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Description
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.
Content:-
Preface
Acknowledgments
About the Authors
How to Contact the Authors and Access the Data Sets
Chapter 1: Overview: The Role of Statistics in Transportation Engineering
Acknowledgments
About the Authors
How to Contact the Authors and Access the Data Sets
Chapter 1: Overview: The Role of Statistics in Transportation Engineering
Chapter 2: Graphical Methods for Displaying Data
Chapter 3: Numerical Summary Measures
Chapter 4: Probability and Random Variables
Chapter 5: Common Probability Distributions
Chapter 6: Sampling Distributions
Chapter 7: Inferences: Hypothesis Testing and Interval Estimation
Chapter 8: Other Inferential Procedures: ANOVA and Distribution-Free Tests
Chapter 9: Inferences Concerning Categorical Data
Chapter 10: Linear Regression
Chapter 11: Regression Models for Count Data
Chapter 12: Experimental Design
Chapter 13: Cross-Validation, Jackknife, and Bootstrap Methods for Obtaining Standard Errors
Chapter 14: Bayesian Approaches to Transportation Data A nalysis
Chapter 15: Microsimulation
Appendix: Soft Modeling and Nonparametric
Model Building
INDEX
Model Building
INDEX
Author Details
"Dr. Clifford Spiegelman" is a distinguished professor of statistics at Texas A&M University, where he has been for twenty-three years. Dr. Spiegelman has also been a senior research scientist at Texas Transportation Institute (TTI) for about fifteen years. He held a position at the National Bureau of Standards (now NIST).
"Dr. Eun Sug Park" is a research scientist at TTI, where she has worked for the past nine years. Prior to joining TTI, she was a research associate at the University of Washington’s National Research Center for Statistics and the Environment.
"Dr. Laurence R. Rilett" is a distinguished professor of civil engineering at the University of Nebraska–Lincoln. He also serves as the director of both the U.S. Department of Transportation’s Region VII University Transportation Center (the Mid-America Transportation Center) and the Nebraska Transportation Center. Dr. Rilett received his BASc degree and his MASc degree from the University of Waterloo, and his PhD degree from Queen’s University.
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