1. Neighborhood Pedestrian Fatality Risk
Office of the Assistant Secretary of Transportation for Policy
Paul D. Teicher, Transportation Analyst

2. Disclaimer:
Preliminary research
Views expressed are my own
Not announcing new USDOT policy

3. Outline of the Project
Goal: Better understand pedestrian fatality risk through data use and integration
Analysis: Estimate national risk at the neighborhood level
Use: Potential decision support tool
Analysis: Statistical analysis of fatal crashes
Use: risk analysis and resource allocation
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4. Why is this important? What are we trying to understand?
Around 5,000 pedestrians die on our roadways annually, and the number is increasing
What lessons learned can illuminate opportunities and challenges for traffic safety data analysis?
How can this inform policy on roadway safety?
How can we leverage multiple data sources to better understand pedestrian risk?
What roadway, land use, and socio-economic indicators are associated with pedestrian fatality risk?

5. Traffic Fatality Counts, by Category

6. Percent Change in Traffic Fatalities from 2014-2015

7. The Approach CHOOSE level of analysis Census Tract, 2010-2014
AGGREGATE data to Census Tract level
Lat/Long values
Shapefiles
COMBINE datasets
via GIS, to produce a Census Tract level database

8. Datasets HPMS (FHWA, Highway Performance Monitoring System)
Level of Analysis:
Census Tract
HPMS (FHWA, Highway Performance Monitoring System)
Vehicle Miles Traveled
Functional classification
U.S. Census data (American Community Survey and Longitudinal Employer-Household Dynamics survey)
Socio-economic indicators
FARS (NHTSA, Fatality Analysis Reporting System)
Pedestrian fatalities
Smart Location (EPA data)
Intersection density
Auto vs. multi-modal intersections
LODES data = LEHD Origin-Destination Employment Statistics
We used the ACS data and LODES commuter flow data to generate Census-tract average daily populations, which was used as the model offset (adjust for exposure)
We also derived the actiivty mix index (capturing land-use diversity) from the LODES and ACS data
Finally, we should note that we used HPMS VMTto develop national traffic density measures--not just VMT

9. Count of Census Tracts with Pedestrian Fatalities, 2010-2014
Zero fatality tracts account for 77% of the total 73,056 tracts

10. Statistical Modeling Info
Zero-inflated negative binomial model
Dependent variable: pedestrian fatalities per 100,000 people
Two models – rural and urban
Independent variables:
Traffic Density (Vehicle Miles Traveled per mile2)
Built environment variables (density, diversity, and design)
Population and employment density, and employment mix
Intersection density
Parks and Native American reservations
% who walk to work, and State law variables
Control variables for demographics
ZINB model = a count model
Because the dependent variable needs to be usable in a count model, the normalization was done via an off-set variable. Average daytime population calculated from ACS data and LODES commuter flows
Intersection density = auto and multimodal intersections)
Demographics: household income, race, age, household vehicle access
Misc. place-based variables: Parks, Native American reservations, State laws, and regional fixed effects

11. Mapping the model: Los Angeles Area

12. Impact of Roadway VMT, by Type
FARS shows that the highest percentage of pedestrian fatalities are consistently on urban arterials (34% of all fatalities between 2010 and 2014)
Roadway Functional Classification
FC1 & FC2: Interstates, expressways, and other freeways
FC3: Primary arterials
FC4: Minor arterials
FC5: Major collectors major collectors

13. What’s the policy application?
Place-based risk identification and quantification
By estimating pedestrian fatalities we can identify higher risk neighborhoods
Understand how a changing neighborhood may increase/decrease the risk profile
Potential decision support tool (with some more tweaking)

14. Policy Observations
This prospective risk-based modeling identifies where, but not WHY  that is for safety practitioners to examine
Neighborhood-level analysis can assist in a systemic safety approach
In a world of limited resources, States and locals need to carry out interventions where the risk is; tools like this approach could help

15. Data Observations
Data integration and analysis can inform data-driven policy and decision making
Data quality challenges influenced the analysis
Missing local road data
Unusable/imprecise FAR geospatial coordinates
No serious injury data
Federal level data can only go so far; State data be a valuable supplement

16. Questions?
Contact information: Paul Teicher
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17. This model vs High Injury Networks
High Injury Network (HIN) analysis: identify streets/intersections with higher incidences of serious and fatal injuries
Shows areas of higher risk
Some Vision Zero cities such as Los Angeles have conducted HIN analysis for non-motorized users

18. Los Angeles HIN
Source: City of LA Vision Zero
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19. This model vs. HIN Note: the two analyses are somewhat different
How are the two analyses different: Census tract versus roadway, serious and fatal vs. fatal only, ped/bike/crashes vs. ped only, their timing was vs
Note: the two analyses are somewhat different
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