LTC 629-2018 Evaluation of Photo Red Light Enforcement ProgramMIAMI BEACH
OFFICE OF THE CITY MANAGER
NO . LTC # 629-2018 LETIER TO COMMISSION
To : Mayor Dan Gelber and Members
Date : December 7, 2018
Subject : Evaluation of Photo Red Light Enforcement Program
The purpose of this Letter to Commission is to transmit the final report for the Evaluation of
Photo Red Light (PRL) Enforcement Program in the City of Miami Beach submitted by Florida
International University's Lehman Center for Transportation Research on December 7, 2018 .
At the March 2018 Neighborhoods/Community Affairs Comm ittee (NCAC) meeting , Committee
members suggested the evaluation of the City's photo red light camera program data . Florida
International University's Lehman Center for Transportation Research submitted a proposal
specifying the assessment approach for the nine (9) intersections with photo red light cameras
and the intersections to be used as the control group. The cost for the assessment was not to
exceed $13 ,500 with funds from the police departments PRL program to fund the study .
The proposal was approved via Resolution No. 2018-30284 at the April 11 , 2018 Commission
meeting . Dr. Priyanka Alluri, Assistant Professor FIU Lehman Center for Transportation
Research and primary contact for the study , worked with City staff in the Transportation
Department , Police Department, and Organizational Development Department , to collect all
required data. Dr. Alluri provided the completed evaluation to administration on December 7,
2018 .
The ma in objectives of the study were to evaluate the safety and effectiveness of the PRL
Enforcement Program in Miami Beach. The simple before-and-after analysis and the full Bayes
(FB) before-and-after evaluation approach were used to quantify the safety effectiveness of the
RLCs . The analysis was based on target crash type which includes angle/left-turn/right-turn ,
rear-end , and sideswipe crashes, and target crash severity which includes property damage
(PDQ) and fatal/injury crashes.
A total of of ten RLCs are operat ional at nine signalized intersect ions in Miami Beach . Due to
the long period of construction activity , the intersection where Alton Road meets 17 1h Street was
not included in the study . The simple before-and-after crash data analysis was conducted for
the remaining eight signalized intersections . The advanced full Bayes before-and-after analysis
was conducted only for the five four-legged signalized intersections . Only three treatment
intersections are three-legged ; the sample size is too small to yield reliable results from the FB
statistical analys is .
The below are the findings from the simple before-and-after crash data analys is :
• Four-legged Intersections with RLCs
o Reduction in target crashes after the installation of RLCs
o Target crash types angle/left-turn/right-turn and sideswipe crashes usually
decreased while rear-end crashes usually increased
o Reduction in PDO target crashes
• Approaches with RLCs
o At three of the five intersections, target crashes reduced after the installation of
RLCs
o At two of the five intersections, total crashes reduced after the installation of
RLCs
o At three of the five intersections, rear-end crashes increased after the installation
of RLCs
• Three-legged Intersections with RLCs
o Intersections with RLCs experienced a reduction in target crashes after the
installation of RLCs
o At all three intersections, rear-end and sideswipe crashes reduced after the
installation of RLCs
o Angle crashes reduced at two of the three intersections
o Reduction in PDO target crashes
o Approach with RLCs no target crashes after the installation of RLCs
• Safety Performance of Intersections with No RLCs
o Average number of target crashes at the non-treatment intersections (i.e.,
signalized intersections with no RLCs) that are in the vicinity of treatment
intersections reduced from 2011-2013 with fewer angle/left-turn and sideswipe
crashes
o Average number of target crashes at five signalized intersections that are far
away from the intersections with RLCs was higher during 2011-2013 compared
to 2008-2009 with an increase in angle/left-turn and sideswipe crashes
The below are the findings from the full Bayes before-and-after analysis:
• Crashes at the treatment intersections on an increasing trend similar to city, state, and
national level crashes on an increasing trend
• Significant sudden drop in all types of target crashes immediately after the installation of
RLCs
• Compared to the before-period, the after-period experienced fewer angle/left-turn/right-
turn crashes, fewer sideswipe crashes, and more rear-end crashes
• Sideswipe and angle/left-turn/right-turn crashes dropped immediately after the
installation of RLCs and then continued to increase, but are still lower than the before-
period
• Rear-end crashes dropped immediately after the installation of the RLCs and then
continue to increase
A discussion of the evaluation is on the December 14, 2018 Neighborhoods/Community Affairs
Committee agenda.
Please contact me should you have any questions or concerns.
Attach~J}I
KGB/~'--
Final Report
Evaluation of Photo Red Light Enforcement Program
in the City of Miami Beach
Prepared for:
Prepared by:
Priyanka Alluri, Ph.D., P.E., Assistant Professor
Angela Kitali, Graduate Research Assistant
Fabio Soto, Graduate Research Assistant
Florida International University
Dept. of Civil & Environmental Engg.
10555 West Flagler Street, EC 3628
Miami, FL 33174
December 2018
ii
DISCLAIMER
The opinions, findings, and conclusions expressed in this publication are those of the authors and
not necessarily those of the City of Miami Beach.
iii
ACKNOWLEDGMENTS
This research was funded by the City of Miami Beach. The authors are grateful to Dr. Leslie
Rosenfeld, Chief Learning and Development Officer, Organization Development Performance
Initiatives, City of Miami Beach for her guidance and support throughout the project. A special
thanks is due to the City of Miami Beach Police Department and the City of Miami Beach
Transportation Department for providing the required data. The authors are also thankful to Mr.
Hector Vargas and Ms. Liana Roque, undergraduate research assistants at Florida International
University for assisting with the data collection.
iv
TABLE OF CONTENTS
DISCLAIMER .............................................................................................................................................. ii
ACKNOWLEDGMENTS ........................................................................................................................... iii
LIST OF FIGURES ...................................................................................................................................... v
LIST OF TABLES ....................................................................................................................................... vi
LIST OF ACRONYMS/ABBREVIATIONS ............................................................................................. vii
1. INTRODUCTION .................................................................................................................................... 1
2. RESEARCH OBJECTIVE ....................................................................................................................... 2
3. LITERATURE REVIEW ......................................................................................................................... 2
4. STUDY DATA ......................................................................................................................................... 4
4.1 Treatment and Non-treatment Intersections ............................................................................................ 4
4.2 Crash Data ............................................................................................................................................... 6
4.3 Traffic Volume ...................................................................................................................................... 10
4.4 Intersection Characteristics ................................................................................................................... 10
5. METHODOLOGY ................................................................................................................................. 11
5.1 Descriptive Statistics ............................................................................................................................. 11
5.1.1 Washington Ave and 17th Street .................................................................................................... 11
5.1.2 41st St. and Prairie Ave ................................................................................................................. 13
5.1.3 Alton Rd and Chase Ave ............................................................................................................... 15
5.1.4 Indian Creek Dr and W 63rd Street ................................................................................................ 17
5.1.5 Indian Creek Dr and 71st Street ..................................................................................................... 19
5.1.6 Washington Ave and Dade Blvd ................................................................................................... 21
5.1.7 Pine Tree Blvd and 23rd Street ...................................................................................................... 23
5.1.8 Indian Creek Dr and Abbott Ave .................................................................................................. 25
5.2 Statistical Models .................................................................................................................................. 27
5.2.1 Data Variables Considered ............................................................................................................ 27
5.2.2 Poisson-gamma Model .................................................................................................................. 29
5.2.3 Model Results and Discussion ...................................................................................................... 30
6. REFLECTION ........................................................................................................................................ 34
6.1 Target Crash Type and Target Crash Severity ...................................................................................... 34
6.2 General Crash Trend ............................................................................................................................. 34
6.3 Safety Performance of Non-treatment Intersections ............................................................................. 36
7. SUMMARY ............................................................................................................................................ 40
7.1 Simple Before-and-after Crash Data Analysis ...................................................................................... 40
7.1.1 Four-legged Intersections .............................................................................................................. 40
7.1.2 Three-legged Intersections ............................................................................................................ 40
7.1.3 Safety Performance of Intersections with No RLCs ..................................................................... 41
7.2 FB Before-and-after Analysis ............................................................................................................... 41
REFERENCES ........................................................................................................................................... 42
v
LIST OF FIGURES
Figure 1: Map of Treatment and Non-Treatment Intersections ................................................................... 5
Figure 2: Aerial View of Washington Ave and 17th St. Intersection ......................................................... 12
Figure 3: Overview of Target Crashes at Washington Ave and 17th St Intersection ................................. 12
Figure 4: Washington Ave and 17th St Intersection - Overview of Target Crashes at Approaches
with RLCs ..................................................................................................................................... 12
Figure 5: Aerial View of W 41st St and Prairie Ave Intersection .............................................................. 14
Figure 6: Overview of Target Crashes at W 41st St and Prairie Ave Intersection ..................................... 14
Figure 7: W 41st St and Prairie Ave Intersection - Overview of Target Crashes at Approach with
RLC ............................................................................................................................................... 14
Figure 8: Aerial View of Alton Rd and Chase Ave Intersection ............................................................... 16
Figure 9: Overview of Target Crashes at Alton Rd and Chase Ave Intersection ...................................... 16
Figure 10: Alton Rd and Chase Ave Intersection - Overview of Target Crashes at Approach with
RLC ............................................................................................................................................... 16
Figure 11: Aerial View of Indian Creek Dr and W 63rd St. Intersection ................................................... 18
Figure 12: Overview of Target Crashes at Indian Creek Dr and W 63rd St. Intersection .......................... 18
Figure 13: Indian Creek Dr and W 63rd St. Intersection - Overview of Target Crashes at Approach
with RLC ....................................................................................................................................... 18
Figure 14: Aerial View of Indian Creek Dr and 71st St. Intersection ......................................................... 20
Figure 15: Overview of Target Crashes at Indian Creek Dr and 71st St. Intersection ................................ 20
Figure 16: Indian Creek Dr and 71st St. Intersection - Overview of Target Crashes at Approach
with RLC ....................................................................................................................................... 20
Figure 17: Aerial View of Washington Ave and Dade Blvd Intersection ................................................. 22
Figure 18: Overview of Target Crashes at Washington Ave and Dade Blvd. Intersection ....................... 22
Figure 19: Washington Ave and Dade Blvd. Intersection - Overview of Target Crashes at
Approach with RLC ...................................................................................................................... 22
Figure 20: Aerial View of Pine Tree Blvd and 23rd St. Intersection .......................................................... 24
Figure 21: Overview of Target Crashes at Pine Tree Blvd and 23rd St. Intersection ................................. 24
Figure 22: Pine Tree Blvd and 23rd St. Intersection - Overview of Target Crashes at Approach
with RLC ....................................................................................................................................... 24
Figure 23: Aerial View of Indian Creek Dr and Abbott Ave Intersection ................................................. 26
Figure 24: Overview of Target Crashes at Indian Creek Dr and Abbott Ave Intersection ........................ 26
Figure 25: Indian Creek Dr and Abbott Ave Intersection - Overview of Target Crashes at
Approach with RLC ...................................................................................................................... 26
Figure 26: Percent of Fatal and Severe Injury Crashes by Target Crash Type .......................................... 34
Figure 27: Annual Crash Trend ................................................................................................................. 35
Figure 28: Target Crashes by Crash Type at Comparison Intersections .................................................... 37
Figure 29: Target Crashes at Intersections Far Away from the Treatment Sites ....................................... 39
vi
LIST OF TABLES
Table 1: Summary of Recent Literature on the Safety Impacts of RLCs ..................................................... 3
Table 2: Treatment and Non-treatment Intersections.................................................................................... 6
Table 3: Descriptive Statistics of Crashes at Treatment and Non-treatment Intersections ........................... 9
Table 4: Descriptive Statistics of the Data Used in the full Bayes Models ................................................ 29
Table 5: Model Results for Different Target Crash Types ......................................................................... 32
Table 6: Model Results for Different Target Crash Severities ................................................................... 33
vii
LIST OF ACRONYMS/ABBREVIATIONS
AADT Annual Average Daily Traffic
BCI Bayesian Credible Interval
DHSMV Department of Highway Safety and Motor Vehicles (Florida)
EB East Bound
FB Full Bayes
FDOT Florida Department of Transportation
NB North Bound
PDO Property Damage Only
PRL Photo Red Light
RLC Red Light Camera
RLR Red-light Running
RTM Regression-to-the-mean
SB South Bound
WB West Bound
1
EVALUATION OF PHOTO RED LIGHT ENFORCEMENT PROGRAM
This report summarizes the key findings of the safety evaluation of the Photo Red Light (PRL)
Enforcement Program in the City of Miami Beach, Florida. The report is structured as follows.
