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10/10/2017
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Center for Urban Transportation Research | University of South Florida
Application of Demographic Analysis to Pedestrian Safety
Pei‐Sung Lin, Ph.D., P.E., PTOE, FITEProgram Director
Achilleas Kourtellis, Ph.D.Senior Research Associate
CUTR, University of South Florida
BDV25 TWO 977‐30
CUTR WebcastOctober 10, 2017
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Introduction• The presentation is based on the BDV25‐977‐30 research project
sponsored by FDOT.
• This project focused on application of demographic analysis to pedestrian safety.
• FDOT project manager:
o Mr. Mark Plass (PM), FDOT District 4 Traffic Operations Engineer
• CUTR research team: o Dr. Pei‐Sung Lin (PI)o Dr. Achilleas Kourtellis (Co‐PI)o Dr. Yu Zhang (Co‐PI)o Dr. Rui Guo (Researcher)o Ms. Elzbieta Bialkowska‐Jelinska (GIS Analyst)
• This CUTR webcast will highlight all aspects of the project, and major conclusions and recommendations.
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Project Background• FDOT invested significantly more resources to enhance pedestrian
safety in Florida.
• There was still a need to effectively and systematically address the experiences of pedestrians in low‐income areas.
• Pedestrian lives were lost at disproportionately higher rates in the nation’s poorer neighborhoods.
• Pedestrian fatality rates in low‐income areas were approximately twice those of more affluent neighborhoods.
• Examining Census tract poverty rates yielded a similar pattern—the country’s poorest neighborhoods have the highest per‐capita pedestrian fatalities.
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Presentation Outline
• Introduction
• Project Background
• Project Objectives
• Research Activities and Findings
• Summary of Research Conclusions
• Recommendations
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Project Objectives
• Develop a demographics‐based methodology
to identify low‐income areas with greater
pedestrian hazard
• Identify major factors associated with
pedestrian crash frequency and injury severity
• Produce recommendations for engineering
countermeasures and pedestrian safety
education/outreach plans
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Pedestrian Fatality Rate and Income Level
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Example: Poverty Distribution and 5-Year Pedestrian Crash Map in Tampa
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Agenda
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Potential Factors for Pedestrian Crashes
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Demographics-based Methodology
Methodological flowchart:
Step 1. Data Collection and Compilation
Step 2. Data Preparation by Analysis Unit
Step 3. GIS Visualization and Spatial Analysis
Step 4. Statistical Tests and Modeling
Step 5. Discussion of Results of Data Analysis
Step 6. Education and Engineering Countermeasures
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Flowchart
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Demographic & Social Factors
(Source: U.S. Census)
Road Environment Factors
(Sources: FDOT RCI
GTFS, Transit Agencies)
Land Use Factors
(Source: FGDL)
Individual Factors
(Source: CARS)
• Population density • Road type • Stores • Pedestrian age, gender
• Age groups & gender • Intersections • Mixed use • Driver age, gender• Ethnic minorities • Sidewalk density • Department stores • Driver action
• Poverty & income • Crosswalk density • Supermarkets • Pedestrian action• Employment • Light condition • Bars • Pedestrian visibility
• Commute mode • Weather condition • Schools • Impairment
• Car ownership • Traffic control device • Industrial area • Impact speed
• Education level • Posted speed limit • Residential area • Vehicles maneuver
• English language fluency
• Traffic volume • Vehicle types• Bus stop locations
Candidate Variables for Methodology Test
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Methodology Test
• Pedestrian crash frequency
• Pedestrian crash injury severity
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Definition of Low-income Area
Based on the definition in Governing (2014) report, census block
groups (BGs) were categorized into low‐income BGs and higher‐
income BGs:
Low‐income BGs: poverty rates >15% or per‐capita income < $21,559
Higher‐income BGs: poverty rates 15% and per‐capita income $21,559
Reference: Governing (2014)‐America’s Poor Neighborhoods Plagued by Pedestrian Deaths
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Identification of Low-income Area
Broward County:
475 low‐income
BGs (out of a total
of 939 census BGs)
were identified
Palm Beach County:
337 low‐income
BGs (out of a total
of 876 census BGs)
were identified
Broward County
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Analysis for Pedestrian Crash Frequency
• Pedestrian Crashes– Pedestrian crash frequency
– Severe injury pedestrian crash frequency
• Explanatory Variables– Demographic and Social Factors
– Road Environment Factors
– Neighborhood Land Use Attributes
– Others
Relationship?
