a case study of bicyclist and pedestrian crashes in...
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A CASE STUDY OF BICYCLIST AND PEDESTRIAN CRASHES IN JACKSONVILLE FLORIDA
By
ROSARIO E. LACAYO
A TERMINAL PROJECT PRESENTED TO THE GRADUATE SCHOOL OF THE
UNIVERSITY OF FLORIDA IN PARTIAL FUFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING
UNIVERSITY OF FLORIDA
2016
© 2016 Rosario Lacayo
To my mom and dad.
4
ACKNOWLEDGMENTS
I would like to thank Dr. Steiner for her patience and assistance during my terminal
project. I would also like to thank Dr. Bejleri for his help in Geo Spatial modeling and for
creating Signal Four Analytics. Finally, I would like to thank Stanley Latimer for being so
helpful in a beginner GIS course which helped me tremendously in my terminal project.
5
TABLE OF CONTENTS page
A Case Study of Bicyclist and Pedestrian CRASHES in Jacksonville Florida ................. 1
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 6
LIST OF FIGURES .......................................................................................................... 7
ABSTRACT ..................................................................................................................... 9
INTRODUCTION ........................................................................................................... 11
LITERATURE REVIEW ................................................................................................. 15
Socio- Economic and Demographic Factors ........................................................... 15
Land Use Factors ................................................................................................... 16 Traffic Volume ......................................................................................................... 17
Location of Crashes……………………………………………………………………….17
METHODOLOGY .......................................................................................................... 19
Methods and Procedures ........................................................................................ 19 Results……………………………………………………………………………………...20
Findings .................................................................................................................. 59
CONCLUSIONS AND RECOMMENDATIONS ............................................................. 68
LIST OF REFERENCES ............................................................................................... 74
6
LIST OF TABLES
Table page Table 3-1- Selection Criteria for Case Study Intersections (Source: Author using
Signal Four Analytics Data) ................................................................................ 22
Table 3-2 Land Use Characteristics of Intersections .................................................... 54
Table 3-3. Estimate of Case Study Intersection Neighborhoods Age group (18 and over or 18 and younger) (Source: Census, 2010)............................................... 55
Table 3-4. Intersection Characteristics ......................................................................... 56
Table 3-5. Duval County Planning Districts .................................................................. 57
Table 3-6. Income-to-Poverty Ratios for Census Tracts surrounding Intersections with the Highest Number of Pedestrian and Bicycle Crashes in Duval County, 2011-2014. ......................................................................................................... 58
Table 3-7. Age Group of Case Study Intersection Neighborhoods………………………59 Table 3-8. Time of day and Location of Crashes…………………...……………………..65
7
LIST OF FIGURES
Figure page Figure 1. Number of Pedestrian Crashes and Fatalities in Florida Data. Source:
NHTSA ............................................................................................................... 12
Figure 1-2. Bicycle fatality rates per 100,000 people. Source: NHTSA ........................ 12
Figure 3. Spatial Distribution of Crashes Map .............................................................. 23
Figure 3-1. Location of all Case Study Intersections. A: City wide locations. B: Close-up of case study intersections ............................................................................ 24
Figure 3-2. Exposure Index Versus # of Crashes ............ Error! Bookmark not defined.
Figure 3-3. Powers Avenue and University Boulevard. A: Map area showing intersection. B: Aerial of Powers Ave and University Blvd. W Intersection ......... 26
Figure 3-4. Crashes at Powers-University Blvd Intersection ........................................ 27
Figure 3-5. Blanding Boulevard and Collins Road. A: Map area showing intersection. B: Aerial of Blanding Blvd and Collins Rd Intersection ....................................... 28
Figure 3-6. Crashes at Blanding-Collins Road Intersection .......................................... 29
Figure 3-7. Tampico Road and 103rd Street. A: Map showing location. B: Aerial of Tampico Rd and 103rd St Intersection ................................................................ 30
Figure 3-8- Crashes at Tampico-103rd Street Intersection ............................................ 31
Figure 3-9. Beach Boulevard and Countryside Village Drive and Desalvo Road. A: Map showing location. B: Aerial of Beach Blvd and Desalvo Rd and Countryside Village D ......................................................................................... 32
Figure 3-10. Crashes at Beach-Desalvo Road Intersection ......................................... 33
Figure 3-11. Beach Boulevard and University Boulevard South. A: Map showing location. B: Aerial of Beach Blvd and University Blvd W ..................................... 34
Figure 3-12. Crashes at Beach-University Blvd Intersection ........................................ 35
Figure 3-13. Ricker Road and 103rd Street. A: Map showing location. B: Aerial of Ricker Rd and 103rd St Intersection .................................................................... 36
Figure 3-14. Crashes at Ricker-103rd Street Intersection ............................................. 37
Figure 3-15. Century 21 Drive and Atlantic Boulevard and Acme Street. A: Map showing location. B: Aerial of Century 21 Dr and Atlantic Blvd Intersection ....... 38
8
Figure 3-16. Crashes at Atlantic-Acme Street Intersection ........................................... 39
Figure 3-17. Timuquana Road and Seaboard Avenue. A: Map showing location. B: Aerial of Timuquana Rd and Seaboard Ave Intersection. ................................... 40
Figure 3-18. Crashes at Seaboard-Timuquana Road Intersection ............................... 41
Figure 3-19. Atlantic Boulevard and Leon Road. A: Map showing location. B: Aerial of Atlantic Blvd and Leon Rd Intersection. .......................................................... 42
Figure 3-20. Crashes at Atlantic-Leon Road Intersection ............................................. 43