Section 1 presents a brief introduction on the usage of red light cameras (RLCs) to improve
safety at signalized intersections.
Section 2 presents the study objective.
Section 3 shows the results of the most recent studies on the safety effectiveness of RLCs.
Section 4 discusses the data used in this study and the data collection efforts.
Section 5 presents the methodology adopted to evaluate the safety performance of the PRL
enforcement program in the City of Miami Beach.
Section 6 reflects on the main findings from the study.
Section 7 summarizes this research effort.
1. INTRODUCTION
Intersection-related crashes represent approximately 40% of all crashes (Decina et al., 2007). As
such, intersection safety is a serious problem in the United States. Many of the crashes at signalized
intersections can be attributed to red-light running (RLR), which “involves a driver entering an
intersection after the traffic signal has turned red” (City of Fort Lauderdale, n.d.). The following
crash types are commonly attributed to RLR: angle, left-turn, right-turn, and head-on crashes.
These crashes are often severe and result in fatalities. For example, in 2015, on average, two people
are killed every day due to RLR. From 2011 to 2015, 719 people died each year on an average
from RLR crashes. In 2014, about 126,000 people were injured in RLR crashes. The estimated
economic losses exceed $4 Billion annually (ATSOL, 2018). Interestingly, in RLR crashes, it was
not the drivers who run red lights who sustain fatal injuries, but the occupants of other vehicles,
pedestrians, and bicyclists who were hit by drivers who run red lights (IIHS-HLDI, 2016). In 2010,
61 people were killed in RLR crashes in Florida, making it the third deadliest state in the nation
for RLR crashes (City of Miami Springs, n.d.).
Over the last decade, photo red light (PRL) cameras have been increasingly deployed to reduce
the occurrence of RLR crashes. This automated enforcement method is used to discourage red
light runners and decrease intersection crashes. Red light cameras (RLCs) are automated systems
that photograph vehicles entering intersections after the traffic signals have turned red. The
photographic evidence captured by the cameras allow camera operators to determine whether or
not a ticket should be issued to the violating vehicle. In Florida, RLR tickets usually cost $158,
but drivers could pay up to $262 if they fail to pay for the offence after the first notification (Florida
Online Traffic School, 2018).
2
2. RESEARCH OBJECTIVE
The main objective of this study was to evaluate the safety effectiveness of the PRL Enforcement
Program in the City of Miami Beach, Florida. The safety effectiveness of the RLCs was measured
using the simple before-and-after analysis and the full Bayes before-and-after evaluation approach.
The full Bayes method was implemented to take into account the regression-to-the-mean (RTM)
phenomenon, which is observed due to the natural variability of crash data, to also consider the
changes in traffic volume, geometric characteristics and driver behavior over time.
3. LITERATURE REVIEW
Table 1 summarizes recent studies that focused on the safety effectiveness of RLCs. The literature
review revealed that most studies have compared data for the “before installation” and “after
installation” periods for treatment and non-treatment intersections to quantify the safety
effectiveness of RLCs. While most studies determined that signalized intersections equipped with
RLCs experienced a considerably large reduction in RLR crash types and injuries, some studies
determined that RLCs contribute only to a small reduction in crashes. Additionally, these studies
found that RLCs have some negative safety impacts, i.e., they tend to increase specific crash types,
such as rear-end and sideswipe collisions, by both number and severity. Understanding the
methodology and findings of previous studies allowed the research team to adopt a methodology
to overcome the past flaws and difficulties, such as, the RTM effect, and identify the right type of
crashes to be evaluated.
3
Table 1: Summary of Recent Literature on the Safety Impacts of RLCs
Reference Number of Study
Intersections Study Results Method Study Period City, State
Ko et al. (2017)
Treatment Sites: 48
Comparison Sites:
Not Available
After the RLC activation: 37% crash reduction in
all RLR crash types.
After the RLC deactivation: 20% increase in all
RLR crash types.
Before-after
(EB approach)
2008 - 2014 Houston, TX
Llau et al. (2015) Treatment Sites: 20
Comparison Sites: 40
19% and 24% reduction in total injury, and RLR-
related injury crashes.
40% increase in rear-end crashes.
Before-after using
comparison group
(EB approach)
2008 – 2012 Miami-Dade
County, FL
Claros et al.
(2017)
Treatment Sites: 24
Comparison Sites: 35
12% reduction in angle crashes.
10.5% increase in rear-end crashes.
Crash cost benefit of $47,000 per site per year.
Before-after
(EB approach) 2006 – 2011 Missouri
Ahmed and
Abdel-Aty (2014)
Treatment Sites: 25
Comparison Sites:
Not Available
Angle and left-turn crashes decreased by 24% and
26% for all severity and fatal and injury crashes,
respectively, at approaches with RLCs.
Rear-end crashes increased by 32% and 41% for all
severity level and fatal crashes.
Before-after
(EB approach) 2006 – 2011 Orange
County, FL
Shin and
Washington
(2007)
Treatment Sites: 24
Comparison Sites: 13
Angle crashes decreased by 20% and left-turn
crashes decreased by 45% at target approaches.
Rear-end crashes increased by 41%.
Similar results were found for non-target
approaches, indicating a high spillover effect.
Before-after
(Simple, with
traffic flow
correction, using
comparison group,
& EB approach)
1998 – 2003;
1990 – 2003
Phoenix and
Scottsdale, AZ
Pulugurtha and
Otturu (2013)
Treatment Sites: 32
Comparison Sites:
Not Available
Significant increase in sideswipe and rear-end
crashes.
Significant reduction in total crashes.
Before-after
(EB approach) 1997 – 2010 Charlotte, NC
Note: treatment sites are the signalized intersections with RLCs; while comparison (i.e., non-treatment) sites are the signalized intersections with no RLCs.
4
4. STUDY DATA
The study area included a total of 28 signalized intersections in the City of Miami Beach, Florida.
The intersections were divided into two categories:
signalized intersections with RLCs, termed as “treatment intersections”; and
signalized intersections without RLCs, termed as “non-treatment intersections”.
As the name implies, treatment intersections are locations where RLCs were installed. On the other
hand, non-treatment intersections, also known as comparison intersections, are those intersections
that have similar traffic volume, roadway geometrics, and other site characteristics as the treatment
intersections, but without the RLCs. These sites were manually selected after an extensive review
of the City of Miami Beach’s roadway network.
Figures 1(a) and 1(b) show the map of the treatment and non-treatment intersections, respectively.
A total of nine signalized intersections were identified as “treatment intersections”. Of these nine,
six are four-legged and the remaining three are three-legged intersections. A total of 19 signalized
intersections were identified as “non-treatment intersections”, 14 of which are four-legged, and
the remaining five are three-legged signalized intersections.
The following data were included in the analysis: crash data, traffic volume, roadway geometric
characteristics, and traffic control features. Note that these data were required for both treatment
and non-treatment intersections. The following subsections discuss these data in detail.
