Statistical and Econometric Methods for Transportation Data Analysis: 2nd Edition (Hardback) book cover

Statistical and Econometric Methods for Transportation Data Analysis

2nd Edition

By Simon P. Washington, Matthew G. Karlaftis, Fred Mannering

Chapman and Hall/CRC

544 pages | 90 B/W Illus.

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Hardback: 9781420082852
pub: 2010-12-02
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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.


The second edition introduces an especially broad set of statistical methods, which are useful not only for transportation modeling but also for modeling in other disciplines. 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.

—Itzhak Ditzian, The American Statistician, November 2011

Praise for the First Edition:

With [an] evolution in methods comes a tremendous need for books that synthesize and extend the usual statistical theory presentation to one suitable for application-oriented audiences. Washington et al.’s book provides an excellent and needed addition to this genre of texts. … an excellent addition to a practicing transportation analyst’s library as well as a perfect companion to a first-year graduate modeling or methods course … this text adroitly fills a very important niche between practice and theory. … I recommend it for most transportation analysts and believe it to be a good, solid addition to the libraries of transportation graduate students.

Journal of Transportation Statistics, Vol. 7, Issue 2/3, 2005

It is well done and well organized, and provides good coverage of all the essential elements of statistical and econometric methods and models applied to transportation … I would highly recommend it to anyone engaged in transportation research. I suspect it will be the definitive text on statistics in transportation for some years to come … I am pleased to have had the opportunity to read the book and look forward to using it in my work in the future.

Technometrics, November 2004

In a time when transportation organizations are gathering unprecedented amounts of data on all aspects of the transportation system performance, transportation professionals need to equip themselves with analytical tools that can adequately handle the uncertainty of that data. This book is quite timely in meeting that need. This book presents the reader with an extensive set of analytical tools that are particularly well-suited for transportation data analysis. … an outstanding and unique contribution to the existing transportation literature. I have no doubt that the book will serve as an important resource for transportation practitioners and researchers, and will play a major role in improving the way in which statistical and econometric methods are currently employed in transportation research. The book is well-organized and well-written, and can serve as an excellent textbook for a number of graduate-level classes in transportation-related disciplines.

Journal of Transportation Engineering, September/October 2004

In summary, the book succeeds in providing a well-written, clear and concise explanation of an array of statistical and econometric methods. The content is presented in a lively and extremely readable manner that conveys a definite sense that the authors truly understand the psyche of the student body that comprises the bulk of the target market.

Maritime Economics & Logistics, (6) 2004

Table of Contents


Statistical Inference I: Descriptive Statistics

Measures of Relative Standing

Measures of Central Tendency

Measures of Variability

Skewness and Kurtosis

Measures of Association

Properties of Estimators

Methods of Displaying Data

Statistical Inference II: Interval Estimation, Hypothesis Testing, and Population Comparisons