Figure 3-21. 103rd Street and Firestone Road. A: Map showing location. B: Aerial of 103rd St and Firestone Rd Intersection. .............................................................. 44
Figure 3-22. Crashes at Firestone-103rd Street Intersection ........................................ 45
Figure 3-23. Catoma Street and Timuquana Road. A: Map showing location. B: Aerial of Catoma St and Timuquana Rd Intersection………………………….....46
Figure 3-24. Catoma Street -Timuquana Road Intersection………………………......…47 Figure 3-25. San Jose and Loretto Road. A: Map showing location. B: Aerial of
San Jose and Loretto Road Intersection……………………………………………48 Figure 3-26. San Jose-Loretto Road Intersection……………………………………..…..49 Figure 3-27. Mayport Road and Assisi Lane . A: Map showing location. B: Aerial of Mayport Road and Assisi Lane Intersection…………………………………….….50 Figure 3-28. Mayport Rd -Assisi Ln Intersection………………………………….…….…51 Figure 3-29. Beaver Street West and North Laura Street . A: Map showing location. B: Aerial of Beaver Street West and North Laura Street Intersection…………..….52 Figure 3-30. Beaver St W- N Laura St Intersection…………………………….…………53 Figure 3-31. Population Estimates for Intersection Neighborhoods ............................. 65
Figure 3-32. Bikes-ped Crashes in Low to Mod Income Boundaries ............................ 66
Figure 3-33. Countywide Analysis with relation to percent of median income .............. 67
9
Terminal Project Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Urban and Regional
Planning
A CASE STUDY OF BICYCLIST AND PEDESTRIAN CRASHES IN JACKSONVILLE, FLORIDA
By
Rosario Lacayo
April 2017
Chair: Dr. Ruth L. Steiner Cochair: Dr. Ilir Bejleri Major: Urban and Regional Planning
This study investigated the spatial distribution of bicyclist and pedestrian crashes
in Jacksonville, Florida, and analyzed the social and physical factors that affect the risk
of getting involved in such crashes. Precisely, this study attempted to understand the
influence of socio- economic, demographic, land use, and traffic characteristics on
bicyclist and pedestrian crash rates. The crashes examined in this study involved
pedestrian/bicyclists -motor vehicles collisions. An exploratory spatial analysis of
bicyclist and pedestrian crashes was developed to identify major concentrations of
these types of crashes. This data was used to define the intersections where city
residents are more at risk for crashes. The aggregate analysis was then used for a
more qualitative analysis to identify the primary relationships between land use, socio-
economic, demographic, and traffic factors at the census block group level. The study
used pedestrian and bicyclist crash data provided by Florida’s Signal Four Analytics,
land use category information from the Jacksonville Planning and Development
Department, and socio- economic and demographic data from the 2010 U.S. Census.
10
The results revealed that bicyclist and pedestrian crashes throughout Jacksonville are
clustered in areas of lower income where commercial and residential land use abut
arterial and local roadway intersections. This data only indicate that an association may
exist between bicyclist/pedestrian crashes and poverty. Recommendations for
improving risk are also provided which include conducting audits at intersections for
redesign and traffic mitigation; increasing sidewalk widths and bicycle lanes; increasing
driver and bicyclist-pedestrian safety education; jaywalking operations; and improving
agency partnerships to encourage non-vehicular transportation such as walking and
bicycling.
11
CHAPTER 1 INTRODUCTION
Located in Northeast Florida, Jacksonville is geographically the largest city in the
United States, covering over 840 square miles (Visit Jax, 2011), and continues to be
one of America’s most dangerous communities for pedestrians, ranking fourth worst in
the 2016 Pedestrian Danger Index (PDI) (Dangerous by Design, 2016). The PDI ranks
the 104 largest metro areas in the country. Unfortunately, Jacksonville’s current ranking
is just a slight improvement from its number three standing in 2014. At a state level,
Florida ranked first in the PDI with 5,142 total pedestrian deaths from 2005 and 2014.
Depicted in Figure 1, 8,956 pedestrian crashes and fatalities occurred in 2016 in the
State of Florida. Between 2005 to 2014, 46,149 pedestrians were hit and killed by
vehicles (Smart Growth America, 2016) in the United States. An estimated average of
13 pedestrians per day were struck and killed by an automobile in 2014, making
Americans 7.2 times more likely to die as a pedestrian when compared to fatalities from
a natural disaster (Smart Growth America 2016).
In like manner, the safety of bicycling is also a major concern. In 2011 the
Centers for Disease Control and Prevention found Florida to have the highest rate of
bicycling deaths in the United States, more than doubling the nationwide total of 23 per
100,000 people (FDOT, 2013). Figure 1-2 shows the national and state of Florida trend
in relation to bicyclist fatalities according to data from the National Highway Traffic
Safety Administration (NHTSA, 2016). More disturbingly, Florida accounted for 17.4
percent of all U.S. bicycle fatalities. According to the NHTSA, 726 bicyclists were killed
and approximately 50,000 injured in crashes with motor vehicles in the United States,
which accounts for 2.2 percent of all traffic fatalities in 2014.
12
Figure 1. Number of Pedestrian Crashes and Fatalities in Florida Data. Source: NHTSA
Figure 1-2. Bicycle fatality rates per 100,000 people. Source: NHTSA
13
However, shouldn’t everyone should be able to feel safe while walking or riding
their bicycles in their community? America Walks Executive Scott Bricker said “People
really should have the right to walk safely. It starts with recognition, this is a critical issue
and recognizing that people should be able to access critical services on foot”, told the
(Florida Times-Union, 2014). For that reason, many of these crashes can be linked to
factors such as site designs that are not effectively serving the community, or social and
economic barriers that cause residents to commute by walking or bicycling.
Consequently, the sheer size of Jacksonville makes it a vehicle-dependent city which
presents a more unique challenge when it comes planning for and reversing this
problem.
Thus, it is very important to understand why people walk or use bicycles and the
factors that promote it. For some, walking and bicycling are activities influenced by the
physical and health related benefits, but for others it is due to a lack of safe and
convenient access to daily activities. The objectives covered in this study explore the
spatial distribution of bicyclist and pedestrian crashes in Jacksonville to better
understand the connection between such crashes and the socio-economic,
demographic, and built environment characteristics. Based on the findings, this study
will also offer recommendations for improving bicycle and pedestrian safety in
Jacksonville.