4.1 Treatment and Non-treatment Intersections
A total of ten red-light cameras are operational at nine signalized intersections in the City of Miami
Beach, as listed in Table 2 and shown in Figure 1(a). For all the intersections, Table 2 includes the
number of intersection legs (i.e., three-legged or four-legged), and the major road and minor road
names. For the treatment intersections, the table provides information on the RLC installation date
and the approach with the RLC.
Due to the long period of construction activity, the intersection where Alton Road meets 17th
Street was not included in the study. Temporary traffic controls, such as concrete barriers and
traffic cones, tend to affect traffic patterns, traffic volumes, and the occurrence of crashes.
Therefore, this intersection is expected to have an unusual crash frequency that cannot be
associated with the operation of RLCs. The rest of the intersections (i.e., both treatment and non-
treatment intersections) were manually reviewed to identify any major construction activity that
might have taken place during the study period. However, no significant construction activity was
found at the rest of the intersections. The review was also done to make sure that there were no
other countermeasures during the study period other than the RLCs.
5
(a) Treatment Intersections
(b) Non-treatment Intersections
Figure 1: Map of Treatment and Non-Treatment Intersections
6
Table 2: Treatment and Non-treatment Intersections
Intersection Type Item Major Road Minor Road Date of Operation Approach with RLC
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1 Washington Ave 17th St. 4/1/2010 SB
2 Washington Ave 17th St. 4/1/2010 EB
3* Alton Road 17th St. 9/1/2015 WB
4 41st St. Prairie Ave 4/1/2010 NB
5 Alton Road Chase Ave 4/1/2010 NB
6 Indian Creek Dr W 63rd St. 4/1/2010 SB
7 71st St. Indian Creek Dr 4/1/2010 NB
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8 Washington Ave Dade Blvd. 4/1/2010 EB
9 Pinetree Blvd 23rd St. 4/1/2010 WB
10 Indian Creek Dr Abbott Ave 4/1/2010 SB
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11 Dade Blvd Prairie Ave
Not Applicable Not Applicable
12 5th St. Alton Road
13 Arthur Godfrey Rd. Meridian Ave
14 Alton Road 16th St.
15 Alton Road 11th St.
16 Alton Road 8th St.
17 17th St. James Ave
18 Alton Road W 47th St.
19 Washington Ave 16th St.
20 41st St. Indian Creek Dr
21 5th St. Collins Ave
22 Collins Ave 16th St.
23 Collins Ave 23rd St.
24 63rd St. Pine Tree Dr
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25 West Ave 17th St.
26 Pine Tree Dr Sheridan Ave
27 Washington 15th St.
28 Pine Tree Dr W 47th St.
29 West Avenue 11th St.
* This location experienced major construction during the study period. Hence, it is not included in the analysis.
4.2 Crash Data
The analysis was based on five years of crash data. Since the RLCs were installed in April 2010,
crash data from two years before the RLC installation (i.e., 2008-2009), and three years after the
RLC installation (i.e., 2011-2013) were included in the analysis.
Signal Four Analytics, a statewide interactive web-based geospatial crash analytical tool
developed by and hosted at the University of Florida, GeoPlan Center, was used to identify and
extract crash data. All crashes that occurred within 300 ft from the center of the intersection were
identified, and the police crash reports of all these crashes were downloaded and reviewed.
As is evident from the literature, RLCs impact only specific (and not all) crash types, commonly
known as target crashes. The following crash types were considered to be associated with RLR
and the operation of RLCs.
7
Angle (side impact)
Right-turn (vehicle turning)
Left-turn (vehicle turning)
Rear-end
Sideswipe
The police reports of all crashes that occurred within 300 ft of the intersections were manually
reviewed to identify intersection-related crashes and target crashes. The number of crashes
extracted from Signal Four Analytics was 2,419. Of this total, only 1,080 target crashes were
included in the analysis. A total of 389 target crashes were found to have occurred at treatment
sites, and 691 target crashes were found to have occurred at non-treatment sites, during both the
before and the after periods. Table 3 provides a summary of all observed crash frequencies for
treatment and non-treatment intersections. In addition to crash type, crash severity was also
considered in the analysis. Crashes were categorized into property damage only (PDO), and
fatal/injury crashes. Note that all injury severity levels (i.e., incapacitating, non-incapacitating, and
possible injury) were grouped in the fatal/injury crash category.
The following information was collected while reviewing the police reports:
The approach the crash occurred
o Major approach
o Minor approach
o Middle of the intersection
o Not sure
The crash occurred on the approach with a red light camera? (applicable only for
treatment intersections)
o Yes
o No
o Not sure
Manner of collision
o Rear-end
o Angle/left-turn/right-turn
o Head-on/front-to-front
o Sideswipe
o Backed into
o Fixed object
o Other
o Not sure
1st vehicle maneuver action
o Going straight
o Making a left-turn
o Making a U-turn
o Making a right-turn
8
o Not sure
o Other
2nd vehicle maneuver action
o Going straight
o Making a left-turn
o Making a U-turn
o Making a right-turn
o Not sure
o Other
Crash involving driveway/on-street parking
o Yes
o No
o Not sure
Crash involving a pedestrian/bicyclists
o Yes
o No
o Not sure
Crash involving a distracted driver
o Yes
o No
o Not sure
To evaluate the direct impact of RLCs on crash frequency and severity, target crashes were
excluded when one of the following conditions were present:
Adverse weather condition
Distraction
Driving under the influence (DUI)
Crashes related to emergency vehicles responding in emergency circumstances
Sickness
Sleep deprivation/fatigue
Table 3 provides the descriptive statistics of crashes at each of the treatment and non-treatment
intersections.
9
Table 3: Descriptive Statistics of Crashes at Treatment and Non-treatment Intersections
Int.
Type Item Major
Road
Minor
Road
Before-period After-period
Target Crashes by Crash
Type
Target Crashes
by Severity Total
Target
Crashes
Total
Crashes
Target Crashes by Crash
Type
Target Crashes
by Severity Total
Target
Crashes
Total
Crashes Angle/
Left-turn/
Right-turn
Rear-
end
Side-
swipe PDO Fatal &
Injury
Angle/
Left-turn/
Right-turn
Rear-
end
Side-
swipe PDO Fatal &
Injury
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1 Washington Ave 17th St. 9 1.5 4.5 12 3 15 27 3.3 1.7 2.3 6.0 1.3 7.3 18.0
2 Washington Ave 17th St. 9 1.5 4.5 12 3 15 27 3.3 1.7 2.3 6.0 1.3 7.3 18.0
3* Alton Road 17th St. 3.5 9 8 16 4.5 20.5 49 7.7 4.7 8.7 19.0 2.0 21.1 52.7
4 41st St. Prairie Ave 0.5 3.5 0.5 4 0.5 4.5 21 1.3 4.7 0.0 5.0 1.0 6 13.0
5 Alton Road Chase Ave 1 3 1.5 5 0.5 5.5 16 0.0 4.7 1.0 4.3 1.3 5.7 14.7
6 Indian Creek Dr W 63rd St. 1.5 5 3.5 9 1 10 19.5 1.0 3.7 2.7 6.3 1.0 7.4 25.0
7 71st St. Indian Creek Dr 3.5 11.5 4 16.5 2.5 19 23.5 2.7 8.3 3.3 11.0 3.3 14.3 18.7
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8 Washington Ave Dade Blvd. 1.5 2 1.5 4.5 0.5 5 10 2.7 1.0 0.7 3.3 1.0 4.4 10.3
9 Pine Tree Blvd 23rd St. 1.5 1 1.5 4 0 4 12 0.7 0.0 1.3 1.7 0.3 2 7.3
10 Indian Creek Dr Abbott Ave 1 2.5 1.5 4 1 5 13 0.3 1.0 1.3 2.3 0.3 2.6 11.3
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11 Dade Blvd. Prairie Ave 1.5 1.5 1 3.5 0.5 4 6 2.0 1.0 0.3 2.7 0.7 3.3 5.7
12 5th St. Alton Road 2 35.5 8 39.5 6 45.5 55.5 2.0 26.0 9.7 31.7 6.0 37.7 51.3
13 Arthur Godfrey Rd. Meridian Ave 1.5 7 1 8.5 1 9.5 11.5 0.7 5.0 0.0 4.3 1.3 5.7 9.0
14 Alton Road 16th St. 4.5 4 6.5 14 1 15 28.5 3.3 3.3 3.0 8.3 1.3 9.6 25.0
15 Alton Road 11th St. 2 3 2 6.5 0.5 7 9 3.0 3.0 1.7 5.3 2.3 7.7 15.0
16 Alton Road 8th St. 4 6.5 8.5 16 3 19 25.5 5.3 5.3 3.0 12.0 1.7 13.6 20.0
17 17th St. James Ave 2.5 1 0 2 1.5 3.5 5.5 1.0 0.7 2.0 3.7 0.0 3.7 6.7
18 Alton Road W 47th St. 0.5 1.5 0.5 2.5 0 2.5 4.5 0.7 1.0 0.3 2.0 0.0 2 4.0
19 Washington Ave 16th St. 1.5 8 7 14 2.5 16.5 32.5 1.3 3.3 3.3 6.3 1.7 7.9 21.0
20 41st St. Indian Creek Dr 0.5 4.5 2 6 1 7 24 0.3 4.0 3.7 7.7 0.3 8 25.7
21 5th St. Collins Ave 3.5 2 1.5 6.5 0.5 7 26.5 0.3 0.7 2.3 3.0 0.3 3.3 16.3
22 Collins Ave 16th St. 0 2 2 3 1 4 19.5 0.7 0.7 2.0 2.7 0.7 3.4 19.0
23 Collins Ave 23rd St. 1.5 1 1 3 0.5 3.5 20 2.0 1.7 2.0 4.3 1.3 5.7 25.3
24 63rd St. Pine Tree Dr 0.5 1 0.5 2 0 2 10.5 0.3 0.0 0.7 1.0 0.0 1 9.0
3 -le
g
g
e
d
25 West Ave 17th St. 1.5 2.5 1.5 4.5 1 5.5 8.5 0.3 1.7 0.3 2.0 0.3 2.3 4.7
26 Pine Tree Dr Sheridan Ave 0 1.5 0 1.5 0 1.5 4 0.7 0.7 0.3 1.3 0.3 1.7 4.5
27 Washington Ave 15th St. 1 1.5 0 2 0.5 2.5 20.5 0.3 1.0 0.3 1.3 0.3 1.6 19.7
28 Pine Tree Dr W 47th St. 0.5 1 0 1 0.5 1.5 7 0.3 1.3 0.0 1.3 0.3 1.6 6.7
29 West Avenue 11th St. 0 3.5 0.5 3.5 0.5 4 5.5 1.0 1.3 0.7 1.0 2.0 3 4.7
* This location experienced major construction during the study period, and is not included in the analysis.
