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TitleThe Impact of Red Light Cameras on Injury Crashes within Miami-Dade County, Florida
LanguageEnglish
File Size524.9 KB
Total Pages123
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                            Florida International University
FIU Digital Commons
	4-27-2015
The Impact of Red Light Cameras on Injury Crashes within Miami-Dade County, Florida
	Anthoni Llau
		Recommended Citation
The Impact of Red Light Cameras on Injury Crashes within Miami-Dade County, Florida
                        
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Page 1

Florida International University
FIU Digital Commons

FIU Electronic Theses and Dissertations University Graduate School

4-27-2015

The Impact of Red Light Cameras on Injury
Crashes within Miami-Dade County, Florida
Anthoni Llau
[email protected]

DOI: 10.25148/etd.FIDC000059
Follow this and additional works at: https://digitalcommons.fiu.edu/etd

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Recommended Citation
Llau, Anthoni, "The Impact of Red Light Cameras on Injury Crashes within Miami-Dade County, Florida" (2015). FIU Electronic
Theses and Dissertations. 2240.
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Page 2

FLORIDA INTERNATIONAL UNIVERSITY

Miami, Florida







THE IMPACT OF RED LIGHT CAMERAS ON INJURY CRASHES WITHIN

MIAMI-DADE COUNTY, FLORIDA





A dissertation submitted in partial fulfillment of the

requirements for the degree of

DOCTOR OF PHILOSOPHY

in

PUBLIC HEALTH

by

Anthoni F Llau Jr

2015

Page 61

48

CHAPTER III

MANUSCRIPT 2 – STATISICAL METHODOLOGY (PUBLICATION

ACCEPTED)

IDENTIFICATION OF AN ACCIDENT PREDICTION MODEL FOR RED

LIGHT CAMERA ANALYSES

Page 62

49

MANUSCRIPT 2

Identification of an accident prediction model for red light camera analyses

Abstract

Objectives: Determining whether red light cameras (RLC’s) are effective is difficult for

several reasons. One issue is the phenomenon known as regression to the mean (RTM). If

not accounted for, results may be biased in estimating the benefit of RLCs. Empirical

Bayes (EB) analyses allow researchers to account for RTM by estimating the number of

collisions based on crash counts prior to RLC installation at treatment and comparison

sites. EB methodology requires an accident prediction model which is a multivariate

regression formula that fits collision data for comparison intersections to an independent

set of variables that may be expected to affect safety. Recent crash studies have utilized

Poisson, negative binomial, and gamma regression models to develop accident prediction

models. Since the distribution of motor vehicle crashes can be overdispersed or

underdispersed, the most appropriate model must be determined using goodness of fit

testing. The purpose of this study is to develop an accident prediction model for motor

vehicle crashes occurring in Miami-Dade County, Florida during 2008-2011.

Methods: Motor vehicle crash data were extracted from the Florida Department of

Motor Vehicle and Highway Safety dataset for 40 intersections within Miami-Dade

County, Florida for development of an accident prediction model (i.e. safety performance

function. Each intersection selected was matched at least one of 20 intersections with red

light cameras (RLC’s) using selected geometric variables (number of lanes & speed limit

for major and minor roads) and average daily traffic. In addition, each intersection

examined was at least 2 miles away from any RLC site. The dependent variable

Page 122

109

VITA

ANTHONI F LLAU JR

EDUCATION AND EMPLOYMENT

1995-1999 Bachelor of Science in Nutrition
Florida International University
Miami, Florida


2003-2005 Masters of Public Health
Graduate Certificate in Epidemiology
Florida International University
Miami, Florida

2005-2007 Lead Poisoning Epidemiologist
Florida Department of Health
Miami-Dade County, Florida

2007-2015 Epidemiologist
Florida Department of Health
Miami-Dade County, Florida

2009-2015 Ph.D. Candidate, Public Health
Specialization in Epidemiology
Florida International University
Miami, Florida

PUBLICATIONS AND PRESENTATIONS


Llau AF, Ahmed N, Khan H, Cevallos F, Pekovic V (2015 – In Press). The
impact of red light cameras on crashes within Miami-Dade County. Traffic Injury
Prevention: In Press

Llau AF, Ahmed N. (2014). The effectiveness of red light cameras in the United States,
A literature review. Traffic Injury Prevention: 15(6):542-50

Mann P, O’Connell EK, Zhang G, Llau A, Rico E, Leguen F. (2011). Alert system to
detect possible school-based outbreaks of influenza-like illness. Emerging Infectious
Diseases: 17(2):262-264.

O’Connell EK, Zhang G, Leguen F, Llau A, Rico E (2010) Innovative Uses for
Syndromic Surveillance. Emerging Infectious Diseases: 16(4):669-671

Page 123

110

Guest Presenter, Practicum Epidemiology Methods, Florida International
University - Miami, Florida (2011 – 2015)

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