines the incorporation of pattern management in business technologies as well as decision making and prediction process through the use of data management and analysis. Featuring coverage on a broad range of topics such as business intelligence, feature extraction, and data collection, this publication is geared towards professionals, academicians, practitioners, and researchers seeking current research on the development of pattern management systems for business applications. |
| business analytics in transportation: International Encyclopedia of Transportation , 2021-05-13 In an increasingly globalised world, despite reductions in costs and time, transportation has become even more important as a facilitator of economic and human interaction; this is reflected in technical advances in transportation systems, increasing interest in how transportation interacts with society and the need to provide novel approaches to understanding its impacts. This has become particularly acute with the impact that Covid-19 has had on transportation across the world, at local, national and international levels. Encyclopedia of Transportation, Seven Volume Set - containing almost 600 articles - brings a cross-cutting and integrated approach to all aspects of transportation from a variety of interdisciplinary fields including engineering, operations research, economics, geography and sociology in order to understand the changes taking place. Emphasising the interaction between these different aspects of research, it offers new solutions to modern-day problems related to transportation. Each of its nine sections is based around familiar themes, but brings together the views of experts from different disciplinary perspectives. Each section is edited by a subject expert who has commissioned articles from a range of authors representing different disciplines, different parts of the world and different social perspectives. The nine sections are structured around the following themes: Transport Modes; Freight Transport and Logistics; Transport Safety and Security; Transport Economics; Traffic Management; Transport Modelling and Data Management; Transport Policy and Planning; Transport Psychology; Sustainability and Health Issues in Transportation. Some articles provide a technical introduction to a topic whilst others provide a bridge between topics or a more future-oriented view of new research areas or challenges. The end result is a reference work that offers researchers and practitioners new approaches, new ways of thinking and novel solutions to problems. All-encompassing and expertly authored, this outstanding reference work will be essential reading for all students and researchers interested in transportation and its global impact in what is a very uncertain world. Provides a forward looking and integrated approach to transportation Updated with future technological impacts, such as self-driving vehicles, cyber-physical systems and big data analytics Includes comprehensive coverage Presents a worldwide approach, including sets of comparative studies and applications |
| business analytics in transportation: Data Science for Transport Charles Fox, 2018-02-27 The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the sexiest job of the 21st century. Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds. - Tom van Vuren, Divisional Director, Mott MacDonald WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book. - Leighton Cardwell, Technical Director, WSP. From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to talk to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds. - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources. - Yaron Hollander, author of Transport Modelling for a Complete Beginner |
| business analytics in transportation: Statistical and Econometric Methods for Transportation Data Analysis Simon Washington, Matthew G. Karlaftis, Fred Mannering, Panagiotis Anastasopoulos, 2020-01-30 The book's website (with databases and other support materials) can be accessed here. Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems. |
| business analytics in transportation: International Journal of Business Analytics (IJBAN). John Wang, 2015 |
| business analytics in transportation: Truckload Transportation Leo J. Lazarus, 2010 Truckload Transportation: Economics, Pricing and Analysis covers every facet of truckload pricing including the truckload business model, one-way pricing concepts, dedicated fleet pricing and design, and bid response analysis. The book covers all the primary truckload transportation concepts such as capacity and balance, utilization, length of haul, empty miles, and revenue per mile.The book provides an in depth review of all forms of dedicated pricing including fixed-variable, utilization scales and over-under. The dedicated pricing chapters also cover special topics such as shuttle pricing, short haul pricing, and mileage band pricing. The book also includes four detailed case studies in bid response analysis, a detailed chapter on network analysis, and a special chapter of truckload transportation concepts specifically for truckload shippers.For additional information, please visitTRUCKLOADTRANSPORTATION.COM |