Note: all the crashes are per year. Total target crashes include angle/left-turn/right-turn, sideswipe, and rear-end crashes. There is an approximation error of ±0.1 in total target crashes.
10
4.3 Traffic Volume
Traffic volume data is included in traffic safety models because it is proven to be the main
contributor to what is called crash exposure, i.e., as traffic volume increases there is a higher
likelihood for crashes to occur.
AADT were collected from the Florida Department of Transportation (FDOT) Traffic Online
website, a web-based mapping application that provides traffic count site locations and historical
traffic count data. It is managed by the FDOT Transportation Data and Analytics Office. AADT
data were collected for every year of analysis, 2008, 2009, 2011, 2012, and 2013. However, it is
important to note that traffic counts are not available for all years and all roads due to high data
collection costs. As such, the research team made reasonable assumptions to estimate missing
traffic counts. For the missing data, AADT was obtained from parallel roads with similar roadway
geometric characteristics, and AADT for the missing years was extrapolated assuming that traffic
volume increased by 3% each year.
4.4 Intersection Characteristics
Roadway geometric characteristics and traffic control features often influence the occurrence and
severity of crashes at signalized intersections. Including these data in the analysis helps understand
the relationship, if any, between the RLR behavior and the characteristics of the intersections. The
following roadway geometric characteristics and traffic control features were collected for each of
the study intersection:
Roadway Geometric Characteristics
o Number of through lanes
o Approaches with left-turn lanes
o Approaches with right-turn lanes
o Length of the sidewalk
Traffic Control Features
o Yellow and all-red times
o Presence of pedestrian phase
o Type of left-turn signal phasing (i.e., protected, protected-permitted, or permitted)
o Right-turn on red restriction
11
5. METHODOLOGY
5.1 Descriptive Statistics
The following 9 signalized intersections were installed with RLCs in the City of Miami Beach:
1. Washington Ave and 17th St.
2. Alton Road and 17th St.
3. 41st St. and Prairie Ave
4. Alton Road and Chase Ave
5. Indian Creek Dr and W 63rd St.
6. Indian Creek Dr and 71st St.
7. Washington Ave and Dade Blvd.
8. Pinetree Blvd and 23rd St.
9. Indian Creek Dr and Abbott Ave
Note that the intersection Alton Road and 17th St. experienced major construction during the study
period, and hence excluded from the analysis. The following subsection discuss the safety
performance of each of the remaining eight intersections.
5.1.1 Washington Ave and 17th Street
This intersection is a four-legged signalized intersection. Figure 2 shows the aerial view of this
intersection. The RLCs on the SB and EB approaches were installed on April 1, 2010. Figure 3
shows the number of target crashes by crash type and crash severity at this intersection before and
after the RLC installation. Figure 4 shows the number of target crashes and total crashes at the
approaches with RLCs. Note that target crashes include angle/left-turn/right-turn, rear-end, and
sideswipe crashes. Some of the key findings include:
This intersection experienced a total of 108 crashes during the study period (i.e., 2008-
2009 and 2011-2013). The before-period had 27 crashes per year, and the after-period had
18 crashes per year.
Overall, there was a reduction in target crashes after the installation of the RLCs at this
intersection.
After the RLC installation, both the PDO and fatal/injury crashes decreased by half.
Both total crashes and total target crashes decreased at the approaches with RLCs.
Angle/left-turn/right-turn crashes decreased, rear-end crashes increased, while sideswipe
crashes didn’t change after the installation of RLCs.
12
Figure 2: Aerial View of Washington Ave and 17th St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 3: Overview of Target Crashes at Washington Ave and 17th St Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 4: Washington Ave and 17th St Intersection - Overview of Target Crashes at
Approaches with RLCs
9
1.5
4.5
15
3.3
1.7 2.3
7.3
0
2
4
6
8
10
12
14
16
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
12
3
6.0
1.3
0
2
4
6
8
10
12
14
PDO Fatal/Injury
Before After
1 0.5
2
3.5
8
0.3 0.7
2
3
5.7
0
1
2
3
4
5
6
7
8
Angle Rear-end Sideswipe Total
Target
Crashes
Total
CrashesBeforeAfter
3.5
0
2.7
0.3
0
1
2
3
4
5
6
7
8
PDO Fatal/Injury
Before After
17th St
13
5.1.2 41st St. and Prairie Ave
This intersection is a four-legged signalized intersection. Figure 5 shows the aerial view of this
intersection. The RLC on the NB approach was installed on April 1, 2010. Figure 6 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 7 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 81 crashes during the study period (i.e., 2008-2009
and 2011-2013). The before-period had 21 crashes per year, and the after-period had 13
crashes per year.
Overall, the number of target crashes at this intersection increased after installing the RLC.
Angle/left-turn/right-turn and rear-end crashes increased after the RLC installation. No
sideswipe crashes were reported after the RLC installation.
Even though there was an increase in target crashes, the total crashes at the intersection
decreased.
At the approach with the RLC, the before-period did not have any target crashes, however,
the approach experienced three target crashes after the RLC installation.
14
Figure 5: Aerial View of W 41st St and Prairie Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 6: Overview of Target Crashes at W 41st St and Prairie Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 7: W 41st St and Prairie Ave Intersection - Overview of Target Crashes at Approach
with RLC
0.5
3.5
0.5
4.5
1.3
4.7
0
6
0
1
2
3
4
5
6
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4
0.5
5
1
0
1
2
3
4
5
PDO Fatal/Injury
Before After
0 0 0 0
1
0.3
1
0
1.3
2
0
0.5
1
1.5
2
Angle Rear-end Sideswipe Total Target
Crashes
Total
Crashes
Before After
0 0
1
0.3
0
0.5
1
1.5
2
PDO Fatal/Injury
Before After
W 41st St
15
5.1.3 Alton Rd and Chase Ave
This intersection is a four-legged signalized intersection. Figure 8 shows the aerial view of this
intersection. The RLC on the NB approach was installed on April 1, 2010. Figure 9 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 10 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 76 crashes during the study period (i.e., 2008-2009
and 2011-2013). The before-period had 16 crashes per year, and the after-period had 14.7
crashes per year.
The results show that while angle/left-turn/right-turn and sideswipe crashes decreased,
rear-end crashes increased after the RLC installation.
Overall, this intersection experienced similar crash trend after the RLC installation.
The approach with RLC did not experience any angle/left-turn/right-turn or sideswipe
crashes during the study period. However, multiple rear-end crashes were reported. Rear-
end crashes doubled after the RLC installation.
16
Figure 8: Aerial View of Alton Rd and Chase Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 9: Overview of Target Crashes at Alton Rd and Chase Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 10: Alton Rd and Chase Ave Intersection - Overview of Target Crashes at
Approach with RLC
1
3
1.5
5.5
0
4.7
1
5.7
0
1
2
3
4
5
6
Angle Rear-end Sideswipe Total Target
Crashes
Before After
5
0.5
4.3
1.3
0
1
2
3
4
5
6
PDO Fatal/Injury
Before After
0
1.50
0
1.50
4
0
3
0
3
5.67
0
1
2
3
4
5
6
Angle Rear-end Sideswipe Total
Target
Crashes
Total
CrashesBeforeAfter
1
0.50
2
1
0
1
2
3
4
5
6
PDO Fatal/Injury
Before After
17
5.1.4 Indian Creek Dr and W 63rd Street
This intersection is a four-legged signalized intersection. Figure 11 shows the aerial view of this
intersection. The RLC on the SB approach was installed on April 1, 2010. Figure 12 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 13 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 114 crashes during the study period (i.e., 2008-
2009 and 2011-2013). The before-period had 19.5 crashes per year, and the after-period
had 25 crashes per year.
This intersection experienced a reduction in all target crashes (i.e., angle/left-turn/right-
turn, rear-end, and sideswipe crashes) after the RLC installation.
There was a reduction in PDO target crashes; while the target crashes resulting in injuries
experienced similar crash trend after the RLC installation.
The approach with RLC experienced a positive effect; angle/left-turn/right-turn, rear-end,
and sideswipe crashes decreased after the RLC installation.
The approach with RLC experienced a reduction in PDO crashes. No fatal/injury crashes
were reported on this approach during the study period.
18
Figure 11: Aerial View of Indian Creek Dr and W 63rd St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 12: Overview of Target Crashes at Indian Creek Dr and W 63rd St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 13: Indian Creek Dr and W 63rd St. Intersection - Overview of Target Crashes at
Approach with RLC
1.5
5
3.5
10
1
3.7
2.7
7.3
0
2
4
6
8
10
Angle Rear-end Sideswipe Total Target
Crashes
Before After
9
1
6
1
0
2
4
6
8
10
PDO Fatal/Injury
Before After
1
2
1
4
6.5
0.3 0
0.7 1
6.7
0
1
2
3
4
5
6
7
Angle Rear-end Sideswipe Total
Target
Crashes
Total
Crashes
Before After
4
0
1
0
0
1
2
3
4
5
6
7
PDO Fatal/Injury
Before After
In
d
i
a
n
C
r
e
e
k
D
r
19
5.1.5 Indian Creek Dr and 71st Street
This intersection is a four-legged signalized intersection. Figure 14 shows the aerial view of this
intersection. The RLC on the NB approach was installed on April 1, 2010. Figure 15 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 16 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 103 crashes during the study period (i.e., 2008-
2009 and 2011-2013). The before-period had 23.5 crashes per year, and the after-period
had 18.7 crashes per year.
