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| statistical and econometric methods for transportation data analysis: Statistical and Econometric Methods for Transportation Data Analysis Simon P. Washington, Matthew G. Karlaftis, Fred Mannering, 2010-12-02 The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics. After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models. Along with an entirely new section on other statistical methods, this edition offers a wealth of new material. New to the Second Edition A subsection on Tobit and censored regressions An explicit treatment of frequency domain time series analysis, including Fourier and wavelets analysis methods New chapter that presents logistic regression commonly used to model binary outcomes New chapter on ordered probability models New chapters on random-parameter models and Bayesian statistical modeling New examples and data sets Each chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book’s CRC Press web page. PowerPoint and Word presentations for each chapter are also available for download. |
| statistical and econometric methods for transportation data analysis: 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. |
| statistical and econometric methods for transportation data analysis: 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. |
| statistical and econometric methods for transportation data analysis: Transportation Systems Planning Konstadinos G. Goulias, 2002-12-26 Transportation engineering and transportation planning are two sides of the same coin aiming at the design of an efficient infrastructure and service to meet the growing needs for accessibility and mobility. Many well-designed transport systems that meet these needs are based on a solid understanding of human behavior. Since transportation systems |
| statistical and econometric methods for transportation data analysis: Econometrics in Theory and Practice Panchanan Das, 2019-09-05 This book introduces econometric analysis of cross section, time series and panel data with the application of statistical software. It serves as a basic text for those who wish to learn and apply econometric analysis in empirical research. The level of presentation is as simple as possible to make it useful for undergraduates as well as graduate students. It contains several examples with real data and Stata programmes and interpretation of the results. While discussing the statistical tools needed to understand empirical economic research, the book attempts to provide a balance between theory and applied research. Various concepts and techniques of econometric analysis are supported by carefully developed examples with the use of statistical software package, Stata 15.1, and assumes that the reader is somewhat familiar with the Strata software. The topics covered in this book are divided into four parts. Part I discusses introductory econometric methods for data analysis that economists and other social scientists use to estimate the economic and social relationships, and to test hypotheses about them, using real-world data. There are five chapters in this part covering the data management issues, details of linear regression models, the related problems due to violation of the classical assumptions. Part II discusses some advanced topics used frequently in empirical research with cross section data. In its three chapters, this part includes some specific problems of regression analysis. Part III deals with time series econometric analysis. It covers intensively both the univariate and multivariate time series econometric models and their applications with software programming in six chapters. Part IV takes care of panel data analysis in four chapters. Different aspects of fixed effects and random effects are discussed here. Panel data analysis has been extended by taking dynamic panel data models which are most suitable for macroeconomic research. The book is invaluable for students and researchers of social sciences, business, management, operations research, engineering, and applied mathematics. |
| statistical and econometric methods for transportation data analysis: Probability Theory and Statistical Inference Aris Spanos, 2019-09-19 Doubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research methods, teaching the probabilistic and statistical foundations that enable the specification and validation of statistical models, providing the basis for an informed implementation of statistical procedure to secure the trustworthiness of evidence. Each chapter has been thoroughly updated, accounting for developments in the field and the author's own research. The comprehensive scope of the textbook has been expanded by the addition of a new chapter on the Linear Regression and related statistical models. This new edition is now more accessible to students of disciplines beyond economics and includes more pedagogical features, with an increased number of examples as well as review questions and exercises at the end of each chapter. |
| statistical and econometric methods for transportation data analysis: Statistical and Econometric Methods for Transportation Data Analysis Simon P. Washington, Matthew G. Karlaftis, Fred L. Mannering, 2003-04-29 As the field of transportation moves toward the total quality management paradigm, performance-based outcomes and quantitative measures have become increasingly important. Measuring performance in the field depends heavily on modeling trends and data, which in turn requires powerful, and flexible analytical tools. To date, however, transportation professionals have lacked a unified, rigorous guide to modeling the wide range of problems they encounter in the field. Statistical and Econometric Methods for Transportation Data describes the techniques most useful for modeling the many complex aspects of transportation, such as travel demand, safety, emissions, and the environment. Taking care not to overwhelm readers with statistical theory, the authors clearly and concisely present the relevant analytical methods in quantitative chapters built on transportation case studies. Mastering this material enables readers to: Formulate research hypotheses Identify appropriate statistical and econometric models Avoid common pitfalls and misapplications of statistical methods Interpret model results correctly Ideal as both a textbook and reference, this book makes three unique contributions to transportation practice and education. First, it presents a host of analytical techniques-both common and sophisticated-used to model transportation phenomena. Second, it provides a wealth of examples and case studies, and third, it specifically targets present and future transportation professionals. It builds the foundation they need not only to apply analytical models but also to understand and interpret results published elsewhere. |