| business analytics in transportation: ICTE in Transportation and Logistics 2019 Egils Ginters, Mario Arturo Ruiz Estrada, Miquel Angel Piera Eroles, 2020-01-30 This proceedings volume explores the latest advances in transport and logistics, while also discussing the applications of modern information technologies, telecommunications, electronics, and prospective research methods and analyzing their impacts on society and the environment, which in turn determine the future development of these technologies. The book is intended for a broad readership, including transport and logistics business planners and technical experts, leveraging industry knowledge and facilitating technology adoption in promising business regions and transit corridors such as Ukraine, Kazakhstan, and others. The authors, who include policy planners and crafters as well as education and training professionals, address various types of intermodal transport such as rail, road, maritime, air, etc. |
| business analytics in transportation: Intelligent Optimization Techniques for Business Analytics Bansal, Sanjeev, Kumar, Nitendra, Agarwal, Priyanka, 2024-04-15 Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. |
| business analytics in transportation: Global Business Analytics Models Hokey Min, 2016-03-05 THE COMPLETE GUIDE TO USING ANALYTICS TO MANAGE RISK AND UNCERTAINTY IN COMPLEX GLOBAL BUSINESS ENVIRONMENTS Practical techniques for developing reliable, actionable intelligence–and using it to craft strategy Analytical opportunities to solve key managerial problems in global enterprises Written for working managers: packed with realistic, useful examples This guide helps global managers use modern analytics to gain reliable, actionable, and timely business intelligence–and use it to manage risk, build winning strategies, and solve urgent problems. Dr. Hokey Min offers a practical, easy-to-understand overview of business analytics in a global context, focusing especially on managerial and strategic implications. After demystifying today’s core quantitative tools, he demonstrates them at work in a wide spectrum of global applications. You’ll build models to help segment global markets, forecast demand, assess risk, plan financing, optimize supply chains, and more. Along the way, you’ll find practical guidance for developing analytic thinking, operationalizing Big Data in global environments, and preparing for future analytical innovations. Whether you’re a global executive, strategist, analyst, marketer, supply chain professional, student or researcher, this book will help you drive real value from analytics–in smarter decisions, improved strategy, and better management. In today’s global business environments characterized by growing complexity, volatility, and uncertainty, business analytics has become an indispensable tool for managing these challenges. Specifically, global managers need analytics expertise to solve problems, identify opportunities, shape strategy, mitigate risk, and improve their day-to-day operational efficiency. Now, for the first time, there’s an analytics guide designed specifically for decision-makers in global organizations. Leveraging his experience teaching a number of students and training hundreds of managers and executives, Dr. Hokey Min demystifies the principles and tools of modern business analytics, and demonstrates their real-world use in global business. First, Dr. Min identifies key success factors and mindsets, helping you establish the preconditions for effective analysis. Next, he walks you through the practicalities of collecting, organizing, and analyzing Big Data, and developing models to transform them into actionable insight. Building on these foundations, he illustrates core analytical applications in finance, healthcare, and global supply chains. He concludes by previewing emerging trends in analytics, including the newest tools for automated decision-making. Compare today’s key quantitative tools Stats, data mining, OR, and simulation: how they work, when to use them Get the right data... ...and get the data right Predict the future... ...and sense its arrival sooner than others can |
| business analytics in transportation: Big Data Analytics for Smart Transport and Healthcare Systems Saeid Pourroostaei Ardakani, Ali Cheshmehzangi, 2024-01-04 This book aims to introduce big data solutions in urban sustainability applications—mainly smart transportation and healthcare systems. It focuses on machine learning techniques and data processing approaches which have the capacity to handle/process huge, live, and complex datasets in real-time transportation and healthcare applications. For this, several state-of-the-art data processing approaches including data pre-processing, classification, regression, and clustering are introduced, tested, and evaluated to highlight their benefits and constraints where data is sensitive, real-time, and/or semi-structured. |
| business analytics in transportation: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics |
| business analytics in transportation: Business Analytics with Management Science Models and Methods Arben Asllani, 2015 This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems. |
| business analytics in transportation: Encyclopedia of Business Analytics and Optimization Wang, John, 2014-02-28 As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal. |
