Journal Of Transportation And Statistics
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Reliability and Statistics in Transportation and Communication
Author | : Igor Kabashkin |
Publisher | : Springer |
Total Pages | : 823 |
Release | : 2019-03-09 |
Genre | : Technology & Engineering |
ISBN | : 3030124509 |
This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. The book presents the most noteworthy methods and results discussed at the International Conference on Reliability and Statistics in Transportation and Communication (RelStat), which took place in Riga, Latvia on October 17 – 20, 2018. It spans a broad spectrum of topics, from mathematical models and design methodologies, to software engineering, data security and financial issues, as well as practical problems in technical systems, such as transportation and telecommunications, and in engineering education.
Statistical and Econometric Methods for Transportation Data Analysis, Second Edition
Author | : Simon P. Washington |
Publisher | : CRC Press |
Total Pages | : 546 |
Release | : 2010-12-02 |
Genre | : Technology & Engineering |
ISBN | : 142008285X |
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 Techniques for Transportation Engineering
Author | : Kumar Molugaram |
Publisher | : Butterworth-Heinemann |
Total Pages | : 555 |
Release | : 2017-03-03 |
Genre | : Computers |
ISBN | : 0128116420 |
Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges. - Demonstrates how to effectively interpret, summarize, and report transportation data using appropriate statistical descriptors - Teaches how to identify and apply appropriate analysis methods for transportation data - Explains how to evaluate transportation proposals and schemes with statistical rigor
Data Analytics for Intelligent Transportation Systems
Author | : Mashrur Chowdhury |
Publisher | : Elsevier |
Total Pages | : 572 |
Release | : 2024-11-02 |
Genre | : Computers |
ISBN | : 0443138796 |
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
Statistical and Econometric Methods for Transportation Data Analysis
Author | : Simon Washington |
Publisher | : CRC Press |
Total Pages | : 496 |
Release | : 2020-01-30 |
Genre | : Technology & Engineering |
ISBN | : 0429520751 |
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.
The Bureau of Transportation Statistics
Author | : National Research Council |
Publisher | : National Academies Press |
Total Pages | : 161 |
Release | : 1997-09-20 |
Genre | : Transportation |
ISBN | : 030906404X |
How can the Bureau of Transportation Statistics (BTS), the nation's newest federal statistical agency, contribute to the work of the U.S. Department of Transportation (USDOT)? What is the appropriate role for such an agency as a part of a major department? BTS was authorized in 1991 by the Intermodal Surface Transportation Efficiency Act (ISTEA) in recognition of the need for more and better data for transportation officials at local, state, and federal levels. While the USDOT has many long-standing data collection programs for particular transportation modes (highways, rail, air, etc.), it has never had a statistical agency with a mandate to improve the quality and relevance of transportation data for important system-wide, cross-modal analyses of the nation's transportation system. This book examines how BTS can provide statistical leadership for USDOT, define and maintain quality standards for transportation data, and improve data documentation. It considers BTS's role in developing national transportation indicators, coordinating data collection throughout USDOT, filling gaps, identifying user needs, and developing analysis programs for transportation data. Anyone concerned with having high-quality, relevant transportation indicators and other data available for policy planning, evaluation, and research will be interested in this book, as will students of effective government.
Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities
Author | : Moridpour, Sara |
Publisher | : IGI Global |
Total Pages | : 197 |
Release | : 2019-01-11 |
Genre | : Technology & Engineering |
ISBN | : 1522579443 |
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.