This intersection experienced a reduction in all target crashes (i.e., angle/left-turn/right-
turn, rear-end, and sideswipe crashes) after the RLC installation.
There was a reduction in PDO target crashes, but target crashes resulting in injuries slightly
increased.
The approach with RLC experienced a positive effect; angle/left-turn/right-turn, rear-end,
and sideswipe crashes decreased after the RLC installation. The total number of crashes
also decreased at this approach.
While PDO target crashes decreased by more than half, fatal/injury target crashes
increased. It is important to note that no fatal/injury target crashes were reported in the
before-period.
20
Figure 14: Aerial View of Indian Creek Dr and 71st St. Intersection
Figure 15: Overview of Target Crashes at Indian Creek Dr and 71st St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 16: Indian Creek Dr and 71st St. Intersection - Overview of Target Crashes at
Approach with RLC
0.5
1.5
0.5
2.5
3
0.0
0.7
0.3
1
1.3
0
0.5
1
1.5
2
2.5
3
Angle Rear-end Sideswipe Total
Target
Crashes
Total
CrashesBeforeAfter
3
0
1.3
0.3
0
0.5
1
1.5
2
2.5
3
PDO Fatal/Injury
Before After
71st Street
21
5.1.6 Washington Ave and Dade Blvd
This intersection is a three-legged signalized intersection. Figure 17 shows the aerial view of this
intersection. The RLC on the EB approach was installed on April 01, 2010. Figure 18 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 19 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 51 crashes during the study period (i.e., 2008-2009
and 2011-2013). The before-period had 10 crashes per year, and the after-period had 10.3
crashes per year.
Overall, target crashes slightly decreased at this intersection after the RLC installation.
After the RLC installation, this intersection experienced an increase in angle/left-
turn/right-turn crashes but a decrease in rear-end and sideswipe crashes.
PDO target crashes decreased while fatal/injury target crashes increased.
It is important to note that the approach with RLC did not experience any angle/left-
turn/right-turn or rear-end crashes. There were only two target crashes (sideswipe) reported
during the before-period.
At the approach with RLC, no target crashes were reported after the RLC installation. Total
crashes decreased at this approach.
The approach with RLC experienced a reduction in PDO crashes after the RLC installation.
It is important to note that there were no observed fatal/injury crashes during the study
period.
22
Figure 17: Aerial View of Washington Ave and Dade Blvd Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 18: Overview of Target Crashes at Washington Ave and Dade Blvd. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 19: Washington Ave and Dade Blvd. Intersection - Overview of Target Crashes at
Approach with RLC
1.5
2
1.5
5
2.7
1 0.7
4.3
0
1
2
3
4
5
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4.5
0.5
3.3
1
0.0
1.0
2.0
3.0
4.0
5.0
PDO Fatal/Injury
Before After
0 0
1 1
3
0 0 0 0
1
0
0.5
1
1.5
2
2.5
3
Angle Rear-end Sideswipe Total
Target
Crashes
Total
Crashes
Before After
1
00 0
0
0.5
1
1.5
2
2.5
3
PDO Fatal/Injury
Before After
23
5.1.7 Pine Tree Blvd and 23rd Street
This intersection is a three-legged signalized intersection. Figure 20 shows the aerial view of this
intersection. The RLC on the WB approach was installed on April 01, 2010. Figure 21 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 22 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 46 crashes during the study period (i.e., 2008-2009
and 2011-2013). The before-period had 12 crashes per year, and the after-period had 7.3
crashes per year.
Overall, target crashes decreased by half after the RLC installation.
This intersection experienced a reduction in angle/left-turn/right-turn, rear-end, and
sideswipe crashes after the RLC installation.
After the RLC installation, there was a reduction in PDO target crashes at the intersection,
but there was a slight increase in fatal/injury target crashes.
It is important to note that the approach with RLC did not experience any angle/left-
turn/right-turn or rear-end crashes during the study period. Similarly, there were no
observed fatal/injury crashes during the study period.
PDO target crashes decreased at the approach with RLC after its installation.
24
Figure 20: Aerial View of Pine Tree Blvd and 23rd St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 21: Overview of Target Crashes at Pine Tree Blvd and 23rd St. Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 22: Pine Tree Blvd and 23rd St. Intersection - Overview of Target Crashes at
Approach with RLC
1.5
1
1.5
4
0.7
0
1.3
2
0
1
2
3
4
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4
0
1.7
0.3
0
1
2
3
4
PDO Fatal/Injury
Before After
0.0 0.0
0.5 0.5
1.0
0.0 0.0 0.0 0.0
0.3
0.0
0.5
1.0
Angle Rear-end Sideswipe Total
Target
Crashes
Total
CrashesBeforeAfter
0.5
0.00.0 0.0
0.0
0.5
1.0
PDO Fatal/Injury
Before After
25
5.1.8 Indian Creek Dr and Abbott Ave
This intersection is a three-legged signalized intersection. Figure 23 shows the aerial view of this
intersection. The RLC on the SB approach was installed on April 1, 2010. Figure 24 shows the
number of target crashes by crash type and crash severity at this intersection before and after the
RLC installation. Figure 25 shows the number of target crashes and total crashes at the approach
with RLC. Note that target crashes include angle/left-turn/right-turn, rear-end, and sideswipe
crashes. Some of the key findings include:
This intersection experienced a total of 60 crashes during the study period (i.e., 2008-2009
and 2011-2013). The before-period had 13 crashes per year, and the after-period had 11.3
crashes per year.
This intersection experienced a reduction in all target crashes (i.e., angle/left-turn/right-
turn, rear-end, and sideswipe crashes) after the RLC installation.
Similarly, both PDO and fatal/injury target crashes decreased after the RLC installation.
The approach with RLC did not experience any angle/left-turn/right-turn, rear-end or
sideswipe crashes after the RLC installation. There were no observed target crashes on this
approach after the RLC installation.
Even though the approach with RLC didn’t experience any target crashes after the RLC
installation, the total number of crashes at the approach with RLC increased slightly.
26
Figure 23: Aerial View of Indian Creek Dr and Abbott Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 24: Overview of Target Crashes at Indian Creek Dr and Abbott Ave Intersection
(a) Target Crashes by Crash Type (b) Target Crashes by Crash Severity
Figure 25: Indian Creek Dr and Abbott Ave Intersection - Overview of Target Crashes at
Approach with RLC
1
2.5
1.5
5
0.3
1 1.3
2.7
0
1
2
3
4
5
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4
1
2.3
0.3
0
1
2
3
4
PDO Fatal/Injury
Before After
0.5 0.5
0
1
1.5
0 0 0 0
1.7
0
0.4
0.8
1.2
1.6
2
Angle Rear-end Sideswipe Total
Target
Crashes
Total
CrashesBeforeAfter
0.5 0.5
0 0
0
0.4
0.8
1.2
1.6
2
PDO Fatal/Injury
Before After
27
5.2 Statistical Models
Traditionally, naïve statistical methods have been used to quantify the safety performance of
roadway countermeasures. In these approaches, the long-term safety effectiveness of the roadway
countermeasure – in this case intersections with RLC – is quantified based on estimated crash rate.
The estimated crash rates in this case are obtained by averaging observed crash rates over a few
years on sites where the countermeasure have been installed. Despite being the most used
technique for evaluating safety effectiveness of roadway countermeasures, this approach however,
suffers from methodological and statistical limitations, including but not limited to:
Failure to estimate reliable estimates when there is scarcity of sample size. The estimated
long-term crash rate obtained by averaging observed crash rates over a few years can be
unduly influenced by a single year with an unusually high (or low) number of crashes.
Inability to account for the uncertainty of the crash data.
Failure to account for the regression-to-the-mean bias, which is often predominant when
the treatment sites are identified based on prior crash experience.
These limitations can be potentially addressed by employing a more reliable approach such as a
full Bayes (FB) method in lieu of a crash rate approach. The FB method uses the data from the
intersections of interest in combination with information from similar sites to complement the
limited data. A FB approach has the ability to account for most of the uncertainties in the dataset
and model parameters (Park et al., 2016). This method is also independent of sample size, yielding
robust results even when used with small sample size (Li et al., 2013). Additionally, the FB
approach divides the periods into time intervals (yearly in this case) and models each time interval
as a separate data point to account for time variations, unlike the crash rate methodology which
averages the data into a single data point. Detailed discussion about the FB method is presented in
Carriquiry & Pawlovich (2004).
This study evaluated the safety effectiveness of RLCs at signalized intersections. The study
employed the FB methodology to evaluate the overall safety effectiveness in terms of the
effectiveness for target crashes by crash type (angle/left-turn/right-turn, rear-end, and sideswipe),
and crash severity (PDO and fatal/injury crashes). Note that the models were developed only for
four-legged signalized intersections. Only three treatment intersections are three-legged; the
sample size is too small to yield reliable results from the FB statistical analysis.
5.2.1 Data Variables Considered
In this study, the FB before-and-after with comparison sites approach was used to assess the
effectiveness of RLCs at signalized intersections. The RLCs were installed in 2010. The before-
period included two years (2008 and 2009) before the installation of the RLCs; and the after-period
included three years after the installation of RLCs (2011-2013). As mentioned earlier, treatment
intersections are those where RLCs were installed. Comparison (i.e., non-treatment) intersections,
on the other hand, include intersections similar to treatment intersections but without RLCs.