Confidence Intervals

Hypothesis Testing

Inferences Regarding a Single Population

Comparing Two Populations

Nonparametric Methods


Linear Regression

Assumptions of the Linear Regression Model

Regression Fundamentals

Manipulating Variables in Regression

Estimate a Single Beta Parameter

Estimate Beta Parameter for Ranges of a Variable

Estimate a Single Beta Parameter for m – 1 of the m Levels of a Variable

Checking Regression Assumptions

Regression Outliers

Regression Model GOF Measures

Multicollinearity in the Regression

Regression Model-Building Strategies

Estimating Elasticities

Censored Dependent Variables—Tobit Model

Box–Cox Regression

Violations of Regression Assumptions

Zero Mean of the Disturbances Assumption

Normality of the Disturbances Assumption

Uncorrelatedness of Regressors and Disturbances Assumption

Homoscedasticity of the Disturbances Assumption

No Serial Correlation in the Disturbances Assumption

Model Specification Errors

Simultaneous-Equation Models

Overview of the Simultaneous-Equations Problem

Reduced Form and the Identification Problem

Simultaneous-Equation Estimation

Seemingly Unrelated Equations

Applications of Simultaneous Equations to Transportation Data

Panel Data Analysis

Issues in Panel Data Analysis

One-Way Error Component Models

Two-Way Error Component Models

Variable-Parameter Models

Additional Topics and Extensions

Background and Exploration in Time Series

Exploring a Time Series

Basic Concepts: Stationarity and Dependence

Time Series in Regression

Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and Extensions

Autoregressive Integrated Moving Average Models

The Box–Jenkins Approach

Autoregressive Integrated Moving Average Model Extensions

Multivariate Models

Nonlinear Models

Latent Variable Models

Principal Components Analysis

Factor Analysis

Structural Equation Modeling

Duration Models

Hazard-Based Duration Models

Characteristics of Duration Data

Nonparametric Models

Semiparametric Models

Fully Parametric Models

Comparisons of Nonparametric, Semiparametric, and Fully Parametric Models


State Dependence

Time-Varying Covariates

Discrete-Time Hazard Models

Competing Risk Models


Count Data Models

Poisson Regression Model

Interpretation of Variables in the Poisson Regression Model

Poisson Regression Model Goodness-of-Fit Measures

Truncated Poisson Regression Model

Negative Binomial Regression Model

Zero-Inflated Poisson and Negative Binomial Regression Models

Random-Effects Count Models

Logistic Regression

Principles of Logistic Regression

The Logistic Regression Model

Discrete Outcome Models

Models of Discrete Data

Binary and Multinomial Probit Models

Multinomial Logit Model

Discrete Data and Utility Theory

Properties and Estimation of MNL Models

The Nested Logit Model (Generalized Extreme Value Models)

Special Properties of Logit Models

Ordered Probability Models

Models for Ordered Discrete Data

Ordered Probability Models with Random Effects

Limitations of Ordered Probability Models

Discrete/Continuous Models

Overview of the Discrete/Continuous Modeling Problem

Econometric Corrections: Instrumental Variables and Expected Value Method

Econometric Corrections: Selectivity-Bias Correction Term

Discrete/Continuous Model Structures

Transportation Application of Discrete/Continuous Model Structures


Random-Parameter Models

Random-Parameters Multinomial Logit Model (Mixed Logit Model)

Random-Parameter Count Models

Random-Parameter Duration Models

Bayesian Models

Bayes’ Theorem

MCMC Sampling-Based Estimation

Flexibility of Bayesian Statistical Models via MCMC Sampling-Based Estimation

Convergence and Identifi ability Issues with MCMC Bayesian Models

Goodness-of-Fit, Sensitivity Analysis, and Model Selection Criterion using MCMC Bayesian Models

Appendix A: Statistical Fundamentals

Appendix B: Glossary of Terms

Appendix C: Statistical Tables

Appendix D: Variable Transformations



About the Authors

Simon P. Washington is the Queensland Transport and Main Roads chair and professor in the School of Urban Development, Faculty of Built Environment and Engineering, Center for Accident Research and Road Safety (CARRS-Q), Faculty of Health at Queensland University of Technology. Dr. Washington is an associate editor of the Journal of Transportation Engineering; area editor of the Journal of Transportation Safety and Security; and an editorial board member of Accident Analysis & Prevention, the Journal of Sustainable Transportation, and Transportation Research: Part A. His research interests include transport mobility safety and risk, travel behavior, urban and transport planning, and transport sustainability.

Matthew G. Karlaftis is an associate professor in the School of Civil Engineering at the National Technical University of Athens. Dr. Karlaftis is European editor of the Journal of Transportation Engineering; an associate editor of the Journal of Infrastructure Systems; and an editorial board member of Transportation Research: Part C, IET Intelligent Transport Systems, Accident Analysis & Prevention, and Transportation Letters. His research areas include public transit operations, urban transportation, and infrastructure management.

Fred L. Mannering is the Charles Pankow Professor of Civil Engineering at Purdue University, where he also holds a courtesy appointment in the Department of Economics. Dr. Mannering is the author of over 100 journal papers and is the editor-in-chief of Transportation Research: Part B. His research interests include the application of econometric and statistical methods to engineering problems, highway safety, transportation economics, automobile demand, and travel behavior.

About the Series

Chapman & Hall/CRC Interdisciplinary Statistics

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General