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Statistical and Econometric Methods for Transportation Data Analysis
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Table of Contents

FUNDAMENTALS
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

CONTINUOUS DEPENDENT VARIABLE MODELS
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
Heterogeneity
State Dependence
Time-Varying Covariates
Discrete-Time Hazard Models
Competing Risk Models

COUNT AND DISCRETE DEPENDENT VARIABLE 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

OTHER STATISTICAL METHODS
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

References

Index

About the Author

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.

Reviews

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

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