| business analytics in transportation: Big Data and Business Analytics Jay Liebowitz, 2016-04-19 The chapters in this volume offer useful case studies, technical roadmaps, lessons learned, and a few prescriptions todo this, avoid that.'-From the Foreword by Joe LaCugna, Ph.D., Enterprise Analytics and Business Intelligence, Starbucks Coffee CompanyWith the growing barrage of big data, it becomes vitally important for organizations to mak |
| business analytics in transportation: Machine Learning for Business Analytics Galit Shmueli, Peter C. Bruce, Amit V. Deokar, Nitin R. Patel, 2023-03-08 Machine Learning for Business Analytics Machine learning—also known as data mining or data analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes: A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology. |
| business analytics in transportation: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| business analytics in transportation: Business Analytics and Decision Making in Practice Ali Emrouznejad, |
| business analytics in transportation: Encyclopedia of Big Data Laurie A. Schintler, Connie L. McNeely, 2022-02-23 This encyclopedia will be an essential resource for our times, reflecting the fact that we currently are living in an expanding data-driven world. Technological advancements and other related trends are contributing to the production of an astoundingly large and exponentially increasing collection of data and information, referred to in popular vernacular as “Big Data.” Social media and crowdsourcing platforms and various applications ― “apps” ― are producing reams of information from the instantaneous transactions and input of millions and millions of people around the globe. The Internet-of-Things (IoT), which is expected to comprise tens of billions of objects by the end of this decade, is actively sensing real-time intelligence on nearly every aspect of our lives and environment. The Global Positioning System (GPS) and other location-aware technologies are producing data that is specific down to particular latitude and longitude coordinates and seconds of the day. Large-scale instruments, such as the Large Hadron Collider (LHC), are collecting massive amounts of data on our planet and even distant corners of the visible universe. Digitization is being used to convert large collections of documents from print to digital format, giving rise to large archives of unstructured data. Innovations in technology, in the areas of Cloud and molecular computing, Artificial Intelligence/Machine Learning, and Natural Language Processing (NLP), to name only a few, also are greatly expanding our capacity to store, manage, and process Big Data. In this context, the Encyclopedia of Big Data is being offered in recognition of a world that is rapidly moving from gigabytes to terabytes to petabytes and beyond. While indeed large data sets have long been around and in use in a variety of fields, the era of Big Data in which we now live departs from the past in a number of key respects and with this departure comes a fresh set of challenges and opportunities that cut across and affect multiple sectors and disciplines, and the public at large. With expanded analytical capacities at hand, Big Data is now being used for scientific inquiry and experimentation in nearly every (if not all) disciplines, from the social sciences to the humanities to the natural sciences, and more. Moreover, the use of Big Data has been well established beyond the Ivory Tower. In today’s economy, businesses simply cannot be competitive without engaging Big Data in one way or another in support of operations, management, planning, or simply basic hiring decisions. In all levels of government, Big Data is being used to engage citizens and to guide policy making in pursuit of the interests of the public and society in general. Moreover, the changing nature of Big Data also raises new issues and concerns related to, for example, privacy, liability, security, access, and even the veracity of the data itself. Given the complex issues attending Big Data, there is a real need for a reference book that covers the subject from a multi-disciplinary, cross-sectoral, comprehensive, and international perspective. The Encyclopedia of Big Data will address this need and will be the first of such reference books to do so. Featuring some 500 entries, from Access to Zillow, the Encyclopedia will serve as a fundamental resource for researchers and students, for decision makers and leaders, and for business analysts and purveyors. Developed for those in academia, industry, and government, and others with a general interest in Big Data, the encyclopedia will be aimed especially at those involved in its collection, analysis, and use. Ultimately, the Encyclopedia of Big Data will provide a common platform and language covering the breadth and depth of the topic for different segments, sectors, and disciplines. |
| business analytics in transportation: An Introduction to Business Analytics Ger Koole, 2019 Business Analytics (BA) is about turning data into decisions. This book covers the full range of BA topics, including statistics, machine learning and optimization, in a way that makes them accessible to a broader audience. Decision makers will gain enough insight into the subject to have meaningful discussions with machine learning specialists, and those starting out as data scientists will benefit from an overview of the field and take their first steps as business analytics specialist. Through this book and the various exercises included, you will be equipped with an understanding of BA, while learning R, a popular tool for statistics and machine learning. |
