Statistical and Econometric Methods for Transportation Data Analysis

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