Transportation Statistics and Microsimulation

By discussing statistical concepts in the context of transportation planning and operations, Transportation Statistics and Microsimulation provides the necessary background for making informed transportation-related decisions. It explains the why behind standard methods and uses real-world transportation examples and problems to illustrate key concepts. The Toolsand Methods to Solve Transportation Problems Classroom-tested at Texas A&M University, the text covers the statistical techniques most frequently employed by transportation and pavement professionals. To familiarize readers with the underlying theory and equations, it contains problems that can be solved usingstatistical software. The authors encourage the use of SAS’s JMP package, which enables users to interactively explore and visualize data. Students can buy their own copy of JMP at a reduced price via a postcard in the book. Practical Examples Show How the Methods Are Used in ActionDrawing on the authors’ extensive application of statistical techniques in transportation research and teaching, this textbook explicitly defines the underlying assumptions of the techniques and shows how they are used in practice. It presents terms from both a statistical and a transportation perspective, making conversations between transportation professionals and statisticians smoother and more productive.

Overview: The Role of Statistics in Transportation Engineering What Is Engineering? What Is Transportation Engineering? Goal of the Textbook Overview of the Textbook Who Is the Audience for This Textbook? Relax—Everything Is Fine Graphical Methods for Displaying DataIntroduction Histogram Box and Whisker Plot Quantile Plot Scatter Plot Parallel Plot Time Series Plot Quality Control PlotsConcluding Remarks Numerical Summary MeasuresIntroduction Measures of Central TendencyMeasures of Relative Standing Measures of Variability Measures of AssociationConcluding Remarks Probability and Random VariablesIntroduction Sample Spaces and Events Interpretation of Probability Random VariableExpectations of Random Variables Covariances and Correlation of Random Variables Computing Expected Values of Functions of Random Variables Conditional Probability Bayes’ Theorem Concluding Remarks Common Probability DistributionsIntroduction Discrete DistributionsContinuous DistributionsConcluding RemarksAppendix: Table of the Most Popular Distributions in Transportation Engineering Sampling DistributionsIntroduction Random Sampling Sampling Distribution of a Sample Mean Sampling Distribution of a Sample Variance Sampling Distribution of a Sample Proportion Concluding Remarks Inferences: Hypothesis Testing and Interval EstimationIntroduction Fundamentals of Hypothesis Testing Inferences on a Single Population MeanInferences about Two Population MeansInferences about One Population Variance Inferences about Two Population VariancesConcluding Remarks Appendix: Welch (1938) Degrees of Freedom for the Unequal Variance t-Test Other Inferential Procedures: ANOVA and Distribution-Free TestsIntroduction Comparisons of More than TwoPopulation Means Multiple Comparisons One- and Multiway ANOVA Assumptions for ANOVA Distribution-Free TestsConclusions Inferences Concerning Categorical DataIntroduction Tests and Confidence Intervals for a Single Proportion Tests and Confidence Intervals for Two Proportions Chi-Square Tests Concerning More Than Two Population ProportionsThe Chi-Square Goodness-of-Fit Test for Checking Distributional Assumptions Conclusions Linear RegressionIntroduction Simple Linear RegressionTransformations Understanding and Calculating R2 Verifying the Main Assumptions in Linear Regression Comparing Two Regression Lines at a Point and Comparing Two Regression ParametersThe Regression Discontinuity Design (RDD) Multiple Linear RegressionVariable Selection for Regression Models Additional Collinearity Issues Concluding Remarks Regression Models for Count DataIntroduction Poisson Regression Model Overdispersion Assessing Goodness of Fit of Poisson Regression Models Negative Binomial Regression Model Concluding Remarks Appendix: Maximum Likelihood Estimation Experimental DesignIntroduction Comparison of Direct Observation and Designed Experiments Motivation for ExperimentationA Three-Factor, Two Levels per Factor ExperimentFactorial Experiments Fractional Factorial Experiments Screening Designs D-Optimal and I-Optimal Designs Sample Size Determination Field and Quasi-Experiments Concluding Remarks Appendix: Choice Modeling of Experiments Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard ErrorsIntroduction Methods for Standard Error Estimation When a Closed-Form Formula Is Not Available Cross-Validation The Jackknife Method for Obtaining Standard Errors Bootstrapping Concluding Remarks Bayesian Approaches to Transportation Data AnalysisIntroduction Fundamentals of Bayesian Statistics Bayesian InferenceConcluding Remarks MicrosimulationIntroduction Overview of Traffic Microsimulation Models Analyzing Microsimulation OutputPerformance MeasuresConcluding Remarks Appendix: Soft Modeling and Nonparametric Model Building Homework Problems and References appear at the end of each chapter.

Clifford Spiegelman is a distinguished professor of statistics at Texas A&M University, where he has been for twenty-three years. Dr. Spiegelman is also a senior research scientist at the Texas Transportation Institute. Eun Sug Park is a research scientist at the Texas Transportation Institute. Dr. Park was a recipient of the TRB Pedestrian Committee Outstanding Paper Award (2006 and 2009) and the Patricia Waller Award (2009). Laurence R. Rilett is a distinguished professor of civil engineering at the University of Nebraska–Lincoln. He also is the director of both the U.S. Department of Transportation’s Region VII University Transportation Center and the Nebraska Transportation Center.