Two hypotheses are proposed:
1. Bicyclist and pedestrian crashes are occurring in areas of lower income.
2. Minority population groups are more prone to being involved in these types of crashes because of the likelihood of living in these lower income neighborhoods.
14
In chapter 2, the literature review summarizes the impacts associated with
pedestrian and bicycle crashes through studies that focused on traffic volume, land use,
demographics and built environment. Academic research indicates that pedestrian
crashes are related to land use and traffic volume (Levine et al. 1995), and strongly tied
to socio-economic characteristics (White and Barker, 2000) where some groups seem
to be more affected than others (Corless and Ohland,2000). Then, chapter 3, describes
the case study methodology used in this project. This includes the analysis of existing
intersection characteristics and overall demographics used in this project. The findings
of this case study are described in chapter 4. The conclusion in chapter 5 summarizes
the recommendations identified by this project.
15
CHAPTER 2 LITERATURE REVIEW
Socio- Economic and Demographic Factors
Numerous studies have analyzed the effects of demographics on pedestrian
crashes. Specifically, Hong, Lee and Jang (2015) present a causative relationship of
pedestrian crashes in communities all over the world. The elderly population,
sometimes referred to as “mobility handicapped” was found to be responsible for 13.8
percent of pedestrian –traffic related deaths in Korea. In relation to age, diminished
attention capacity is also more likely to affect older pedestrians, increasing the risk of
being involved in such crashes. the slow physical speed due to a lack of agility found in
the older population greatly affected the ability to traverse a wide road crossing where
the curb is furthest from the driver (Dunbar, 2011).
Despite demographic and geographic factors, including climate, population
density, and within high or low income countries, all pedestrians are vulnerable to
vehicular collisions. Contrary to popular belief, urban streets that are densely populated,
are safer than rural roads, with a lower fatality rate (NHTSA,2016). Less densely
populated and urbanized areas had a higher fatality risk, even in comparison to Western
Europe which is densely populated and urbanized.
Research also found that pedestrian injuries were directly related to age
composition including a pedestrian’s consumption of alcoholic beverages (LaScala,
2000). Studied in San Francisco, pedestrian injuries were in fact related to
environmental characteristics such as population density, the availability of alcohol
establishments in that area, traffic flow, age, education, unemployment, and gender.
Other characteristics including time of day and day of the week influenced the likelihood
16
of crashes in suburban and rural areas, where fatal and more serious injuries were
more likely related to night-time driving and alcohol. (Levine, 1995).
Land Use Factors
Other studies indicate that land use types have an effect on the frequency of
pedestrian crashes. In Oahu, Hawaii, crashes appeared to be closer to employment
centers than residential areas (Levine, 1995). The connection between land use and
pedestrians in Hawaii was further strengthened, as the land use types with the most
pedestrian crashes were found in the Visitor Lodging and Commercial categories (Kim
& Yamashita, 2002). The natural environment does influence pedestrian casualties
which tend to not be highest in dense residential areas due to low speeds (Graham &
Glister, 2003). Using linear regression models of the measures of travel within socio-
economic and neighborhood characteristics, considerable evidence on the association
between land use and transportation suggests that certain behaviors can be changed
by changing land use characteristics (Kitamura et al., 1997). The characteristics of
destinations are also significant predictors of walking behavior (Vale, 2015), as well as
distances of less than one and a half miles, where bicycling is the quickest commute
mode (University of Washington, 2001).
Additional research has investigated the relationship between crash frequency
and the distance of crashes from a residential area. Census data and road casualties
from the Lothian region in Scotland revealed a relationship between the distance
between the residential region and the crash location (Abdalla et al., 1997). The number
of casualties (including driver, rider, or passenger) which triggered an inter-reliant
relationship with lower income residential areas, occurred at distances greater than
17
2,500 meters and decreased as the distance from the residence area increased.
Casualty frequencies were higher nearer to residential zones.
Traffic Volume
Studies have also explored the relationships between traffic volume and
pedestrian crashes. Levels of employment and population were other significant driving
factors in traffic volume and in pedestrian casualties (Levine, 1995). Local factors
including the volume of people, traffic, physical nature of the environment, and
infrastructure were assumed to influence pedestrian casualties, as daily traffic flow has
a positive association with rates of injury (LaScala, 2000). At private grade crossings,
which are not maintained by public entities, additional factors including train speed,
drivers who did not stop, flying railroad equipment, weather, and sign visibility were also
found to increase fatalities and injuries (Haleem, 2016).
Location of Crashes
While bicycles only account for 1% of trips taken in the United States (CDC,
2016), bicyclists are also facing a high risk of injury. The majority of bicycle and car
collisions occur at intersections accounting for as much as 74 percent of the total
(Watchel & Lewiston, 1994). Where most collisions occur, the complex nature of
intersections is a result of the common place where bicycles and vehicles cross paths
(Wang & Nihan, 2004). Other sources also indicate that intersections are intricate, and
therefore different models would need to be used to assess the risk in bicycle and motor
vehicle crashes. Likewise, a significant relationship between impact speed and age
indicates an increasing fatality rate with increased operator age and higher speeds.
Summary of Literature Review
18
After reviewing the literature, this research found several important points that
should be considered when performing studies on pedestrian and bicyclists. Age
distribution in areas should be taken into account as well as the built environment.
Consequently, low income areas are positively related to the number of bicycle and
pedestrian crashes. The effect of land use particularly residential and commercial can
affect pedestrian and bicyclist crash rates and affect traffic flow. Additionally, man
crashes occur at intersections.