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CO HC NOx PM SOx
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Correlation with Demographic Factorspedestrian crashes are more frequent in the low‐income BGs with more population,
smaller proportion of older people, minority‐dominated, zero‐car ownership
neighborhoods, and among populations with low education level.
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Effects of Demographics on Crash Frequency
o One percent increase in public transit or bike to work, low education level, zero‐car
ownership and minority would result in an average increase of 0.052, 0.047, 0.043 and
0.019 pedestrian crashes in 4 years in a low‐income BG.
o One percent increase in older population would result in an average decrease of
0.055 pedestrian crashes in 4 years in a low‐income BG.
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Correlation with Road Environment Factors
Pedestrian crashes are more frequent in low‐income BGs with more intersections, traffic
signals, bus stops, and larger proportion of roads with higher speed limits.
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Effects of Road Factors on Crash Frequency
o One increase in intersection number, traffic signal number and bus stop location
per mile would result in an average increase of 0.082, 0.655 and 0.170 pedestrian
crashes in 4 years in a low‐income BG.
o One percent increase in proportion of lower‐speed roads would result in an
average decrease of 0.012 pedestrian crashes in 4 years in a low‐income BG.
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Visualization: Neighborhood Land Use Attributes
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Correlation with Land Use AttributesPedestrian crashes occurred more frequently in low‐income BGs with the presence of
Walmart store and with greater densities of discount department stores, fast food
restaurants, convenience stores, grocery stores and barber stores.
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Effects of Land Use Types on Crash Frequency
o On average, the presence of a Walmart store in a low‐income BG would result in an
average increase of 1.803 pedestrian crashes in 4 years.
o One increase in the density (#/square miles) of discount stores, convenience stores,
fast food restaurants, grocery stores and barber shops would result in an average
increase of 0.226, 0.071, 0.069, 0.057 and 0.049 pedestrian crashes in 4 years in a
low‐income BG.
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Analysis for Pedestrian Crash Injury Severity
• Injury Severity– Severe injury(fatality or incapacitating injury)
– Non‐severe injury(no injury, possible injury or non‐incapacitating injury)
• Explanatory Variables– Individual Characteristics– Road Environment Factors– Others
Relationship?
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CO HC NOx PM SOx
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Effects of Individual Characteristics on Injury Severity
o Older pedestrian (11.61%);Pedestrian in travel lane‐ not crosswalk (11.20%);
Dart/dash (4.91%); Impaired pedestrian (70.32%); Aggressive driver (19.64%).
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Impaired pedestrian crashes and locations of bars and alcohol retail
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Effects of Environment Factors on Injury Severity
o Dark‐not lighted (21.56%); Dark‐lighted (18.82%); Bad weather (6.33%).
o Lower speed limit (11.19%); Traffic control device (6.84%).
o Moreover, 72% of pedestrian fatalities occurred at nighttime, and
o 22% of nighttime fatalities occurred on streets without lighting
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• The potential for safety improvements (PSI) is the difference between the expected (or adjusted observed) and predicted number of crashes.
• If the PSI is positive in an area, the area is experiencing more crashes than other areas with similar features.
• All low‐income Block Groups (BGs) can be classified into three zones based on the calculated PSI values—hot, warm, and cold.
• Hot zones are defined as BGs with a top 15% PSI, cold zones refer to BGs with a PSI less than 0, and warm zones are BGs with a PSI between 0 and the top 15%.
• Hot zones are high‐risk BGs for pedestrian safety because there are many more pedestrian crashes than other BGs with similar characteristics. Cold zones are relatively safe for pedestrians.
Identified Hot Zones in Low-income Areas for Improving Pedestrian Safety
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Identified Hot Zones in Low-income Areas for Improving Pedestrian Safety (Cont’d)
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• Roadway Lighting and Lighting Levels
• Treatments at Non‐intersection Locations
• Bus Stop Improvements
• Speed Reduction Treatments
• Road Safety Audits (RSA)
Engineering Countermeasures
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Roadway Lighting and Lighting Levels
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Presence of Lighting
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Adequate lighting level and uniformity
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Increasing illuminance from under 0.9 fc to 0.9 fc or higher will reduce the probability of fatality and serious injury in a nighttime pedestrian/bicycle crash by 8.9%.
