The art of regression modeling in road safety. (2015)
- Record Type:
- Book
- Title:
- The art of regression modeling in road safety. (2015)
- Main Title:
- The art of regression modeling in road safety
- Further Information:
- Note: Ezra Hauer.
- Authors:
- Hauer, E (Ezra)
- Contents:
- Preface; Contents; Glossary; 1: What Is What; 1.1 Units and Their Safety Property; 1.2 Safety, Traits, and Populations; Notation to get used to:; 1.3 What Are Needed for; 1.4 How Are Used: Numerical Examples; 1.4.1 Data for Two Populations; 1.4.2 Estimating E{mu} and sigma{mu}; 1.4.3 How Many High-mu Units Are There?; 1.4.4 The Performance of a Screen; 1.4.5 Estimating the mu of a Unit; 1.4.6 Is the Gamma Assumption Sensible?; 1.5 The Chosen Perspective; 1.6 Summary; References; 2: A Safety Performance Function for Real Populations; 2.1 The Origin; 2.2 The Estimate of E{mu} 2.3 The Estimate of sigma{mu}2.4 The Two sigma´s; Homogeneity Versus Accuracy; 2.5 Summary; References; 3: Exploratory Data Analysis; 3.1 Introduction; 3.2 The Data; 3.3 The Pivot Table; 3.4 Pausing for Reflection; 3.5 Visualization; 3.6 Terrain; 3.7 Summary; References; 4: Curve-Fitting; 4.1 Why Do We Need to Curve-Fit?; 4.2 There is No Free Lunch; 4.3 Kernel Regression; 4.3.1 Bandwidth and Goodness of Fit; 4.3.2 Adding a Variable; 4.4 Summary; References; 5: Preparing for Parametric Curve-Fitting: The ``Solver´´; 5.1 Optimization in Modeling; 5.2 Using the Solver to Find Minima and Maxima. 5.3 Solver for Curve-Fitting: An Example5.4 Initial Guess and Parameter Scaling; 5.5 Summary; References; 6: A First Parametric SPF; 6.1 The Approach to Parametric SPF Modeling; The main difficulties:; 6.2 A Simple Parametric SPF; 6.3 Preparing and Using the First Curve-Fitting Spreadsheet; 6.4 Modifying the ObjectivePreface; Contents; Glossary; 1: What Is What; 1.1 Units and Their Safety Property; 1.2 Safety, Traits, and Populations; Notation to get used to:; 1.3 What Are Needed for; 1.4 How Are Used: Numerical Examples; 1.4.1 Data for Two Populations; 1.4.2 Estimating E{mu} and sigma{mu}; 1.4.3 How Many High-mu Units Are There?; 1.4.4 The Performance of a Screen; 1.4.5 Estimating the mu of a Unit; 1.4.6 Is the Gamma Assumption Sensible?; 1.5 The Chosen Perspective; 1.6 Summary; References; 2: A Safety Performance Function for Real Populations; 2.1 The Origin; 2.2 The Estimate of E{mu} 2.3 The Estimate of sigma{mu}2.4 The Two sigma´s; Homogeneity Versus Accuracy; 2.5 Summary; References; 3: Exploratory Data Analysis; 3.1 Introduction; 3.2 The Data; 3.3 The Pivot Table; 3.4 Pausing for Reflection; 3.5 Visualization; 3.6 Terrain; 3.7 Summary; References; 4: Curve-Fitting; 4.1 Why Do We Need to Curve-Fit?; 4.2 There is No Free Lunch; 4.3 Kernel Regression; 4.3.1 Bandwidth and Goodness of Fit; 4.3.2 Adding a Variable; 4.4 Summary; References; 5: Preparing for Parametric Curve-Fitting: The ``Solver´´; 5.1 Optimization in Modeling; 5.2 Using the Solver to Find Minima and Maxima. 5.3 Solver for Curve-Fitting: An Example5.4 Initial Guess and Parameter Scaling; 5.5 Summary; References; 6: A First Parametric SPF; 6.1 The Approach to Parametric SPF Modeling; The main difficulties:; 6.2 A Simple Parametric SPF; 6.3 Preparing and Using the First Curve-Fitting Spreadsheet; 6.4 Modifying the Objective Function; 6.5 Estimating sigma{mu}; 6.6 The Accuracy of Parameter Estimates; There are two sources of uncertainty surrounding parameter estimates:; 6.6.1 The Statistical Inaccuracy of beta1 ; 6.6.2 The Incompleteness of ``Statistical Inaccuracy´´ 6.7 Regression, Design Choices, Interventions, and Safety Effect6.7.1 A Road Design Example; 6.7.2 A Speed-and-Safety Example; 6.7.3 A Generalization; 6.7.4 The Debate; 6.8 Summary; References; 7: Which Fit Is Fitter; 7.1 Goodness of Fit; 7.2 The CURE Plot; 7.3 The Bias-in-Fit; 7.4 Leveling the Playing Field; 7.5 When Is a CURE Plot Good Enough?; 7.6 Comparing CURE Plots; 7.7 Summary; References; 8: What to Optimize?; 8.1 Introduction; 8.2 Likelihood; 8.2.1 The Parameter Behind Poisson Accident Counts; 8.2.2 The Parameters Behind the NB Distribution; 8.3 A Few Likelihood Functions. 8.3.1 The Poisson Likelihood Function8.3.2 The Negative Binomial Likelihood Function; 8.3.3 The Negative Multinomial Likelihood Function; 8.4 Alternative Objective Functions; 8.5 Summary; References; 9: Adding Variables; 9.1 When to Add a Variable; 9.1.1 The Necessary Conditions; 9.1.2 The Sufficient Condition; 9.2 The Variable Introduction EDA: Is AADT Safety Related?; 9.3 How to Add a Variable to the C-F Spreadsheet; 9.4 The Omitted Variable Bias; 9.5 A Few CURE Plots; 9.6 Adding Variables: Terrain; 9.7 Panel Data and the NM Likelihood; 9.8 Panel Data and Alternative Objective Functions. … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2015
- Copyright Date:
- 2015
- Extent:
- 1 online resource (241 pages)
- Subjects:
- 519 519.5 620 658.56
Engineering
Engineering & Applied Sciences
Civil & Environmental Engineering
Applied Mathematics
Civil Engineering
Regression analysis
Traffic safety -- Statistical methods
Statistics
Quality control
Reliability
Industrial safety
Transportation engineering
Traffic engineering
Engineering
Transportation Technology and Traffic Engineering
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Quality Control, Reliability, Safety and Risk
Engineering
Industrial safety
Quality control
Regression analysis
Reliability
Statistics
Traffic engineering
Traffic safety -- Statistical methods
Transportation engineering
Mathematics -- Probability & Statistics -- General
Technology & Engineering -- Quality Control
Probability & statistics
Reliability engineering
System safety
Technology & Engineering -- Civil -- General
Highway & traffic engineering
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9783319125299
- Related ISBNs:
- 331912529X
3319125281
9783319125282 - Notes:
- Note: Includes bibliographical references at the end of each chapters and index.
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- British Library HMNTS - ELD.DS.361231
- Ingest File:
- 02_340.xml