19
CHAPTER 3 METHODOLOGY
Methods and Procedures
This chapter describes the methodology that was used to evaluate the case study
areas. To investigate the effects of land use, along with socio- economic and
demographic features, qualitative research at an urban block level was performed. The
crash data from 2011 to 2014 used in this research was collected from Florida Signal
Four Analytics. Signal Four Analytics is a web-based system that creates crash
mapping and analysis data for current and previous years. The data included a total of
102 pedestrian and bicyclist crash records illustrated in Table 3-1. Roadway
classification data was obtained from the Florida Geographic Data Library. Once the
data was attained from Signal Four Analytics, it was geocoded into ArcMap 10.5 to
create the spatial distribution analysis seen in Figure 3. Crashes were mapped and
aggregated to Census block group data for spatial analysis. Once the geographical
displays of the crashes were mapped, the “hot spots” /intersections that involved the
highest numbers of pedestrian and bicyclist crashes were identified. Figure 3-1
identifies the location of the “hot spots”/ case study intersections. Therefore, from the
frequency, it was possible to see what areas of town had a cluster of crashes. This
spatial analysis yields the areas in Jacksonville where, statistically, bicyclists and
pedestrians are most at risk for crashes. Local roadway information was obtained from
the GIS Division of the City of Jacksonville’s Planning and Development Department.
The multiple regression model which is useful in several studies is not appropriate when
using the number of accidents as a dependent variable. The income to poverty ratio
table was prepared by using Census ACS estimates which are drawn from the Current
20
Population Survey Annual Social and Economic Supplement. From there I used the in-
question census blocks (belong to the case study neighborhoods). The third column is a
subtotal of the first to columns to illustrate how close a family is to the poverty threshold.
Families and individuals with below 100% are in poverty.
Results
A total of 14 intersections were ranked within the top 5 of most pedestrian/
bicyclist and vehicle crashes. These intersections had a minimum of 3 pedestrian
crashes or more. Table 1 identifies the 14 intersections that ranked first through fifth
place from 2011 to 2014, and were therefore selected for the case study. Due to
variations in the availability and form of data associated with each set of factors, it was
not possible to apply the same intensity of analysis for each location. To aid in the
analysis, a map of each intersection was created showing the location of each hot spot
and, an intersection aerial (see pages 26 - 53.)
Risk was measured in terms of the total number of pedestrian crashes, and a
modified exposure index was calculated by dividing the number of pedestrian/bicyclist
crashes by the number of crashes at that intersection as depicted in Table 3-1. Figure
3-2 compares the case study intersections in terms of number of crashes and exposure
index. Ideally, dividing the number of pedestrian/bicycle crashes by the annual vehicle
traffic and a bicyclist and pedestrian count on the intersecting street would have been a
better measure for exposure risk, but due to data limitations, traffic volume numbers
were unattainable. Additionally limitations include not using severity as a factor. Based
on the number of pedestrian/bicyclist count, and number of vehicles, another exposure
index taking into consideration traffic speed, and time spent to cross the intersection or
21
a mid-block location would also model the bicyclist/pedestrian crossing behavior.
However, the lacks of data to determine the exposure risk was a major limitation.
To understand the role of land use, demographics, and socio-economic
characteristics in bicyclist and pedestrian crashes, it was important to first study the
common trends of where the crashes occurred. This drew attention to areas with
heightened levels of poverty, where bicyclists and pedestrians are common. Using
ArcMap, point data representing bicyclist and pedestrian crashes were overlaid on
socio-economic and demographic characteristics which include poverty rate, race, and
income for each intersection. 2010 Census data at the block level provided socio-
economic and demographic variables (see Tables 3 -2 and 3-3). A county wide analysis
was also performed to obtain better overall assessment of role poverty plays in the city’s
population.
Land use data in conjunction with Census information was used to examine the
land use patterns in the areas of concern. Each area of concern was categorized by
land use type, based on the Florida Land Use and Land Cover Classification System
(FLUCCS). In addition, I conducted a detailed and systematic analysis to evaluate the
most prevalent physical factors within a 200-foot radius from the center of the
intersection. Photographic documentation (Google Earth aerials) were also used to
collect variables associated with street design. Table 3-4 summarizes the street
characteristics of the case study intersections that may have an impact on the
occurrence of bicycle crashes.
22
Table 3-1- Selection Criteria for Case Study Intersections (Source: Author using Signal Four Analytics Data)
23
Figure 3. Map of the Spatial Distribution of Pedestrian and Bicycle Crashes in Duval
County, 2011-2014 (Source: Author using Signal Four Analytics Data)
24
Figure 3-1- Location of all Case Study Intersections. A: City wide locations. B: Close-up
of case study intersections
25
Figure 3-2. Exposure Index Versus # of Crashes
0
5
10
15
20
25
Bike/Ped_Crashes
Exposure Index
26
Figure 3-3. Powers Avenue and University Boulevard. A: Map area showing
intersection. B: Aerial of Powers Ave and University Blvd. W Intersection
27
Figure 3-4. Crashes at Powers-University Blvd Intersection
28
Figure 3-5. Blanding Boulevard and Collins Road. A: Map area showing intersection. B:
Aerial of Blanding Blvd and Collins Rd Intersection
29
Figure 3-6. Crashes at Blanding-Collins Road Intersection
30
Figure 3-7. Tampico Road and 103rd Street. A: Map showing location. B: Aerial of
Tampico Rd and 103rd St Intersection
31
Figure 3-8- Crashes at Tampico-103rd Street Intersection
32
Figure 3-9. Beach Boulevard and Countryside Village Drive and Desalvo Road. A: Map showing location. B: Aerial of Beach Blvd and Desalvo Rd and Countryside Village D
33
Figure 3-10. Crashes at Beach-Desalvo Road Intersection
34
Figure 3-11. Beach Boulevard and University Boulevard South. A: Map showing location. B: Aerial of Beach Blvd and University Blvd W
35
Figure 3-12. Crashes at Beach-University Blvd Intersection
36
Figure 3-13. Ricker Road and 103rd Street. A: Map showing location. B: Aerial of
Ricker Rd and 103rd St Intersection
37
Figure 3-14. Crashes at Ricker-103rd Street Intersection
38
Figure 3-15. Century 21 Drive and Atlantic Boulevard and Acme Street. A: Map
showing location. B: Aerial of Century 21 Dr and Atlantic Blvd Intersection
39
Figure 3-16. Crashes at Atlantic-Acme Street Intersection
40
Figure 3-17. Timuquana Road and Seaboard Avenue. A: Map showing location. B:
Aerial of Timuquana Rd and Seaboard Ave Intersection.