| business analytics in transportation: Business Analytics Jay Liebowitz, 2013-12-19 Together, Big Data, high-performance computing, and complex environments create unprecedented opportunities for organizations to generate game-changing insights that are based on hard data. Business Analytics: An Introduction explains how to use business analytics to sort through an ever-increasing amount of data and improve the decision-making cap |
| business analytics in transportation: A Handbook to Business Analytics Sahil Kohli, Deepanshi Wadhwa, 2023-02-14 Business Analytics has become a crucial aspect of decision-making in the modern business world. With the availability of vast amounts of Data and the increasing use of technology, organizations are now relying more than ever on data-driven insights to drive growth and gain a competitive advantage. In A Handbook to Business Analytics, authors Sahil Kohli and Deepanshi Wadhwa present a comprehensive guide to understanding the principles and practices of Business Analytics. The book covers a wide range of topics, from the basics of Data Collection and Analysis to Visualisation and Decision Analysis. With clear explanations and practical examples, this handbook is designed to be accessible to readers with little or no prior experience in the field. Whether you are a student, professional, or entrepreneur, this book will provide you with the knowledge and skills you need to make informed decisions based on data. By the end of this handbook, you will have a deep understanding of the role of Analytics in Business, the various tools and techniques available for Data Analysis, and how to apply these techniques to real-world business problems. Whether you are looking to build a career in Business Analytics or simply want to gain a competitive advantage in your current role, this book is an essential resource for anyone interested in using Data to drive Business success. |
| business analytics in transportation: Data Science and Business Intelligence for Corporate Decision-Making Dr. P. S. Aithal, 2024-02-09 About the Book: A comprehensive book plan on Data Science and Business Intelligence for Corporate Decision-Making with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes. |
| business analytics in transportation: Business Analytics Dr. K. Soundararajan, Dr. Kadhirvel Ramasamy , 2022-03-03 Buy E-Book of Business Analytics Book For MBA 2nd Semester of Anna University, Chennai |
| business analytics in transportation: Navigating the Technological Tide: The Evolution and Challenges of Business Model Innovation Bahaaeddin Alareeni, |
| business analytics in transportation: Big Data Driven Supply Chain Management Nada R. Sanders, 2014-05-07 Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance. |
| business analytics in transportation: 65 Case Study Ideas In Production,operation,supply Chain And Logistics Management Dr. Asim Kumar Bandyopadhyay, 2022-04-26 This book is a compilation of case study ideas in the areas of production, operation, supply chain and logistics management (POM-SCL) ,also incorporating operation strategies. This book has been designed conforming to the standards of this common subject for the courses of Master of Business Administration (MBA) and Post Graduate Diploma in Management (PGDM) prescribed by the All India Council of Technical Education (AICTE) and University Grants Commission (UGC). The book consists of sixty five case study ideas covering almost the entire gamut of the subject concerned. All these cases are based on Indian organizations/industries using the characters with names typically Indian and the narrations of these cases reflect Indian work culture, value systems and ethos. All these cases are followed by a set of about half a dozen questions pertaining to the narrations with the corresponding answers suggested for the guidance of the teachers and the students alike. Apart from making the book very convenient and handy for studying the subject, it is also aimed at dispelling fears and apprehensions among the students with non –science/non-technical background that they seem to have about this subject. Getting familiar with the numerical exercises given and solved in this book by practice will make passing this subject a cakewalk for any average student. The cases are so designed as to expose the students to the realities and challenges to the actual professional life while still seating in the class rooms, much before entering a professional career. The book provides a colossal value addition as both a complement and a supplement to the theories in the text books. |
| business analytics in transportation: Handbook of Big Data Research Methods Shahriar Akter, Samuel Fosso Wamba, 2023-06-01 This state-of-the-art Handbook provides an overview of the role of big data analytics in various areas of business and commerce, including accounting, finance, marketing, human resources, operations management, fashion retailing, information systems, and social media. It provides innovative ways of overcoming the challenges of big data research and proposes new directions for further research using descriptive, diagnostic, predictive, and prescriptive analytics. |