Independent variables included in this study are listed below (see Table 4):
28
Treatment indicator. It represents the countermeasure effectiveness (i.e., RLCs in this
case). It measures the difference in crash count between treatment and comparison
intersections.
o comparison intersections (code 0)
o treatment intersections (code 1)
Time indicator. It accounts for the changes in crash frequency due to the intervention.
o before period (code 0)
o after period (code 1)
Treatment by time indicator. It accounts for different time trends across treatment and
comparison intersections.
Jump parameter. It accounts for a sudden drop or increase in crash frequency upon
installation of RLCs.
Average annual daily traffic (AADT) on the major street (Major AADT).
AADT on the minor street (Minor AADT).
Speed limit on the major approach (Major speed)
o ≤ 30 mph (code 0)
o > 30 mph (code 1)
Speed limit on the minor approach (Minor AADT)
o ≤ 30 mph (code 0)
o > 30 mph (code 1)
Yellow time
o < 4 seconds (code 0)
o ≥ 4 seconds (code 1)
All red time
o ≤ 2 seconds (code 0)
o > 2 seconds (code 1)
Length of the pedestrian crosswalk
Number of through lanes on the major approach (Major through lanes)
o ≤ 2 lanes (code 0)
o > 2 lanes (code 1)
Number of lanes on the minor approach (Minor through lanes)
o ≤ 1 lanes (code 0)
o > 1 lanes (code 1)
Number of driveways within 250 feet from the center of the intersection (Number of
driveways)
o ≤ 3 (code 0)
o > 3 (code 1)
29
Table 4: Descriptive Statistics of the Data Used in the full Bayes Models
Variable Units Mean Std. Dev. Minimum Median Maximum
Major AADT Vehicles per day 31,721 13,674.06 4,900 35,000 83,500
Minor AADT Vehicles per day 9,581 8,802.09 2,700 5,500 39,500
Major speed Miles per hour 32.13 2.48 30 30 35
Minor speed Miles per hour 30.38 2.35 20 30 35
Yellow time Seconds 3.94 0.20 3.06 4 4.3
All red time Seconds 2.36 0.55 2 2 4
Length of pedestrian crosswalk Feet 72.27 18.7 48 67.3 120
Major through lanes Count 2.15 0.53 1 2 4
Minor through lanes Count 1.225 0.47 0 1 2
Number of driveways Count 3.325 1.53 0 3 8
5.2.2 Poisson-gamma Model
The Poisson-gamma statistical model was considered to assess the safety effectiveness of RLCs.
In all cases, 𝑌𝑖𝑡 in Equation 1 denotes the crash count observed at intersection �ℎ(�ℎ=1,2,3,…,𝑙)
during year t (𝑟=1,2,3,…,5) and can be modeled with a Poisson distribution with mean and
variance equal to 𝜃𝑖𝑡.
𝑌𝑖𝑡|𝜃𝑖𝑡 ~ 𝑃𝑙�ℎ𝑟𝑟𝑙𝑙 (𝜃𝑖𝑡), (1)
The Poisson mean 𝜃𝑖𝑡 can be written as shown in Equation 2.
𝑙𝑙 (𝜃𝑖𝑡)=𝑙𝑙 (𝜇𝑖𝑡)+𝜀𝑖 (2)
Using the Poisson-gamma, the random effect εi is assumed to follow a Gamma distribution as
presented in Equation 3.
εi ~𝐺𝑎𝑙𝑙𝑎(𝜑,1
𝜑) 𝑤�𝑒𝑟𝑒 𝜑∼𝐺𝑎𝑙𝑙𝑎(1,1) (3)
The log-gamma model for crash density is described as a piecewise linear function (Equation 4)
of predictor variables, such that the function is continuous at the change point 𝑟0𝑖. 𝑟0𝑖 represent the
interventions year (2010) for the ith treatment intersections. The piecewise linear function is
defined by at least two equations, each of which applies to a different part of the domain, i.e.,
before-and-after installation of the RLCs in this case. The linear-intervention model allows for
different slopes of crash frequency on time before and after the installation of the RLCs and also
across the treatment and comparison intersections.
ln(𝜃𝑖𝑡)= α0 +α1 Ti +α2 It>t0i + α3 Ti 𝑟+ α4 Ti It>t0i + β1 X1it + β2 X 2it +⋯+ β10 X10 (4)
30
5.2.3 Model Results and Discussion
To obtain the FB estimates of the unknown parameters, it is required to specify prior distributions
for the hyper-parameters. The most commonly used priors are vague normal distributions (with
zero mean and large variance) for the regression parameters. The posterior estimates of the model's
parameters for the FB methods were obtained via four independent chains with 25,000 iterations
whereby the first 25,000 were used as a burn-in sample. The posterior means and the 80th percentile
Bayesian Credible Intervals (BCIs) of the posterior distributions for each crash type and crash
severity are shown in Tables 5 and 6, respectively. The predictor variable is considered to be
significant at 80% BCI if the values of the 10% and 90% percentiles do not include zero (0), i.e.,
they are both negative or positive. The findings from the analyses are summarized below by target
crash type and target crash severity.
Total Target Crashes
For both treatment and non-treatment four-legged signalized intersections,
• In general, major and minor approach AADT, speed limit of over 30 mph, amber time ≥ 4
seconds, length of pedestrian crossing, and number of driveways ≥ 3 within the intersection
influence area increase the target crash probability.
• Longer all red interval has a positive impact in reducing the target crash probability;
however, it is not significant at 80% BCI.
• Major approach with more than two through lanes and minor approach with more than one
lane result in reduced target crash probabilities.
For four-legged signalized intersections with RLC,
• Compared to the before-period, the after-period experienced fewer target crashes.
• Fewer target crashes are expected at the treatment intersections compared to the non -
treatment intersections; however, it is not significant at 80% interval.
• There is a significant drop in target crashes immediately after the installation of RLCs.
• The target crashes dropped immediately after the installation of RLCs, and then continued
to increase, but they are still lower than the target crashes in the before-period.
Angle/Left-Turn/Right-Turn Crashes
• Compared to the before-period, the after-period experienced fewer angle/left-turn/right-
turn crashes. This observation is significant at 70% BCI.
• Fewer angle/left-turn/right-turn crashes are expected at the treatment intersections
compared to the non-treatment intersections; however, it is not significant at 80% interval.
• There is a significant drop in angle/left-turn/right-turn crashes immediately after the
installation of RLCs.
• The angle/left-turn/right-turn crashes dropped immediately after the installation of RLCs,
and then continued to increase, but they are still lower than the before-period.
31
Rear-end Crashes
• Compared to the before-period, the after-period experienced fewer rear-end crashes.
• More rear-end crashes are expected at the treatment intersections compared to the non-
treatment intersections. This observation is significant at 70% BCI.
• There is a drop in rear-end crashes immediately after the installation of RLCs; however,
this drop is not significant at 80% BCI.
• The rear-end crashes dropped immediately after the installation of RLCs, and they
continued to increase at a steeper rate.
Sideswipe Crashes
• Compared to the before-period, the after-period experienced fewer sideswipe crashes.
• Fewer sideswipe crashes are expected at the treatment intersections compared to the non -
treatment intersections.
• There is a significant drop in sideswipe crashes immediately after the installation of RLCs.
• The sideswipe crashes dropped immediately after the installation of RLCs, and then
continued to increase, but they are still lower than the before-period.
Target PDO Crashes
• Compared to the before-period, the after-period experienced fewer target PDO crashes.
• Fewer target PDO crashes are expected at the treatment intersections compared to the non-
treatment intersections; however, it is not significant at 80% BCI.
• There is a significant drop in target PDO crashes immediately after the installation of
RLCs.
• The target PDO crashes dropped immediately after the installation of RLCs, and then
continued to increase, but they are still lower than the before-period.
Target Fatal/Injury Crashes
• Compared to the before-period, the after-period experienced fewer target fatal/injury
crashes; but it is not significant at 80% BCI.
• Fewer target fatal/injury crashes are expected at the treatment intersections compared to
the non-treatment intersections; however, it is also not significant at 80% BCI.
• There is a significant drop in target fatal/injury crashes immediately after the installation
of RLCs.
• The target fatal/injury crashes dropped immediately after the installation of RLCs, and they
continued to increase, but they are still lower than the before-period.
32
Table 5: Model Results for Different Target Crash Types
Variable/
Parameter
Total Target Crashes Angle/Left-turn/Right-turn
Crashes Rear-end Crashes Sideswipe Crashes
Mean 10% 90% Mean 10% 90% Mean 10% 90% Mean 10% 90%
Intercept -13.371 -15.039 -11.719 -8.508 -11.573 -5.461 -17.916 -20.354 -15.498 -15.539 -18.477 -12.634
ln(major AADT) 0.239 0.129 0.35 -0.092 -0.29 0.106 0.548 0.393 0.704 0.017 -0.172 0.207
ln(minor AADT) 0.607 0.455 0.76 -0.1 -0.38 0.182 0.95 0.711 1.193 0.779 0.507 1.053
Major approach posted
speed > 30 mph 0.072 0.039 0.105 0.202 0.132 0.272 -0.029 -0.076 0.019 0.124 0.065 0.185
Minor approach posted
speed > 30 mph 0.117 0.078 0.156 0.104 0.033 0.176 0.101 0.041 0.16 0.086 0.018 0.154
Yellow time ≥ 4
seconds 0.485 0.278 0.692 0.982 0.538 1.434 0.233 -0.045 0.511 1.294 0.884 1.714
All red time > 2
seconds -0.084 -0.238 0.069 -0.191 -0.524 0.139 -0.309 -0.501 -0.117 0.806 0.466 1.148
Length of pedestrian
crossing 0.012 0.005 0.019 -0.009 -0.021 0.003 0.037 0.026 0.048 0.011 -0.001 0.024
Major approach
through lanes > 2 -1.282 -1.793 -0.776 -0.465 -1.408 0.484 -2.247 -3.068 -1.441 -0.917 -1.889 0.048
Minor approach
through lanes > 1 -0.535 -0.766 -0.304 0.775 0.331 1.219 -1.55 -1.893 -1.209 -0.755 -1.162 -0.352
Number of driveways
within 250 ft of the
intersection
0.219 0.173 0.265 0.325 0.242 0.409 0.192 0.13 0.255 0.254 0.171 0.337
Time indicator (after
treatment) -0.347 -0.472 -0.224 -0.188* -0.365* -0.011* -0.455 -0.626 -0.286 -0.335 -0.538 -0.133
Treatment indicator
(treated intersection) -0.081 -0.311 0.148 -0.085 -0.484 0.317 0.272* 0.001* 0.545* -1.006 -1.436 -0.58
Jump parameter
(Interaction of Time &
Treatment indicator)
-0.552 -0.827 -0.276 -0.622 -1.114 -0.13 -0.202 -0.563 0.159 -1.082 -1.623 -0.544
Treatment effect over
time 0.243 0.158 0.328 0.127 -0.025 0.279 0.25 0.141 0.359 0.329 0.161 0.498
Note: bold values are significant at 80% Bayesian Credible Interval (BCI); * values are significant at 70% BCI instead of 80% BCI.