| statistical and econometric methods for transportation data analysis: Panel Data Econometrics Mike Tsionas, 2019-06-19 Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made. - Provides a vast array of empirical applications useful to practitioners from different application environments - Accompanied by extensive case studies and empirical exercises - Includes empirical chapters accompanied by supplementary code in R, helping researchers replicate findings - Represents an accessible resource for diverse industries, including health, transportation, tourism, economic growth, and banking, where researchers are not always econometrics experts |
| statistical and econometric methods for transportation data analysis: Microeconometrics A. Colin Cameron, Pravin K. Trivedi, 2005-05-09 This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets. |
| statistical and econometric methods for transportation data analysis: Urban Informatics Wenzhong Shi, Michael F. Goodchild, Michael Batty, Mei-Po Kwan, Anshu Zhang, 2021-04-06 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. |
| statistical and econometric methods for transportation data analysis: Transportation Statistics Sarojini Balachandran, 1976 |
| statistical and econometric methods for transportation data analysis: Statistical and Econometric Methods for Transportation Data Analysis Simon Washington, 2003 |
| statistical and econometric methods for transportation data analysis: Discrete Choice Methods with Simulation Kenneth Train, 2009-07-06 This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing. |
| statistical and econometric methods for transportation data analysis: Introduction to Statistical and Machine Learning Methods for Data Science Carlos Andre Reis Pinheiro, Mike Patetta, 2021-08-06 Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have. |
| statistical and econometric methods for transportation data analysis: Econometric Analysis of Count Data Rainer Winkelmann, 2013-11-27 This monograph deals with econometric models for the analysis of event counts. The interest of econometricians in this class of models has started in the mid-eighties. After more than one decade of intensive research, the litera ture has reached a level of maturity that calls for a systematic and accessible exposition of the main results and methods. Such an exposition is the aim of the book. Count data models have found their way into the curricula of micro-econometric classes and are available on standard computer software. The basic methods have been used in countless applications in fields such as labor economics, health economics, insurance economics, urban economics, and economic demography, to name but a few. Other, more recent, methods are poised to become standard tools soon. While the book is oriented towards the empirical economists and applied econometrician, it should be useful to statisticians and biometricians as well. A first edition of this book was published in 1994 under the title Count Data Models - Econometric Theory and an Application to Labor Mobility . While this edition keeps the character and broad organization of this first edition, and its emphasis on combining a summary of the existing literature with several new results and methods, it is substantially revised and enlarged. Many parts have been completely rewritten and several new sections have New sections include: count data models for dependent processes; been added. |
| statistical and econometric methods for transportation data analysis: Statistical Methods for Categorical Data Analysis Daniel A. Powers, Yu Xie, 2000 Statistical Methods for Categorical Data Analysis is designed as an accessible reference work and textbook about categorical data (that is, data arising from counts instead of measurement. Examples include data about birth, death, marriage, and so forth). It integrates statistical and econometric approaches to the analysis of limited and categorical dependent variables, thereby offering a practical, mathematically uncomplicated approach to the topics of modern data analysis. The volume offers a comprehensive presentation of many different models in a one-volume format (with website). Two features distinguish this book from other analyses of categorical data. First, the authors present both the transformational and latent variable approaches and so synthesize similar methods in statistical and econometric literatures. Second, the book has an applied orientation and features actual examples from social science research. The authors keep discussions of theory to a minimum. Key Features * Exercises and examples utilize popular data already familiar to many social scientists * Examples of the use of various popular software packages * Includes non-standard applications of existing software for estimating models which cannot be handled directly using existing pre-programmed software |
| statistical and econometric methods for transportation data analysis: Optimal Traffic Control Slobodan Guberinic, Gordana Senborn, Bratislav Lazic, 2007-11-08 Despite traffic circles, four-way stop signs, lights regulated by timers or sensors, and other methods, the management of urban intersections remains problematic. Consider that transportation systems have all the features of so-called complex systems: the great number of state and control variables, the presence of uncertainty and indeterminism, th |
| statistical and econometric methods for transportation data analysis: Statistics and Econometric Models Christian Gourieroux, 1995 |
| statistical and econometric methods for transportation data analysis: An Introduction to Statistical Methods Horace Secrist, 1917 |
