Statistical and Econometric Methods for Transportation Data Analysis

2. New Auflage 2010
ISBN: 978-1-4200-8285-2
Verlag: Taylor & Francis Ltd

Buch, Englisch, 544 Seiten, Format (B × H): 161 mm x 241 mm, Gewicht: 919 g

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

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.

Zielgruppe

Transportation planners and engineers; civil engineers; senior undergraduate and graduate students in engineering, urban planning, economics, and sociology; statisticians working in the transportation field.

Autoren/Hrsg.

Washington, Simon P.

Karlaftis, Matthew G.

Mannering, Fred L.

Mannering, Fred

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Transport- und Verkehrswirtschaft
• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure

Inhaltsverzeichnis

FUNDAMENTALSStatistical Inference I: Descriptive StatisticsMeasures 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 ComparisonsConfidence IntervalsHypothesis Testing Inferences Regarding a Single PopulationComparing Two PopulationsNonparametric Methods

CONTINUOUS DEPENDENT VARIABLE MODELSLinear RegressionAssumptions of the Linear Regression ModelRegression FundamentalsManipulating Variables in RegressionEstimate 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 AssumptionsRegression OutliersRegression 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 AssumptionHomoscedasticity of the Disturbances Assumption No Serial Correlation in the Disturbances Assumption Model Specification Errors

Simultaneous-Equation ModelsOverview of the Simultaneous-Equations ProblemReduced Form and the Identification ProblemSimultaneous-Equation Estimation Seemingly Unrelated Equations Applications of Simultaneous Equations to Transportation Data

Panel Data AnalysisIssues in Panel Data AnalysisOne-Way Error Component Models Two-Way Error Component Models Variable-Parameter Models Additional Topics and Extensions

Background and Exploration in Time SeriesExploring a Time SeriesBasic Concepts: Stationarity and DependenceTime Series in Regression

Forecasting in Time Series: Autoregressive Integrated Moving Average (ARIMA) Models and ExtensionsAutoregressive Integrated Moving Average Models The Box–Jenkins ApproachAutoregressive Integrated Moving Average Model ExtensionsMultivariate Models Nonlinear Models

Latent Variable ModelsPrincipal Components Analysis Factor Analysis Structural Equation Modeling

Duration ModelsHazard-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 MODELSCount Data ModelsPoisson 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 RegressionPrinciples of Logistic Regression The Logistic Regression Model

Discrete Outcome ModelsModels of Discrete Data Binary and Multinomial Probit ModelsMultinomial Logit Model Discrete Data and Utility Theory Properties and Estimation of MNL ModelsThe Nested Logit Model (Generalized Extreme Value Models) Special Properties of Logit Models

Ordered Probability ModelsModels for Ordered Discrete Data Ordered Probability Models with Random Effects Limitations of Ordered Probability Models

Discrete/Continuous ModelsOverview of the Discrete/Continuous Modeling Problem Econometric Corrections: Instrumental Variables and Expected Value Method Econometric Corrections: Selectivity-Bias Correction TermDiscrete/Continuous Model Structures Transportation Application of Discrete/Continuous Model Structures

OTHER STATISTICAL METHODSRandom-Parameter ModelsRandom-Parameters Multinomial Logit Model (Mixed Logit Model) Random-Parameter Count Models Random-Parameter Duration Models

Bayesian ModelsBayes’ 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 FundamentalsAppendix B: Glossary of Terms Appendix C: Statistical Tables Appendix D: Variable Transformations
References
Index

Über Autor(innen)

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 editorof 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.