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

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

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 ‹#›

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?

Traffic Fatality Counts, by Category

Percent Change in Traffic Fatalities from 2014-2015

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

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

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

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

Mapping the model: Los Angeles Area

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

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)

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

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

Questions? Contact information: Paul Teicher Paul.teicher@dot.gov ‹#›

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

Los Angeles HIN Source: City of LA Vision Zero ‹#›

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 2009-2013 vs. 2010-2014. Note: the two analyses are somewhat different ‹#›