| statistical and econometric methods for transportation data analysis: Transportation Planning Handbook ITE (Institute of Transportation Engineers), Michael D. Meyer, 2016-07-11 A multi-disciplinary approach to transportation planning fundamentals The Transportation Planning Handbook is a comprehensive, practice-oriented reference that presents the fundamental concepts of transportation planning alongside proven techniques. This new fourth edition is more strongly focused on serving the needs of all users, the role of safety in the planning process, and transportation planning in the context of societal concerns, including the development of more sustainable transportation solutions. The content structure has been redesigned with a new format that promotes a more functionally driven multimodal approach to planning, design, and implementation, including guidance toward the latest tools and technology. The material has been updated to reflect the latest changes to major transportation resources such as the HCM, MUTCD, HSM, and more, including the most current ADA accessibility regulations. Transportation planning has historically followed the rational planning model of defining objectives, identifying problems, generating and evaluating alternatives, and developing plans. Planners are increasingly expected to adopt a more multi-disciplinary approach, especially in light of the rising importance of sustainability and environmental concerns. This book presents the fundamentals of transportation planning in a multidisciplinary context, giving readers a practical reference for day-to-day answers. Serve the needs of all users Incorporate safety into the planning process Examine the latest transportation planning software packages Get up to date on the latest standards, recommendations, and codes Developed by The Institute of Transportation Engineers, this book is the culmination of over seventy years of transportation planning solutions, fully updated to reflect the needs of a changing society. For a comprehensive guide with practical answers, The Transportation Planning Handbook is an essential reference. |
| statistical and econometric methods for transportation data analysis: Optimal Transport Methods in Economics Alfred Galichon, 2018-08-14 Optimal Transport Methods in Economics is the first textbook on the subject written especially for students and researchers in economics. Optimal transport theory is used widely to solve problems in mathematics and some areas of the sciences, but it can also be used to understand a range of problems in applied economics, such as the matching between job seekers and jobs, the determinants of real estate prices, and the formation of matrimonial unions. This is the first text to develop clear applications of optimal transport to economic modeling, statistics, and econometrics. It covers the basic results of the theory as well as their relations to linear programming, network flow problems, convex analysis, and computational geometry. Emphasizing computational methods, it also includes programming examples that provide details on implementation. Applications include discrete choice models, models of differential demand, and quantile-based statistical estimation methods, as well as asset pricing models. Authoritative and accessible, Optimal Transport Methods in Economics also features numerous exercises throughout that help you develop your mathematical agility, deepen your computational skills, and strengthen your economic intuition. The first introduction to the subject written especially for economists Includes programming examples Features numerous exercises throughout Ideal for students and researchers alike |
| statistical and econometric methods for transportation data analysis: Applied Econometrics with R Christian Kleiber, Achim Zeileis, 2008-12-10 R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research. |
| statistical and econometric methods for transportation data analysis: Matching, Regression Discontinuity, Difference in Differences, and Beyond Myoung-jae Lee, 2016 Myoung-jae Lee reviews the three most popular methods (and their extensions) in applied economics and other social sciences: matching, regression discontinuity, and difference in differences. This book introduces the underlying econometric and statistical ideas, shows what is identified and how the identified parameters are estimated, and illustrates how they are applied with real empirical examples. Lee emphasizes how to implement the three methods with data: data and programs are provided in a useful online appendix. All readers-theoretical econometricians/statisticians, applied economists/social-scientists and researchers/students-will find something useful in the book from different perspectives. |
| statistical and econometric methods for transportation data analysis: Urban Transportation Networks Yosef Sheffi, 1984 |
| statistical and econometric methods for transportation data analysis: Making Sense of Statistical Methods in Social Research Keming Yang, 2010-03-25 Making Sense of Statistical Methods in Social Research is a critical introduction to the use of statistical methods in social research. It provides a unique approach to statistics that concentrates on helping social researchers think about the conceptual basis for the statistical methods they′re using. Whereas other statistical methods books instruct students in how to get through the statistics-based elements of their chosen course with as little mathematical knowledge as possible, this book aims to improve students′ statistical literacy, with the ultimate goal of turning them into competent researchers. Making Sense of Statistical Methods in Social Research contains careful discussion of the conceptual foundation of statistical methods, specifying what questions they can, or cannot, answer. The logic of each statistical method or procedure is explained, drawing on the historical development of the method, existing publications that apply the method, and methodological discussions. Statistical techniques and procedures are presented not for the purpose of showing how to produce statistics with certain software packages, but as a way of illuminating the underlying logic behind the symbols. The limited statistical knowledge that students gain from straight forward ′how-to′ books makes it very hard for students to move beyond introductory statistics courses to postgraduate study and research. This book should help to bridge this gap. |