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
Total
Ped
estrian/bicycle
Head
‐on
Angle
Others
Rear‐en
d
Sidesw
ipe
3.9%
8.9%
6.9%
5.3%4.4%
2.1%
1.2%
Adequate lighting level and uniformity
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Expected N
ighttim
e Crash fre
quen
cy (per 4‐years)
3.5
4
4.5
5
5.5
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6.5
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Average Horizontal Illuminance (fc)
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Overall
Good Uniformity
Poor Uniformity
Adequate lighting level and uniformity
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Pedestrian lighting placement
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Treatments at Non-intersection Locations
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Treatments at Non-intersection Locations (Cont’d)
a. b.
c. d.
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Bus Stop Improvement
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Bus Stop Relocation
• Inadequate sight distance or sight distance obstruction
• Excessive congestion or conflicts caused by the bus,
• Frequent vehicle conflicts with non‐motorists such as pedestrian crossings. A far‐side bus stop location typically is preferred
Bus stop relocation should be considered if any of the following situation exists
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Transit Bus Request Lights
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Speed Reduction Treatments
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Speed Reduction Treatments (Cont’d)
a. b.
c.d.
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Road Safety Audit
• The identified low‐income areas with higher pedestrian hazards will require an accompanying Road Safety Audit (RSA) report to determine eligibility for safety improvements.
• An RSA is the formal safety performance examination of an existing or future road or intersection by an independent, multidisciplinary team,
• The aim of an RSA is to answer the following questions: (a) What elements of the road may present a safety concern—to what extent, to which road users, and under what circumstances? (b) What opportunities exist to eliminate or mitigate identified safety concerns?
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• WalkWise Safety Education
• Distribution of Education Tip Cards
• Social Media Outreach
• Community Networking
• Business Sweeps
• Law Enforcement Roll‐Call Training
• Public‐Private Partnerships
Education and Outreach Plan
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WalkWise Safety Education
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Distribution of Education Tip Cards
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Distribution of Education Tip Cards (Cont’d)
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Social Media Outreach
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Community Networking
• Attending meetings of Community meetings and events will help build partnerships and connect the safety initiative and message out to the community.
• Often, these partnerships lead to more safety presentations and outreach for an audience that represents more of pedestrian and bicycle crashes.
• Local non‐profit organizations working within the high crash and low‐income areas are good leads to help integrate the WalkWise presentation, as well as other safety information in the community.
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Business Sweeps
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Law Enforcement Roll-Call Training
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Public-Private Partnerships
Example
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• Successfully identified the databases used for demographic analysis to pedestrian safety, developed and tested the
methodological flowchart, obtained major findings, and
recommended implementation strategies for pedestrian safety.
• Developed a demographic‐based methodology (flowchart) to
conduct demographic analysis for including identified inputs, outputs, and outcomes for pedestrian safety analysis.
• Identified major factors associated with pedestrian crash
frequency and injury severity, and quantified their relationships.
• Developed recommendations for both engineering
countermeasures and pedestrian safety education/outreach plans
Summary of Research Conclusions
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Major Engineering Countermeasures o Roadway lighting and lighting levels
• Presence of lighting• Adequate lighting level and uniformityo Proper pedestrian lighting placement
o Treatments at non‐intersection locations• Midblock pedestrian crossing signals (HAWKs, RRFBs)• High‐visibility crosswalk• Medians and crossing islandso Appropriate landscaping
o Bus stop improvements• Bus stop reallocation• Transit stop request lights
o Speed reduction treatments• Slow speed zones• Road diets• Roundabouts• Traffic calming on residential streets
o Road Safety Audits (RSA)
Summary of Research Conclusions
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Major Pedestrian Safety educational/Outreach Plans
o WalkWise safety education
o Distribution of education tip cards
o Social media outreach
o Community networking
o Business sweeps
o Law enforcement role call training
o Public‐private partnerships
Summary of Research Conclusions
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Recommendations
• Identify and prioritize low‐income areas to Implement the research results and findings to improve pedestrian safety.
• Conduct pilot implementations and evaluations.
• Recommend further research projects to investigate (1) land uses and midblock crossings in low‐income areas, and (2) the impact of street lighting levels on pedestrian crashes and injury severity in low income areas.
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Questions?Contact
Dr. Pei‐Sung Lin, P.E., PTOE, FITE
Program Director
ITS, Traffic Operations and Safety
Center for Urban Transportation Research (CUTR)
University of South Florida
(813) 974‐4910
Final Report ‐ Application of Demographic Analysis to Pedestrian Safetyhttp://www.fdot.gov/research/Completed_Proj/Summary_TE/FDOT‐BDV25‐977‐30‐rpt.pdf