33
Table 6: Model Results for Different Target Crash Severities
Variable/
Parameter
Total Target Crashes PDO Crashes Fatal/Injury Crashes
Mean 10% 90% Mean 10% 90% Mean 10% 90%
Intercept -13.371 -15.039 -11.719 -13.699 -15.435 -11.974 -15.349 -18.84 -11.892
ln(major AADT) 0.239 0.129 0.35 0.271 0.155 0.388 0.083 -0.148 0.316
ln(minor AADT) 0.607 0.455 0.76 0.627 0.469 0.787 0.478 0.123 0.836
Major approach posted
speed > 30 mph 0.072 0.039 0.105 0.064 0.03 0.099 0.139 0.058 0.221
Minor approach posted
speed > 30 mph 0.117 0.078 0.156 0.11 0.07 0.151 0.164 0.075 0.252
Yellow time ≥ 4 seconds 0.485 0.278 0.692 0.475 0.261 0.691 0.514 0.025 1.013
All red time > 2 seconds -0.084 -0.238 0.069 -0.124 -0.289 0.039 0.092 -0.250 0.431
Length of pedestrian
crossing 0.012 0.005 0.019 0.014 0.007 0.021 -0.001 -0.016 0.015
Major approach through
lanes > 2 -1.282 -1.793 -0.776 -1.353 -1.887 -0.821 -0.803 -2.026 0.411
Minor approach through
lanes > 1 -0.535 -0.766 -0.304 -0.507 -0.748 -0.266 -0.735 -1.265 -0.214
Number of driveways
within 250 ft of the
intersection
0.219 0.173 0.265 0.224 0.176 0.273 0.186 0.088 0.286
Time indicator (after
treatment) -0.347 -0.472 -0.224 -0.376 -0.504 -0.247 -0.154 -0.416 0.11
Treatment indicator
(treated intersection) -0.081 -0.311 0.148 -0.011 -0.253 0.228 -0.272 -0.766 0.227
Jump parameter
(Interaction of Time &
Treatment indicator)
-0.552 -0.827 -0.276 -0.494 -0.784 -0.205 -0.902 -1.48 -0.328
Treatment effect over time 0.243 0.158 0.328 0.206 0.116 0.296 0.409 0.238 0.583
Note: bold values are significant at 80% Bayesian Credible Interval (BCI).
34
6. REFLECTION
The main objective of this study was to evaluate the safety effectiveness of the PRL Enforcement
Program in the City of Miami Beach, Florida. The simple before-and-after analysis and the full
Bayes before-and-after evaluation approach were used to quantify the safety effectiveness of the
RLCs. The analysis was based on target crash type which includes angle/left-turn/right-turn, rear-
end, and sideswipe crashes, and target crash severity which includes PDO and fatal/injury crashes.
In general, three main inferences could be drawn from the analysis results.
6.1 Target Crash Type and Target Crash Severity
The presence of RLCs at the study intersections tend to increase rear-end crashes and decrease
angle/left-turn/right-turn and sideswipe crashes. This observation is consistent with several other
previous studies that have focused on analyzing the effectiveness of RLCs at intersections (Høye,
2013; Ahmed and Abdel-Aty, 2015; Llau et al., 2015; Claros et al., 2017).
Rear-end crashes are often less severe compared to angle crashes. For example, Figures 26 (a) and
(b) show the proportion of fatal and severe injury crashes by these crash types in the City of Miami
Beach and statewide, respectively. Note that these figures are for the year 2013 and for all crashes
that occurred on non-limited access facilities. It can be inferred from Figure 26 that in the City of
Miami Beach, about 2.4% of all angle/left-turn/right-turn crashes result in fatal or incapacitating
injury, while a relatively lower 0.8% of all rear-end crashes are fatal and incapacitating injury
crashes. A similar trend, nonetheless at a higher magnitude, is observed in Florida (Figure 26 (b)).
(a) City of Miami Beach (b) State of Florida
Figure 26: Percent of Fatal and Severe Injury Crashes by Target Crash Type
6.2 General Crash Trend
From the full Bayes model results, it can be inferred that crashes at the treatment intersections
were on an increasing trend. However, this trend may not be attributed to the presence of RLCs.
In general, recent years have seen an increasing trend in crashes. At the city, state, and the national
level, crashes are generally on an increasing trend, especially since 2011. Figure 27 gives the
annual crash trends from 2008-2016 in the City of Miami Beach, the State of Florida, and the
United States.
2.4
0.8
0.0
2.1
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Pe
r
c
e
n
t
o
f
F
a
t
a
l
&
S
e
v
e
r
e
In
j
u
r
y
C
r
a
s
h
e
s
5.5
2.1
1.0
3.9
0.0
1.0
2.0
3.0
4.0
5.0
6.0
Pe
r
c
e
n
t
o
f
F
a
t
a
l
&
S
e
v
e
r
e
In
j
u
r
y
C
r
a
s
h
e
s
35
(a) Annual Crash Trend in the City of Miami Beach (Source: Signal Four Analytics, 2018)
(b) Annual Crash Trend in Florida (Source: Signal Four Analytics, 2018)
(c) Annual Crash Trend in the U.S. (Source: NHTSA, 2018)
Figure 27: Annual Crash Trend
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
2008 2009 2010 2011 2012 2013 2014 2015 2016
To
t
a
l
C
r
a
s
h
e
s
Year
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
2008 2009 2010 2011 2012 2013 2014 2015 2016
To
t
a
l
C
r
a
s
h
e
s
Year
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
2008 2009 2010 2011 2012 2013 2014 2015 2016
To
t
a
l
C
r
a
s
h
e
s
Year
36
6.3 Safety Performance of Non-treatment Intersections
The simple before-and-after study revealed that most of the treatment intersections experienced
fewer target crashes after the installation of RLCs. The average number of target crashes at the
non-treatment (i.e., comparison) intersections have also reduced in the after-period (i.e., from
2011-2013). Drivers, when they encounter a RLC at an intersection, may anticipate RLCs at other
intersections within the region, and as a result drive more cautiously. This behavior may result in
a reduction in target crashes at non-treatment intersections as well. Moreover, jurisdiction-wide
publicity of RLCs and the general public’s lack of knowledge on the exact installation locations
of RLCs may result in fewer target crashes within the region. Figure 28 shows the average number
of target crashes at non-treatment intersections during the before- and after- periods. Note that
target crashes include angle/left-turn/right-turn, rear-end, and sideswipe crashes.
A total of five signalized intersections that are far away from the intersections with RLCs were
identified. The average number of target crashes at these far-away intersections was found to be
higher during 2011-2013 (i.e., after RLC installation) compared to 2008-2009 (i.e., before RLC
installation). In general, angle/left-turn/right-turn and sideswipe crashes increased in 2011-2013,
while rear-end crashes reduced. In other words, the intersections in the vicinity of RLCs, in
general, were found to experience fewer angle/left-turn/right-turn and sideswipe crashes, while the
intersections far away from the treatment sites were found to experience an increase in angle/left-
turn/right-turn and sideswipe crashes in 2011-2013 compared to 2008-2009.
37
Arthur Godfrey Rd. and Meridian Ave
Intersection
Alton Rd. and 16th St.
Alton Rd. and 11th St.
Alton Rd. and 8th St.
Washington Ave and 16th St.
41st St. and Indian Creek Dr
Figure 28: Target Crashes by Crash Type at Comparison Intersections
2
7
1
9.5
0.7
5
0
5.7
0
2
4
6
8
10
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4.5 4
6.5
15
3.3 3.3 3
9.7
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
2
3
2
7
3 3
1.7
7.7
0
1
2
3
4
5
6
7
8
9
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
4
6.5
8.5
19
5.3 5.3
3
13.7
0
5
10
15
20
Angle Rear-end Sideswipe Total Target
Crashes
Before After
1.5
8 7
16.5
1.3
3.3 3
8
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
0.5
4.5
2
7
0.3
4 3.7
8
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
Angle Rear-end Sideswipe Total Target
Crashes
Before After
38
17th St. and James Ave
5th St. and Collins Ave
63rd St. and Pine Tree Dr
Pine Tree Dr and Sheridan Ave (3-legged)
Washington Ave and 15th St. (3-legged)
West Ave and 11th St. (3-legged)
Figure 28 (cont’d): Target Crashes by Crash Type at Comparison Intersections
2.5
1
0
3.5
1
0.7
2
3.7
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Angle Rear-end Sideswipe Total Target
Crashes
Before After
3.5
2 1.5
7
0.3 0.7
2.3
3.3
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Angle Rear-end Sideswipe Total Target
Crashes
Before After
0.5
1
0.5
2
0.3
0
0.7
1
0.0
0.5
1.0
1.5
2.0
2.5
Angle Rear-end Sideswipe Total Target
Crashes
Before After
0
1.5
0
1.5
0.7 0.7
0.3
1.7
0
1
1
2
2
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
1
1.5
0
2.5
0.3
1
0.3
1.7
0
1
1
2
2
3
3
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
0
3.5
0.5
4
1
1.3
0.7
3
0
1
1
2
2
3
3
4
4
Angle Rear-end Sideswipe Total Target
Crashes
Before After
39
Meridian Ave and 8th St.