| business analytics in transportation: Handbook of Research on Reinventing Economies and Organizations Following a Global Health Crisis Costa, Teresa Gomes da, Lisboa, Inês, Teixeira, Nuno Miguel, 2021-06-11 Due to the global health crisis, economies had to adapt to combat pandemic situations. In the present pandemic crisis, new legislation, methods, labor approaches, values, and social behaviors have emerged with a huge impact in all organizations. However, countries have applied different solutions, procedures, and rules to deal with crises. Therefore, the impact has been different per country. Organizations need to understand their customers and businesses not only to increase operational efficiency but also to increase stakeholder’s satisfaction and their competitiveness in a sustainable way. Customers are becoming more exigent and markets more complex, calling for the need for higher differentiation. This was enhanced in this pandemic situation, and to survive, organizations needed to change and adapt to the new normal. The Handbook of Research on Reinventing Economies and Organizations Following a Global Health Crisis deals with management and economic issues, particularly with the reinvention of businesses and economies due to the pandemic situation and the relevance of entrepreneurship, innovation, and intensive knowledge used to deal with these changes. This book emphasizes the challenges, difficulties, and opportunities for the success of businesses and economies in periods of crisis and provides information for dealing with entrepreneurship and innovation, networks, and complementarities to recover businesses. The chapters also point out possible opportunities, challenges, and risks in the process of recovery highlighting innovation, internationalization, technology, and intensive knowledge in promoting economies and companies’ competitiveness. This book is ideal for entrepreneurs, managers, economists, directors, shareholders, researchers, academicians, and students interested in how businesses reinvent and recover following a global health crisis. |
| business analytics in transportation: Sustainable Automated and Connected Transport Nikolas Thomopoulos, Maria Attard, Yoram Shiftan, 2024-06-04 This volume is a valuable source of ACT information for developing holistic research methods and global policies for making progress towards the SDGs. |
| business analytics in transportation: Business Analytics for Professionals Alp Ustundag, Emre Cevikcan, Omer Faruk Beyca, 2022-05-09 This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python. |
| business analytics in transportation: Data Mining for Business Analytics Galit Shmueli, Peter C. Bruce, Nitin R. Patel, 2016-04-18 An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition ...full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing.– Research Magazine Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature. – ComputingReviews.com Excellent choice for business analysts...The book is a perfect fit for its intended audience. – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years. |
| business analytics in transportation: The Routledge Handbook of Public Transport Corinne Mulley, John Nelson, Stephen Ison, 2021-05-12 The Routledge Handbook of Public Transport is a reference work of chapters providing in-depth examination of the current issues and future developments facing public transport. Chapters in this book are dedicated to specific key topics, identifying the challenges therein and pointing to emerging areas of research and concern. The content is written by an international group of expert contributors and is enhanced through contributions from practitioners to deliver a broader perspective. The Handbook deals with public transport policy context, modal settings, public transport environment, public transport delivery issues, smart card data for planning and the future of public transport. This comprehensive reference work will be a vital source for academics, researchers and transport practitioners in public transport management, transport policy and transport planning. |
Smart urban transport and logistics: A business analytics …
In this study, we focus on the area of UTL by reviewing research topics motivated by innovative devel-opments (Section 2), discussing how data and analytical techniques are integrated in …
Systematic Review of Business Analytics Platforms in …
Business analytics (BA) platforms have become critical tools in driving operational efficiency across transportation and supply chain sectors. This systematic review
The Democratization of Analytics for Transportation and …
Unlocking the Analytics Advantage..... 8 This is the first in a three-part series on using analytics in transportation and logistics to achieve a competitive advantage: Part One: The …
The role of Analytics in Smart Cities and Intelligent …
What transit usage and parking utilization is correlation? Which transit routes (O&D) are most correlated with parking demand? What is the real origin / real destination for transit travelers? …
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In this paper, we discuss how Analytics is leveraged in the transportation industry to create business value. The real-life case studies presented in this paper cover Rail, Trucking and Airline.