| statistical and econometric methods for transportation data analysis: Transportation Decision Making Kumares C. Sinha, Samuel Labi, 2011-09-09 This pioneering text provides a holistic approach to decisionmaking in transportation project development and programming, whichcan help transportation professionals to optimize their investmentchoices. The authors present a proven set of methodologies forevaluating transportation projects that ensures that all costs andimpacts are taken into consideration. The text's logical organization gets readers started with asolid foundation in basic principles and then progressively buildson that foundation. Topics covered include: Developing performance measures for evaluation, estimatingtravel demand, and costing transportation projects Performing an economic efficiency evaluation that accounts forsuch factors as travel time, safety, and vehicle operatingcosts Evaluating a project's impact on economic development and landuse as well as its impact on society and culture Assessing a project's environmental impact, including airquality, noise, ecology, water resources, and aesthetics Evaluating alternative projects on the basis of multipleperformance criteria Programming transportation investments so that resources can beoptimally allocated to meet facility-specific and system-widegoals Each chapter begins with basic definitions and concepts followedby a methodology for impact assessment. Relevant legislation isdiscussed and available software for performing evaluations ispresented. At the end of each chapter, readers are providedresources for detailed investigation of particular topics. Theseinclude Internet sites and publications of international anddomestic agencies and research institutions. The authors alsoprovide a companion Web site that offers updates, data foranalysis, and case histories of project evaluation and decisionmaking. Given that billions of dollars are spent each year ontransportation systems in the United States alone, and that thereis a need for thorough and rational evaluation and decision makingfor cost-effective system preservation and improvement, this textshould be on the desks of all transportation planners, engineers,and educators. With exercises in every chapter, this text is anideal coursebook for the subject of transportation systems analysisand evaluation. |
| statistical and econometric methods for transportation data analysis: CIGOS 2019, Innovation for Sustainable Infrastructure Cuong Ha-Minh, Dong Van Dao, Farid Benboudjema, Sybil Derrible, Dat Vu Khoa Huynh, Anh Minh Tang, 2019-10-10 This book presents selected articles from the 5th International Conference on Geotechnics, Civil Engineering Works and Structures, held in Ha Noi, focusing on the theme “Innovation for Sustainable Infrastructure”, aiming to not only raise awareness of the vital importance of sustainability in infrastructure development but to also highlight the essential roles of innovation and technology in planning and building sustainable infrastructure. It provides an international platform for researchers, practitioners, policymakers and entrepreneurs to present their recent advances and to exchange knowledge and experience on various topics related to the theme of “Innovation for Sustainable Infrastructure”. |
| statistical and econometric methods for transportation data analysis: Spatial Econometrics: Methods and Models L. Anselin, 2013-03-09 Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis. |
| statistical and econometric methods for transportation data analysis: Applied Compositional Data Analysis Peter Filzmoser, Karel Hron, Matthias Templ, 2018-11-03 This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions. |
| statistical and econometric methods for transportation data analysis: Handbook of Applied Spatial Analysis Manfred M. Fischer, Arthur Getis, 2009-12-24 The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them. |
| statistical and econometric methods for transportation data analysis: Real Estate Modelling and Forecasting Chris Brooks, Sotiris Tsolacos, 2010-04-15 As real estate forms a significant part of the asset portfolios of most investors and lenders, it is crucial that analysts and institutions employ sound techniques for modelling and forecasting the performance of real estate assets. Assuming no prior knowledge of econometrics, this book introduces and explains a broad range of quantitative techniques that are relevant for the analysis of real estate data. It includes numerous detailed examples, giving readers the confidence they need to estimate and interpret their own models. Throughout, the book emphasises how various statistical techniques may be used for forecasting and shows how forecasts can be evaluated. Written by a highly experienced teacher of econometrics and a senior real estate professional, both of whom are widely known for their research, Real Estate Modelling and Forecasting is the first book to provide a practical introduction to the econometric analysis of real estate for students and practitioners. |
| statistical and econometric methods for transportation data analysis: Principles of Highway Engineering and Traffic Analysis Fred L. Mannering, Scott S. Washburn, 2020-07-08 Highly regarded for its clarity and depth of coverage, the bestselling Principles of Highway Engineering and Traffic Analysis provides a comprehensive introduction to the highway-related problems civil engineers encounter every day. Emphasizing practical applications and up-to-date methods, this book prepares students for real-world practice while building the essential knowledge base required of a transportation professional. In-depth coverage of highway engineering and traffic analysis, road vehicle performance, traffic flow and highway capacity, pavement design, travel demand, traffic forecasting, and other essential topics equips students with the understanding they need to analyze and solve the problems facing America’s highway system. This new Seventh Edition features a new e-book format that allows for enhanced pedagogy, with instant access to solutions for selected problems. Coverage focuses exclusively on highway transportation to reflect the dominance of U.S. highway travel and the resulting employment opportunities, while the depth and scope of coverage is designed to prepare students for success on standardized civil engineering exams. |