Washington Ave and 11th St.
Meridian Ave and 11th St.
5th St. and Ocean Dr (3-legged)
Washington Ave and 6th St. (3-legged)
Figure 29: Target Crashes at Intersections Far Away from the Treatment Sites
0.5
0 0
0.5
0.7 0.7
0.3
1.7
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Angle Rear-end Sideswipe Total Target
CrashesBeforeAfter
0
1
3.5
4.5
2
7
1.7
10.7
0
2
4
6
8
10
12
Angle Rear-end Sideswipe Total Target
Crashes
Before After
0
0.5
0
0.5
0.7
1
0
1.7
0
0
0
1
1
1
1
1
2
2
Angle Rear-end Sideswipe Total Target
Crashes
Before After
0
0.5
0
0.5
0
1.3
0.7
2
0
1
1
2
2
3
Angle Rear-end Sideswipe Total Target
Crashes
Before After
0
1 0.5
1.51.3
2.3
3.7
7.3
0
1
2
3
4
5
6
7
8
Angle Rear-end Sideswipe Total Target
Crashes
Before After
40
7. SUMMARY
The main objective of this study was to evaluate the safety effectiveness of the PRL Enforcement
Program in the City of Miami Beach, Florida. The simple before-and-after analysis and the full
Bayes before-and-after evaluation approach were used to quantify the safety effectiveness of the
RLCs. The analysis was based on target crash type which includes angle/left-turn/right-turn, rear-
end, and sideswipe crashes, and target crash severity which includes PDO and fatal/injury crashes.
A total of ten RLCs are operational at nine signalized intersections in the City of Miami Beach.
Due to the long period of construction activity, the intersection where Alton Road meets 17th Street
was not included in the study. The simple before-and-after crash data analysis was conducted for
the remaining eight signalized intersections. The advanced full Bayes before-and-after analysis
was conducted only for the five four-legged signalized intersections. Only three treatment
intersections are three-legged; the sample size is too small to yield reliable results from the FB
statistical analysis.
7.1 Simple Before-and-after Crash Data Analysis
7.1.1 Four-legged Intersections
At four-legged intersections with RLCs,
In general, there was a reduction in target crashes after the installation of RLCs.
Among the target crash types, angle/left-turn/right-turn and sideswipe crashes usually
decreased while rear-end crashes usually increased. In other words, the presence of RLCs
at the study intersections were found to increase rear-end crashes and decrease angle/left-
turn/right-turn and sideswipe crashes. However, rear-end crashes are often considered to
be less severe compared to angle/left-turn/right-turn crashes.
In general, there was a reduction in PDO target crashes, but target crashes resulting in
injuries slightly increased.
At approaches with RLCs,
At three of the five intersections, target crashes reduced after the installation of RLCs.
At two of the five intersections, total crashes reduced after the installation of RLCs.
At three of the five intersections, rear-end crashes increased after the installation of RLCs.
7.1.2 Three-legged Intersections
Overall, the intersections with RLCs experienced a reduction in target crashes after the
installation of RLCs.
At all the three intersections, rear-end and sideswipe crashes reduced after the installation
of RLCs.
Angle crashes reduced at two of the three intersections.
In general, there was a reduction in PDO target crashes, but target crashes resulting in
injuries slightly increased.
At approaches with RLCs, there were no target crashes after the installation of RLCs.
41
7.1.3 Safety Performance of Intersections with No RLCs
The average number of target crashes at the non-treatment intersections (i.e., signalized
intersections with no RLCs) that are in the vicinity of treatment intersections have also reduced in
the after-period (i.e., from 2011-2013). Drivers, when they encounter a RLC at an intersection,
may anticipate RLCs at other intersections within the region, and as a result drive more cautiously.
This behavior may result in a reduction in target crashes at intersections with no RLCs as well.
Moreover, jurisdiction-wide publicity of RLCs and the general public’s lack of knowledge on the
exact installation locations of RLCs may result in fewer target crashes within the region.
The average number of target crashes at five signalized intersections that are far away from the
intersections with RLCs was found to be higher during 2011-2013 (i.e., after RLC installation)
compared to 2008-2009 (i.e., before RLC installation). The intersections in the vicinity of RLCs,
in general, were found to experience fewer angle/left-turn/right-turn and sideswipe crashes, while
the intersections far away from the treatment sites were found to experience an increase in
angle/left-turn/right-turn and sideswipe crashes in 2011-2013 compared to 2008-2009.
7.2 FB Before-and-after Analysis
• In general, crashes at the treatment intersections were on an increasing trend. However,
this trend may not be attributed to the presence of RLCs. Recent years have seen an
increasing trend in crashes. At the city, state, and the national level, crashes are generally
on an increasing trend, especially since 2011.
• There is a significant sudden drop in all types of target crashes immediately after the
installation of RLCs.
• Compared to the before-period, the after-period experienced:
• Fewer target crashes
• Fewer angle/left-turn/right-turn crashes
• Fewer sideswipe crashes
• More rear-end crashes
• The sideswipe and angle/left-turn/right-turn crashes dropped immediately after the
installation of RLCs, and then continued to increase, but they are still lower than the before-
period.
• The rear-end crashes dropped immediately after the installation of RLCs, and then
continued to increase, but they increased at a steeper rate.
42
REFERENCES
Ahmed, M. M., & Abdel-Aty, M. (2015). Evaluation and spatial analysis of automated red-light
running enforcement cameras. Transportation Research Part C: Emerging Technologies, 50, 130-
140.
American Traffic Solutions (ATSOL). (2018). Red-light running dangers in the United States.
Retrieved from
https://www.atsol.com/wp-content/uploads/2017/06/ATS-RLR-Dangers-Cutsheet-2017.pdf
Carriquiry, A., & Pawlovich, M. (2004). From empirical Bayes to full Bayes: methods for
analyzing traffic safety data. White Paper, Iowa State University.
City of Fort Lauderdale. (n.d.). Red light safety camera program. Retrieved from
https://www.fortlauderdale.gov/departments/transportation-and-mobility/red-light-safety-
camera-program
City of Miami Springs. (n.d.). Get the facts — red-light cameras save lives. Retrieved from
https://www.miamisprings-
fl.gov/sites/default/files/fileattachments/police/page/19539/get_the_facts_-
_red_light_cameras_save_lives.pdf
Claros, B., Sun, C., & Edara, P. (2017). Safety effectiveness and crash cost benefit of red light
cameras in Missouri. Traffic Injury Prevention, 18(1), 70-76.
Decina, L. E., Thomas, L., Srinivasan, R., & Staplin, L. (2007). Automated enforcement: A
compendium of worldwide evaluations of results. Report prepared for National Highway Traffic
Safety Administration (NHTSA), Kulpsville, PA. Retrieved from
https://www.google.com/search?q=HS810763.pdf&rlz=1C1GGRV_enUS752US753&oq=HS81
0763.pdf&aqs=chrome..69i57.10366j0j8&sourceid=chrome&ie=UTF-8
Florida Online Traffic School. (2018). Red-light camera citations in Florida. Retrieved from
https://www.floridaonlinetrafficschool.com/articles/red-light-camera-tickets.aspx
Høye, A. (2013). Still red for red light cameras? An update. Accident Analysis & Prevention, 55,
77-89.
Insurance Institute for Highway Safety – Highway Loss Data Institute (IIHS-HLDI). (2016).
Turning off red light cameras costs lives, new research shows. Retrieved from
https://www.iihs.org/iihs/news/desktopnews/turning-off-red-light-cameras-costs-lives-new-
research-shows
Ko, M., Geedipally, S. R., Walden, T. D., & Wunderlich, R. C. (2017). Effects of red light running
camera systems installation and then deactivation on intersection safety. Journal of safety
research, 62, 117-126.
43
Li, W., Carriquiry, A., Pawlovich, M., & Welch, T. (2008). The choice of statistical models in
road safety countermeasure effectiveness studies in Iowa. Accident Analysis & Prevention, 40(4),
1531-1542.
Llau, A. F., Ahmed, N. U., Khan, H. M., Cevallos, F. G., & Pekovic, V. (2015). The impact of red
light cameras on crashes Within Miami–Dade County, Florida. Traffic Injury Prevention, 16(8),
773-780.
National Highway Traffic Safety Administration (NHTSA). (2018). Traffic safety facts 2016 data
(DOT HS 812580). NHTSA’s National Center for Statistics and Analysis, Washington D.C.
Park, J., Abdel-Aty, M., & Lee, J. (2016). Use of empirical and full Bayes before–after approaches
to estimate the safety effects of roadside barriers with different crash conditions. Journal of Safety
Research, 58, 31-40.
Pulugurtha, S. S., & Otturu, R. (2014). Effectiveness of red light running camera enforcement
program in reducing crashes: Evaluation using “before the installation”,“after the installation”, and
“after the termination” data. Accident Analysis & Prevention, 64, 9-17.
Shin, K., & Washington, S. (2007). The impact of red light cameras on safety in Arizona. Accident
Analysis & Prevention, 39(6), 1212-1221.
The Geoplan Center, Department of Urban and Regional Planning, University of Florida. (2018).
Signal Four Analytics. The Geoplan Center, Department of Urban and Regional Planning,
University of Florida, Gainesville, Florida. https://s4.geoplan.ufl.edu/