41
Figure 3-18. Crashes at Seaboard-Timuquana Road Intersection
42
Figure 3-19. Atlantic Boulevard and Leon Road. A: Map showing location. B: Aerial of Atlantic Blvd and Leon Rd Intersection.
43
Figure 3-20. Crashes at Atlantic-Leon Road Intersection
44
Figure 3-21. 103rd Street and Firestone Road. A: Map showing location. B: Aerial of 103rd St and Firestone Rd Intersection.
45
Figure 3-22. Crashes at Firestone-103rd Street Intersection
46
A
B
Figure 3-23. Catoma Street and Timuquana Road. A: Map showing location. B: Aerial of Catoma St and Timuquana Rd Intersection.
47
Figure 3-24. Catoma Street -Timuquana Road Intersection
48
A
B
Figure 3-25. San Jose and Loretto Road. A: Map showing location. B: Aerial of San
Jose and Loretto Road Intersection.
49
Figure 3-26. San Jose-Loretto Road Intersection
50
A
B Figure 3-27. Mayport Road and Assisi Lane . A: Map showing location. B: Aerial of Mayport Road and Assisi Lane Intersection.
51
Figure 3-28. Mayport Rd -Assisi Ln Intersection
52
A B Figure 3-29 Beaver Street West and North Laura Street . A: Map showing location. B: Aerial of Beaver Street West and North Laura Street Intersection.
53
Figure 3-30. Beaver St W- N Laura St Intersection
54
Table 3-2 Land Use Characteristics of Intersections
55
Table 3-3. Estimate of Case Study Intersection Neighborhoods Age group (18 and over or 18 and younger) (Source: Census, 2010)
Intersection Under 18 Years
18 Years and Over
POWERS AVE & UNIVERSITY BLVD W 1,229 4,097
BLANDING BLVD & COLLINS RD 1,161 3,904
TAMPICO RD & 103RD ST 1,345 4,907
BEACH BLVD & COUNTRYSIDE VILLAGE DR & DESALVO RD 1,950 5,483
BEACH BLVD & UNIVERSITY BLVD S 846 3,381
RICKER RD & 103RD ST 2,324 5,046
CENTURY TWENTY ONE DR & ATLANTIC BLVD & ACME ST 756 2,258
TIMUQUANA RD & SEABOARD AVE 576 2,170
ATLANTIC BLVD & LEON RD 1,519 3,356
103RD ST & FIRESTONE RD 1,975 4,979
SAN JOSE BLVD & LORETTO RD 2,462 6,426
MAYPORT RD & ASSISI LN 681 2,309
CATOMA ST & TIMUQUANA RD 299 931
BEAVER ST W & N LAURA ST 344 1,940
City of Jacksonville 203,455 642,263
56
Table 3-4. Intersection Characteristics
Intersectionname
POWERSAVE&
UNIVERSITYBLVD
W
BLANDINGBLVD&
COLLINSRD
TAMPICORD
&103RDST
BEACHBLVD&
COUNTRYSIDE
VILLAGEDR&
DESALVORD
BEACHBLVD&
UNIVERSITYBLVDS
NumberofLanes 5 4 5and2 6and2 5
Intersectiontype 4way 4way 3way 4way 4way
Median No Yes 2narrow Yes No
MarkedCrosswalk Halfvisible Yes Halfmarked Halfmarked Halfvisible
TrafficLight Yes Yes Yes Yes Yes
Sidewalk On3sides Yes Yes Yes Yes
Intersectionname
RICKERRD&
103RDST
CENTURYTWENTY
ONEDR&ATLANTIC
BLVD&ACMEST
TIMUQUANA
RD&
SEABOARD
AVE
ATLANTICBLVD&
LEONRD
103RDST&
FIRESTONERD
NumberofLanes 5 6and2 5and2 6and2 6
Intersectiontype 4way 4way 4way 3way 4way
Median 2narrow No No No 2narrow
MarkedCrosswalk Yes 3/4marked Halfvisible 2/3marked Halfvisisble
TrafficLight Yes Yes Yes Yes Yes
Sidewalk Yes Yes Yes Yes Yes-on1side
57
Table 3-5. Duval County Planning Districts
Planning District Population (2010)
1 35,778
2 191,744
3 237,139
4 155,850
5 127,542
6 73,731 Duval County Planning Districts, Source City of Jacksonville
58
Table 3-6. Income-to-Poverty Ratios for Census Tracts surrounding Intersections with the Highest Number of Pedestrian and Bicycle Crashes in Duval County, 2011-2014.
Intersection Under 51 %
50 to 99 %
Sum of 1st 2 Columns
100 to 125 %
125 to 149 %
150 to 185 %
200 % and over
ATLANTIC BLVD & LEON RD 34% 7% 41% 25% 7% 4% 14%
BLANDING BLVD & COLLINS RD 32% 14% 46% 6% 5% 4% 5%
TIMUQUANA RD & SEABOARD AVE 28% 19% 47% 18% 2% 5% 1%
CENTURY TWENTY ONE DR & ATLANTIC BLVD & ACME ST 27% 18% 45% 21% 10% 2% 5%
103RD ST & FIRESTONE RD 27% 20% 47% 14% 5% 5% 3%
BEACH BLVD & UNIVERSITY BLVD S 25% 9% 34% 11% 2% 5% 2%
BEACH BLVD & COUNTRYSIDE VILLAGE DR & DESALVO RD 24% 0% 24% 10% 2% 12% 8%
RICKER RD & 103RD ST 21% 20% 41% 8% 6% 7% 37%
POWERS AVE & UNIVERSITY BLVD W 21% 8% 29% 17% 5% 12% 1%
TAMPICO RD & 103RD ST 21% 8% 29% 18% 4% 6% 2%
SAN JOSE BLVD & LORETTO RD 0% 0% 0% 1% 1% 95% 3%
MAYPORT RD & ASSISI LN 27% 12% 39% 4% 2% 2% 1%
CATOMA ST & TIMUQUANA RD 27% 18% 45% 14% 4% 2% 1%
BEAVER ST W & N LAURA ST 48% 40% 88% 4% 2% 0% 1%
59
Findings
The built characteristics (see Table 3-2 above), the social factors of each
intersection, and the relatively low number of bicycle-pedestrian crashes from 2011 to
2014, demonstrates the commuting corridor of the case study intersection
neighborhoods and that they occur in transit routes. Daily destinations or travel
generators such as work places, school, retail stores or simply trying to access transit,
are important triggers to travel behavior. A pattern found in almost each case study
intersection was that each had a common destination of retail (i.e. pharmacy) or access
to transit. This can result in increased exposure of bicyclist and pedestrians to traffic
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and crashes. The concentration of retail activity and housing also shows a significant
association between bicycle-pedestrian crashes.