| statistical and econometric methods for transportation data analysis: Longitudinal and Panel Data , 2004 This focuses on models and data that arise from repeated observations of a cross-section of individuals, households or companies. These models have found important applications within business, economics, education, political science and other social science disciplines. The author introduces the foundations of longitudinal and panel data analysis at a level suitable for quantitatively oriented graduate social science students as well as individual researchers. He emphasizes mathematical and statistical fundamentals but also describes substantive applications from across the social sciences, showing the breadth and scope that these models enjoy. The applications are enhanced by real-world data sets and software programs in SAS and Stata. |
| statistical and econometric methods for transportation data analysis: The Economics of Urban Transportation Kenneth A. Small, Erik T. Verhoef, 2007-10-18 This timely new edition of Kenneth A. Small’s seminal textbook Urban Transportation Economics, co-authored with Erik T. Verhoef, has been fully updated, covering new areas such as parking policies, reliability of travel times, and the privatization of transportation services, as well as updated treatments of congestion modelling, environmental costs, and transit subsidies. Rigorous in approach and making use of real-world data and econometric techniques, it contains case studies from a range of countries including congestion charging in Norway, Singapore and the UK, light rail in the Netherlands and freeway tolls in the US. Small and Verhoef cover all basic topics needed for any application of economics to transportation: forecasting the demand for transportation services under alternative policies measuring all the costs including those incurred by users setting prices under practical constraints choosing and evaluating investments in basic facilities designing ways in which the private and public sectors interact to provide services. This book will be of great interest to students with basic calculus and some knowledge of economic theory who are engaged with transportation economics, planning and, or engineering, travel demand analysis, and many related fields. It will also be essential reading for researchers in any aspect of urban transportation. |
| statistical and econometric methods for transportation data analysis: Meta-Regression Analysis in Economics and Business T.D. Stanley, Hristos Doucouliagos, 2012-08-21 The purpose of this book is to introduce novice researchers to the tools of meta-analysis and meta-regression analysis and to summarize the state of the art for existing practitioners. Meta-regression analysis addresses the rising Tower of Babel that current economics and business research has become. Meta-analysis is the statistical analysis of previously published, or reported, research findings on a given hypothesis, empirical effect, phenomenon, or policy intervention. It is a systematic review of all the relevant scientific knowledge on a specific subject and is an essential part of the evidence-based practice movement in medicine, education and the social sciences. However, research in economics and business is often fundamentally different from what is found in the sciences and thereby requires different methods for its synthesis—meta-regression analysis. This book develops, summarizes, and applies these meta-analytic methods. |
| statistical and econometric methods for transportation data analysis: The SAGE Handbook of Regression Analysis and Causal Inference Henning Best, Christof Wolf, 2013-12-20 ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis. |
| statistical and econometric methods for transportation data analysis: Basic econometrics 3rd ed Gujrati, |
| statistical and econometric methods for transportation data analysis: Introductory Econometrics Jeffrey M. Wooldridge, 2009 INTRODUCTORY ECONOMETRICS: A MODERN APPROACH, 4e International Edition illustrates how empirical researchers think about and apply econometric methods in real-world practice. The text's unique approach reflects the fact that undergraduate econometrics has moved beyond just a set of abstract tools to being genuinely useful for answering questions in business, policy evaluation, and forecasting environments. The systematic approach, which reduces clutter by introducing assumptions only as they are needed, makes absorbing the material easier and leads to better econometric practices. Its unique organization separates topics by the kinds of data being analyzed , leading to an appreciation for the important issues that arise in drawing conclusions from the different kinds of data economists use. Packed with relevant applications, INTRODUCTORY ECONOMETRICS offers a wealth of interesting data sets that can be used to reproduce the examples in the text or as the starting point for original research projects. |
| statistical and econometric methods for transportation data analysis: Nonparametric Regression and Spline Smoothing, Second Edition Randall L. Eubank, 1999-02-09 Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines. |
| statistical and econometric methods for transportation data analysis: Handbook of Computational Econometrics David A. Belsley, Erricos Kontoghiorghes, 2009-08-18 Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in-depth examination of the relevant methodologies and valuable illustrations. This book: Provides self-contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies. Brings together contributions from leading researchers. Develops the techniques needed to carry out computational econometrics. Features network studies, non-parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time-series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation. This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels. |
STATISTICAL Definition & Meaning - Merriam-Webster
The meaning of STATISTICAL is of, relating to, based on, or employing the principles of statistics. How to use statistical in a sentence.
