Ezra Hauer

The Art of Regression Modeling in Road Safety

• Gebundenes Buch

Produktbeschreibung

This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety.

This book also:

· Presents for the first time a powerful analytical tool for road safety researchers and practitioners

· Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides

· Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint presentations

· Equips readers to perform all analyses on a spreadsheet without requiring mastery of complex and costly software

· Emphasizes understanding without esoteric mathematics

· Makes assumptions visible and explains their role and consequences

Produktdetails

• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 86338766, 978-3-319-12528-2
• 2015
• Seitenzahl: 252
• Erscheinungstermin: 7. Januar 2015
• Englisch
• Abmessung: 241mm x 160mm x 20mm
• Gewicht: 536g
• ISBN-13: 9783319125282
• ISBN-10: 3319125281
• Artikelnr.: 41483747

Autorenporträt

Ezra Hauer is Professor Emeritus in the Department of Civil Engineering, University of Toronto, Canada

Inhaltsangabe

What is what?- A Safety Performance Function for real populations.- Exploratory data analysis.- Curve fitting.- Preparing for parametric curve-fitting: The 'Solver'.- A first parametric SPF.- Which fit is fitter?- What to optimize?- Adding variables.- Choosing the function behind the data.- Accuracies.- Closure.