Also, all case study intersections accommodated: three and four-way traffic,
consisted of traffic lights, marked crosswalks, and were considered a major roadway.
However, approximately half of the intersections studied had either half-marked of half-
visible crosswalks. The “half-visible” crosswalks indicate that the crosswalk markings
need restriping. Four out of the six half-visible and half-marked crosswalks ranked the
highest for crashes. Variations were observed in some fieldwork analysis (table 3-4).
Sidewalks were also available on at least one side of the road for each intersection.
Looking closer at the case study, the intersections with the highest crashes
occurred in Jacksonville’s Planning District (PD) 4, 3, and 2, of which PD 3 and 2 are
the most populous with PD 4 coming in 3rd (Table 3-5). The intersection with the most
crashes, Powers-University Boulevard West is located in Planning District 3 which is the
city’s most populous planning district (2010 Census, Jax GIS) with 237,139 people.
However, this intersection’s neighborhood only represents 6.8 percent of the planning
district’s population. On the other hand, the Blanding-Collins Road intersection’s
neighborhood represents 17% of its planning district’s (PD 4) population.
The results of this study indicate that the case study intersections are in
impoverished neighborhoods containing families and individuals with an income-to-
poverty ratio less than 100 percent and are therefore considered in poverty (IWR,
2016). Table 3-6 below shows the percentage of people by specified income-to-poverty
ratios in the neighborhoods for the case study intersections in 2014 due to it being the
latter of the 3 study years. Moreover, the table shows that among the case study
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intersections, the Atlantic and Leon intersection (34 percent) had the highest proportion
of people with income-to-poverty ratios lower than 51 percent. This intersection is
located in a primarily black neighborhood with a majority of the population 18 years and
over. However, the Atlantic and Leon intersection ranked second to last within the 10
intersections for the highest number of bicyclist and pedestrian crashes.
The leading high-crash intersection (Powers and University Boulevard West) was
found to be located in one of the lowest proportion of income-to-poverty neighborhoods
at only 21 percent. This intersection is located in a mostly minority (see figure 3-31)
neighborhood with over 76 percent of its population over 18 years old. The third highest-
crash intersection Tampico and 103rd, was also located in one of the lowest proportion
of poverty neighborhoods out of the case studies. This intersection is in a primarily white
neighborhood with 78 percent of its population over 18 years old. The mean ratio of
income-to poverty below 51% for the 10 case study intersections was 26 percent. The
second most impoverished intersection was found at the Blanding and Collins
intersection (32 percent). This intersection is also second for most crashes at
intersections, and is located in a primarily white neighborhood. Each intersection
neighborhood had a higher poverty rate than the City as a whole. The intersection with
the largest population under 18 years old was Ricker and 103rd which ranked 6th in
number of bicycle and pedestrian crashes.
Complicating the findings, age did not seem to indicate a positive relationship
with bicycle and pedestrian crashes. This could be attributed to obscured local
demographic factors and not enough detail on age ranges. Table 3-7 identifies the
percentage of population under and over 18 years of age. The intersections in
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neighborhoods with a majority of the population over 18 years old were not the top high-
crash intersections, with the exception of Tampico and 103rd, which had the third
highest percentage of population over 18. The only high-crash intersection with a
significantly higher poverty rate than those of the other neighborhoods studied was
Blanding and Collins. Of the intersections in neighborhoods with the highest percentage
of income to poverty (Atlantic -Leon and Timuquana -Seaboard included), the Blanding
and Collins intersection was the only high-crash intersection in comparison to the other
neighborhoods studied. Regardless, a vast majority of the case study neighborhoods
had significantly higher proportions of minority populations than the City average.
Four intersections from the case study were in close proximity to mobile home
parks: Blanding-Collins, Beach-Countryside and Desalvo, Century 21 Drive and Atlantic,
and the Atlantic-Leon. Each high-crash intersection was located in a commercial area
with a multitude of open-front commercial retail, such as fast food restaurants or
neighborhood convenience stores/pharmacies. Other prominent elements include the
proximity to multi-family/medium density housing surrounded by commercial uses, and
bus stops very close to the intersection. At six of the fourteen sites, bus stops were less
than 100 feet from the intersection. This may present visual impairments when trying to
cross at the crosswalk and indicate a trend in commuter patterns at the intersections.
Furthermore, the majority of crashes occurred between 1 P.M.-5 P.M. and more
than double the number of crashes occurred at the intersection versus mid-block (see
Table 3-8). The overall trend did seem to show an indication of increased crashes in
impoverished areas: however, there was a possibility of an over representation of low
income individuals. In 2014, the poverty rate in Duval County was 18.4% with 36.9% of
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its residents making less than twice the poverty rate in Duval County. The median
household income that same year was $45,778, so a family of four was considered in
poverty if the income was $24,008 or less a year.
