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

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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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