ANALYTICS, AUTOMATION AND AUGMENTED …
Transportation and Logistics with Unisys Logistics companies have traditionally underinvested in IT and OT, leaving holes in security and limiting their ability to grow and transform. Introducing …
Leveraging Data Analytics for Strategic Logistics Decision-
organizations are turning to data analytics to enhance decision-making capabilities. This paper explores how data analytics is being leveraged for strategic logistics decisions, encompassing …
BUSINESS ANALYTICS - daltonstate.edu
How Could Big Data Analytics Be Leveraged In Transportation and Logistics? Like so many business segments, the transportation industry is facing many changes and challenges, …
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Efficient management in transportation allows organizations to handle complicated situations. The studies in my dissertation focus on developing novel methodologies, strategies and decisions …
Big Data Analytics, Connected Vehicles and Smart Cities
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Introduction to Business Analytics - Anasayfa
Analytics in Air Transportation • Sojern is collecting information across airlines, hotels, rental car agencies and credit card companies determine "when people go, where they go, how many …
The foundation of data-driven transportation
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Applications of Business Analytics in Predicting Flight On …
In order to handle flight-delay issues, advanced business analyt-ics techniques are needed to support the FAA and airport executives in making useful business decisions.
Big Data and Analytics: Transportation Implications
Feb 22, 2019 · Data Analytics gives business greater insight than ordinarily available in traditional business intelligence (BI)... techniques include machine learning, data mining, predictive …
The Transportation & Logistics Outlook - DAT Freight
Understanding and predicting the trends in the transportation market requires careful attention to a range of metrics and indicators. Here is a list of key data points to keep an eye on:
Big Data Analytics in Transportation Systems Management …
Transportation efficiency for each dollar spent, supply and demand matching index, transportation agency coordination index, partnership cost- saving index, cost of data storage and …
Big data algorithms and applications in intelligent …
Dec 28, 2019 · Big Data analytics includes the processes of collecting, managing, processing, analyzing and visualizing the continuously evolving data (Marjani et al., 2017). sensor, floating …
Big data and analytics in travel and transportation
Here we reveal ways in which big data solutions can drive business results, allowing travel and transportation companies to rise above the big data hype and reap big benefits.
The Role of Advanced Analytics in Transportation Digital …
Transportation businesses are hard at work simplifying their legacy tech stacks. Transitioning from on-premises data centers to cloud-native, service-oriented infrastructure is fundamental to …
The Transportation & Logistics Outlook - DAT Frei…
A key reason is the rise and wider adoption of transportation market analytics. As velocity and agility become even more essential to supply …
Smart urban transport and logistics: A business analy…
In this study, we focus on the area of UTL by reviewing research topics motivated by innovative devel-opments (Section 2), discussing how data and …
Systematic Review of Business Analytics Platfor…
Business analytics (BA) platforms have become critical tools in driving operational efficiency across transportation and supply chain …
The Democratization of Analytics for Transportati…
Unlocking the Analytics Advantage..... 8 This is the first in a three-part series on using analytics in transportation and logistics to achieve a competitive …
The role of Analytics in Smart Cities and Intellige…
What transit usage and parking utilization is correlation? Which transit routes (O&D) are most correlated with parking demand? What is the real …