Figure 3-32 visually shows a correlation between the distribution of bicycle and
pedestrian crashes from 2011-2014 and the percentage of the population that falls into
low income. A person is considered to be of low income if he or she is a member of a
household whose income would qualify as “very low income” under the Section 8
Housing Assistants Payments Program. Low income represents 50% of area median
income, while moderate income is generally tied to 80 percent of area median. Hot
spots were found in the Urban Core, West side, and North side of Jacksonville, abutting
the Urban Core. These locations also happen to have large low income and minority
populations.
Local median household income (MHI) represented over the case study
intersection neighborhoods demonstrated that some intersections were in fact located in
areas (block groups) with local MHI less than or equal to 30% of area median income.
With 36.9 percent of the city’s population making less than twice the poverty rate, the
case study intersections represent 22 percent of the city’s population. Furthermore, a
little over one third (approximately 318,913 people) of Duval County’s population is
considered poor and over half of the crashes occurred in low income boundaries (see
figure 3-33).
In order to determine if there was a bias in the population, a countywide analysis
(total of 104 bicycle-pedestrian crashes) that focused on the number of crashes and
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income was performed, and indicated an unequal clustering of crashes (from 2011-
2014) in low income neighborhoods.
Arguably, a regression model was not used to further explore the relationship
between the spatial distribution of bicycle and pedestrian crashes and demographic,
economic and land use characteristics due to an over- representation of low income
population. The over-representation of lower income population was also noticeable
when the standard deviation for the countywide data was well above the mean. Despite
over-representation of low income population, many poor neighborhoods are at a higher
risk for bicycle-pedestrian crashes.
Limitation of the Study
Additionally, due to limitations of the project, as well a lack of accessible data
and available resources including traffic volume and road density, it was difficult to
normalize crashes and run correlations. A lack of high quality data that documents
bicycle and pedestrian trips to estimate exposure and crash risk presented constraints
in this project as well as incomplete time of day and crash location data. It is important
to note that some intersections are located on the boundary between two Census block
groups. As such, it can be difficult to allocate these crashes to a specific block group. It
should be noted that this research used bicycle and pedestrian crash data regardless of
the severity of the crashes.
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Figure 3-31. Population Estimates for Intersection Neighborhoods
Intersection Name 5am-noon
1-5pm
6PM-12AM
1AM-4AM
Intersection crossing
Mid-block
POWERS AVE & UNIVERSITY BLVD W 3 4 3 9 1
BLANDING BLVD & COLLINS RD 1 4 3 7 1
TAMPICO RD & 103RD ST 5 2 2 5
BEACH BLVD & COUNTRYSIDE VILLAGE DR & DESALVO RD
1 1 2 3 1
BEACH BLVD & UNIVERSITY BLVD S 1 2 1 2 2
RICKER RD & 103RD ST 2 5 3 4
CENTURY TWENTY ONE DR & ATLANTIC BLVD & ACME ST
4 3 1
TIMUQUANA RD & SEABOARD AVE 3 2 1 5 1
ATLANTIC BLVD & LEON RD 1 1 1 1
103RD ST & FIRESTONE RD 3 3
San Jose Blvd & Loretto Rd 1 3 1 5
Mayport Rd and Assisi Ln 4 1 4 1
Catoma St & Timuquana Rd 1 3
1 3 2
Beaver St and Laura St 3 1 1 4 1
Total 17 30 25 2 45 21
Table 3-8. Time of day and Location of Crashes
0 5,000 10,000 15,000 20,000 25,000 30,000
BLANDING BLVD & COLLINS RD
TIMUQUANA RD & SEABOARD AVE
RICKER RD & 103RD ST
BEACH BLVD & UNIVERSITY BLVD S
TAMPICO RD & 103RD ST
BEACH BLVD & COUNTRYSIDE VILLAGE DR & DESALVO RD
CENTURY TWENTY ONE DR & ATLANTIC BLVD & ACME ST
POWERS AVE & UNIVERSITY BLVD W
ATLANTIC BLVD & LEON RD
103RD ST & FIRESTONE RD
SAN JOSE BLVD & LORETTO
CATOMA& TIMUQUANA
MAYPORT RD & ASSISI LN
BEAVER ST & LAURA ST N
2011 Total Population Estimates of Intersection Neighborhoods
Native haw Asian Am In Black White Total population
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Figure 3-32. Bicycle-Pedestrian Crashes in Low to Mod Income Boundaries
67
Figure 3-33. Countywide Analysis with relation to percent of median income as a percentage of total population
39%-44% 45%-50% 51%-57% 58%-64% 65%-78%
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CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
The results of this study show that representation was not equal but supports the
assumption that pedestrian and bicyclist crashes are more likely to occur in low income,
minority neighborhoods. Therefore, this study does not prove that bicycle-pedestrian
crashes occur in low income neighborhoods with minority populations, but it does
suggest that there is a potential for considering these predictor variables in evaluating
the risk associated to these types of crashes. To make neighborhoods safer for walking
and riding, the following recommendations based on the League of American Bicyclist’s
5 E’s – engineering, education, enforcement, encouragement, and evaluation (2015) of
safety are suggested.
Engineering
It is important for proper infrastructure to be in place in order to welcome and
support walking and bicycling. Bike lanes, connected streets/neighborhoods, shared
use trails, and policies in place for regular maintenance of these services is a key for a
safer physical environment. These traffic engineering countermeasures would likely
involve modifications that separated pedestrians/bicyclists from vehicles by adding
additional time or sidewalk or bike lane space, increasing visibility and presence of
pedestrians and bicyclists or reducing vehicle speeds where possible.
For example, the 103rd Street and Firestone Road intersection does not have sidewalks
on both sides of Firestone Road. Considering a school is located south of that
intersection, more sidewalks could reduce pedestrian crashes and decrease walking
along roadways. At intersections like Powers Avenue and University, reducing the
number of driveways and curb cut outs allowed at the intersection would also promote
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connectivity. Or in areas such downtown and the Laura-Beaver Street intersection,
buffered bike lanes and priority shared lane markings would be appropriate A Context
Sensitive Streets Committee was established in Jacksonville in 2016 and will finalize at
the end of April 2017. This committee was responsible for reviewing and revising
construction and city policies. A committee similar to this could be established to meet
annually or bi-annually to review any amendments to these policies and make sure it
aligns with bicycling and pedestrian safety.
