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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.
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
526 pages, Hardcover