STATISTICAL | English meaning - Cambridge Dictionary
There is very little statistical evidence. It was designed to facilitate the combination of qualitative methods with statistical analysis. The generalizations are advanced on the basis of statistical …
Statistics - Wikipedia
Statistics is the discipline that deals with data, facts and figures with which meaningful information is inferred. Data may represent a numerical value, in form of quantitative data, or a label, as …
STATISTICAL Definition & Meaning | Dictionary.com
of, pertaining to, consisting of, or based on statistics. statistics. Examples have not been reviewed. In doing so, the judges said she could not point to “background circumstances” or …
What is Statistical Analysis? - GeeksforGeeks
Apr 15, 2025 · Statistical Analysis means gathering, understanding, and showing data to find patterns and connections that can help us make decisions. It includes lots of different ways to …
Statistics | Definition, Types, & Importance | Britannica
May 20, 2025 · statistics, the science of collecting, analyzing, presenting, and interpreting data. Governmental needs for census data as well as information about a variety of economic …
Statistical - definition of statistical by The Free Dictionary
Define statistical. statistical synonyms, statistical pronunciation, statistical translation, English dictionary definition of statistical. adj. Of, relating to, or employing statistics or the principles of …
STATISTICAL definition and meaning | Collins English Dictionary
Statistical means relating to the use of statistics. The report contains a great deal of statistical information. Of or relating to statistics.... Click for English pronunciations, examples sentences, …
Introduction to Research Statistical Analysis: An Overview of the ...
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power.
Statistics - Definition, Examples, Mathematical Statistics
Statistics is defined as the process of collection of data, classifying data, representing the data for easy interpretation, and further analysis of data. Statistics also is referred to as arriving at …
STATISTICAL Definition & Meaning - Merriam-Webster
The meaning of STATISTICAL is of, relating to, based on, or employing the principles of statistics. How to use statistical in a sentence.
STATISTICAL | English meaning - Cambridge Dictionary
There is very little statistical evidence. It was designed to facilitate the combination of qualitative methods with statistical analysis. The generalizations are advanced on the basis of statistical …
Statistics - Wikipedia
Statistics is the discipline that deals with data, facts and figures with which meaningful information is inferred. Data may represent a numerical value, in form of quantitative data, or a label, as …
STATISTICAL Definition & Meaning | Dictionary.com
of, pertaining to, consisting of, or based on statistics. statistics. Examples have not been reviewed. In doing so, the judges said she could not point to “background circumstances” or …
What is Statistical Analysis? - GeeksforGeeks
Apr 15, 2025 · Statistical Analysis means gathering, understanding, and showing data to find patterns and connections that can help us make decisions. It includes lots of different ways to …
Statistics | Definition, Types, & Importance | Britannica
May 20, 2025 · statistics, the science of collecting, analyzing, presenting, and interpreting data. Governmental needs for census data as well as information about a variety of economic …
Statistical - definition of statistical by The Free Dictionary
Define statistical. statistical synonyms, statistical pronunciation, statistical translation, English dictionary definition of statistical. adj. Of, relating to, or employing statistics or the principles of …
STATISTICAL definition and meaning | Collins English Dictionary
Statistical means relating to the use of statistics. The report contains a great deal of statistical information. Of or relating to statistics.... Click for English pronunciations, examples sentences, …
Introduction to Research Statistical Analysis: An Overview of the ...
This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power.
Statistics - Definition, Examples, Mathematical Statistics
Statistics is defined as the process of collection of data, classifying data, representing the data for easy interpretation, and further analysis of data. Statistics also is referred to as arriving at …
