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 Tools and 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 using statistical 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 Action
Drawing 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.
Table of Contents
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 Data
Box and Whisker Plot
Time Series Plot
Quality Control Plots
Numerical Summary Measures
Measures of Central Tendency
Measures of Relative Standing
Measures of Variability
Measures of Association
Probability and Random Variables
Sample Spaces and Events
Interpretation of Probability
Expectations of Random Variables
Covariances and Correlation of Random Variables
Computing Expected Values of Functions of Random Variables
Common Probability Distributions
Appendix: Table of the Most Popular Distributions in Transportation Engineering
Sampling Distribution of a Sample Mean
Sampling Distribution of a Sample Variance
Sampling Distribution of a Sample Proportion
Inferences: Hypothesis Testing and Interval Estimation
Fundamentals of Hypothesis Testing
Inferences on a Single Population Mean
Inferences about Two Population Means
Inferences about One Population Variance
Inferences about Two Population Variances
Appendix: Welch (1938) Degrees of Freedom for the Unequal Variance t-Test
Other Inferential Procedures: ANOVA and Distribution-Free Tests
Comparisons of More than Two
One- and Multiway ANOVA
Assumptions for ANOVA
Inferences Concerning Categorical Data
Tests and Confidence Intervals for a Single Proportion
Tests and Confidence Intervals for Two Proportions
Chi-Square Tests Concerning More Than Two Population Proportions
The Chi-Square Goodness-of-Fit Test for Checking Distributional Assumptions
Simple Linear Regression
Understanding and Calculating R2
Verifying the Main Assumptions in Linear Regression
Comparing Two Regression Lines at a Point and Comparing Two Regression Parameters
The Regression Discontinuity Design (RDD)
Multiple Linear Regression
Variable Selection for Regression Models
Additional Collinearity Issues
Regression Models for Count Data
Poisson Regression Model
Assessing Goodness of Fit of Poisson Regression Models
Negative Binomial Regression Model
Appendix: Maximum Likelihood Estimation
Comparison of Direct Observation and Designed Experiments
Motivation for Experimentation
A Three-Factor, Two Levels per Factor Experiment
Fractional Factorial Experiments
D-Optimal and I-Optimal Designs
Sample Size Determination
Field and Quasi-Experiments
Appendix: Choice Modeling of Experiments
Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors
Methods for Standard Error Estimation When a Closed-Form Formula Is Not Available
The Jackknife Method for Obtaining Standard Errors
Bayesian Approaches to Transportation Data Analysis
Fundamentals of Bayesian Statistics
Overview of Traffic Microsimulation Models
Analyzing Microsimulation Output
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.
In this treatment of statistics specifically directed to transportation planners and engineers, Spiegelman and other transportation experts discuss the basics of statistical and graphical methods, differences between methodologies, strategies for conducting computer-aided statistical designs (using JMP software by SAS), bias-corrected confidence intervals, re-sampling techniques for evaluating uncertainties, and the concepts of Bayesian estimation and smoothing estimators. They also overview increasingly used traffic microsimulation models. The text includes homework problems, appended information on soft modeling and nonparametric model building, and a companion website for access to data sets.
—SciTech Book News, February 2011