Education
Pedestrian and bicyclist safety education is vital in order for people to gain the
skills needed to properly travel within the community. Motorists also should learn their
responsibilities behind the wheel and be consider the rights of the pedestrians and
bicyclists. A public campaign over social media and outlets such as the news, mailers,
and commercials would be successful in promoting the message. Training programs for
children and adults about safe travel walking and biking is another policy action that
could mitigate the effects of traffic.
Enforcement
Jaywalking operations (warnings and citations) for example, is an active way of
enforcing laws to increase safety and hold pedestrians accountable. If crashes are
occurring in areas within a certain distance of a crosswalk, crosswalk enforcement
zones could be implemented in neighborhoods that are at higher risk for pedestrian and
bicyclist crashes. This would help ensure that the road is safe for all users. Adequate
notice to the community for these pedestrian safety operations should take place. Any
jaywalking enforcement related to traffic safety should be data driven in order to reach
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the most vulnerable road users. Furthermore, as an alternative to paying the fine and
citation, the City of Jacksonville could offer a pedestrian safety course to offenders.
Encouragement
Community groups such as pedestrian and bicyclist advocacy groups play a
major role in encouraging people to walk or use a bike. Working with the Public Works
Department Planning and Development Department, City Council, and the Florida
Department of Health in Duval, community bike rides or walking challenges should be
conducted in order to raise awareness and inspire people to get out and try these
activities in a safe manner. These partnerships could offer walking maps with sidewalk
and bike lane connectivity and tracking tools including forms and logs in order to keep
increasing walkability and activity in the community. Additionally, keeping an interactive
campaign such as the Mayor’s Journey to One which is a citywide health initiative to
strengthen community through walking, would keep the public involved and be another
tool in receiving input for better policies.
Evaluation
Beach Boulevard, San Jose Boulevard, University Boulevard, Blanding
Boulevard, 103rd Street, Atlantic Boulevard, Timuquana Road, Mayport Road, and
Beaver Street are maintained by the FDOT. The City and Florida Department of
Transportation (FDOT) should conduct audits at high risk /hot spot intersections to help
identify if redesign, traffic mitigation, and bicycle-pedestrian -driver education should be
increased. To improve sidewalks and crosswalks throughout the city, neighborhoods
should receive an extensive walking audit based on priority neighborhoods. A
systematic approach that is concentrated and focused on all maintenance needs of
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these high crash neighborhoods versus a piecemeal approach is recommended. It’s
necessary to check for obstructions or hindrances to sidewalks, including checking the
width of bike lanes and sidewalk which will also help identify the roles of the various
agencies that will play in making improvements. Mid-block crossings at long
intersections should also be explored.
As previously mentioned, a more comprehensive approach with a substantial
risk exposure index rate would display a stronger relationship with the given data. A
fuller evaluation of the results using more advanced statistical techniques, levels of use
of walking and bicycling and street network mileage is recommended. Once there is
significant data on the number of pedestrians and bicyclists that use a sidewalk, bicycle
lane, or crosswalk, pedestrian and bicyclist volume data can be used to account for
exposure at specific locations. Additionally, keeping track of this volume will document
changes in volumes once improvements are made in order to see if the course of action
taken reduced the number of crashes. Estimating pedestrian and bicyclist data in areas
that are not high priority will help reduce the cost associated to conducting bicyclist and
pedestrian counts.
Additional Recommendations
In like manner, the City of Jacksonville is currently drafting a Bicycle-Pedestrian
Master Plan in order to implement bicycling and walking as a viable and safe
transportation option. Knowing the demographic make-up of these communities more
prone to these types of crashes, the City could customize crosswalk timing and signage
to be bilingual or extended depending on the needs of the neighborhood. This would
72
enhance safety for all age levels and groups. Traffic regulation and congestion as
factors of traffic volume should also be explored.
As part of improving safety, community groups connected to these affected
neighborhoods could also reach out to their council district representative for bike and
pedestrian facility improvements to be included in their district’s Capital Improvement
Plan budget. Each council member is allowed to submit priority projects as it fits into the
fixed budget. It is also vital that the City of Jacksonville in partnership with the FDOT
receive input from variety of stakeholders as learning what the public values to align
implementation related to biking and walking.
Moreover, the Beach-Countryside, Ricker-103rd, Century 21-Atlantic,
Timuquana-Seaboard, Atlantic –Leon, 103rd-Firestone and Mayport-Assisi intersections
are all located within 100 feet of a bus stop. The board of the local transportation
authority should consider increasing the distance between an intersection and bus stop
as a matter of policy as it may be effective in reducing crashes. Raised medians at
intersections with more than six lanes such as Beach Boulevard-Countryside, Century
21-Atlantic, San Jose-Lorretto and Catoma-Timuquana could also be effective in
reducing the number of crashes associated to mid-block crossing. However, based on
the research in this study, it is not possible to draw conclusions about bus stops or
number of lanes and their role in bicycle/pedestrian safety.
Future research should account for factors such as the severity,
pedestrian/bicyclist counts, total number of crashes in every neighborhood, and street
segments as it would provide a more comprehensive view and better opportunity to
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prevent and mitigate crashes. Studying the adjacencies next to these intersections
could reveal a stronger relationship to poverty and proximity to job markets.
Lastly, consistent with the study results, crashes tend to occur in areas of high
commercial/residential concentrations in close proximity to bus stops. Multiple
driveways in the nearby commercial corridors are potentially interrupting bicyclists and
pedestrians on sidewalks. A more complete context sensitive street design should be
used in order to separate bicyclists and pedestrians from vehicular traffic.
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