1. Measuring and Communicating the Impact of the Safety Program
Virginia’s Highway Safety Target Setting Methodology
Shan Di, Ph.D., PE, Virginia DOT, On Behalf Of Stephen Read, PE, Virginia DOT
August 2019

2. 2019 Virginia Traffic Fatality Target
The Federal Highway Administration (FHWA) established the Safety Performance Management Regulation (Safety PM) to support the Highway Safety Improvement Program (HSIP).
Furthermore, the Safety PM Final Rule requires State Departments of Transportation (DOTs) to establish and report their safety targets. The FHWA does not identify a specific methodology to use when establishing safety targets; States have flexibility to use a data-driven process, but annual targets are established based on measures using a five-year rolling average.
Virginia is pursuing the development of a more robust safety target setting methodology. VDOT advanced a plan to develop the methods and refine the 2019 targets using four tasks described in a white paper titled “Safety Performance and Target Setting” divided between two consultants. The VHB efforts were divided into the following five Tasks…
2019 tragets were set based on traditional method of using trend lines
Tested model prediction with available data vs actual in 2018 and 2019 target
Virginia Department of Transportation

3. 2019 Virginia Traffic Serious Injury Target
Virginia Department of Transportation

4. Safety Performance Management – Refining Target Setting
Five steps of new Virginia target setting method:
Determine crash factors and causes – behavioral, infrastructure and the interaction
Determine degree of infrastructure improvement’s influence on behavioral crashes
Analyze external factors to predict 2020 baseline severe crash safety measure counts
Evaluate anticipated benefits of recent infrastructure projects
Combine the baseline predictions with project benefits to establish data-driven targets
Virginia Department of Transportation

5. Step 3: Analyze External Factors
The development of the predictive model includes three steps:
Statistical model development and calibration to establish baseline conditions
Model validation (2018 data)
Forecast external risk factors for future measure predictions
Based on NCHRP (Wunderlick TTI and UMichiganTRI)
Used 2009 to 17 data to build model and 2018 to validate the model
The model helps to identify the external risk factors that most impact fatalities, serious injuries, and bike/ped
Exposure is your unit of travel mostly impacting the measure
External Risk Factors indicate the likelihood of measure (e.g., fatality) per unit of travel
Some factors may decrease the measure while most will increase the measure with increasing the risk.
Construction districts were used rather than statewide data to increase the sample size (in terms of observations) for model development, and to identify underlying characteristics that differ across construction districts (for example, underlying trends in fatalities in the Bristol area are likely different than Northern Virginia). Monthly observations were used to both increase sample size and to identify seasonal and/or monthly-related trends.
VHB considered other spatial and temporal aggregation of the safety performance data (e.g., at the city or county level), but these were found to not be as reliable for model estimation as the district-monthly disaggregation.
Aggregation to the district level was a fitting compromise in terms of number of observations, counts within each observation, monthly trends, and number of variables included at the maximum level of disaggregation in the model.
Virginia Department of Transportation

6. Baseline Safety Prediction Models
Assessed models for Fatalities and Serious Injuries using the following external factors
VMT
Urban and Rural VMT
Drivers and Titled Vehicles
Annual Licensed Drivers
District Annual Age of Titled Vehicles
Weather
Average Precipitation
Average Snowfall
Average Temperature
Social Economic Data
District Annual Total Population
Annual Labor Force
Annual Unemployed
Median Household Income
Statewide Annual GDP
Statewide Annual Alcohol consumption
Liquor Licenses by Type
First iteration of model development
Also looked for bike and ped models
There was not enough vehicle age data to include in the models, but over time this variable has promise for inclusion
We have found some good information regarding behavioral program and projects at the local level that may be considered in our District based models in the future.
We will investigate additional data to refine the models (variability) for future year (2020 plus) baseline predictions
Virginia Department of Transportation

7. Additional External Factors Tested
Assessed refined models for Fatalities and Serious Injuries, adding combinations of additional factors:
ABC Stores – Gallons Sold
Average Gas Price
VDOT Infrastructure Spending
DMV HSO Behavioral Programs Spending
2019 dollars
Gallons sold is by District, Alcohol consumption was statewide
We now have good information regarding behavioral program and projects at the local level that was considered in the District based models. To have similar time periods for all data sets we reduced the analysis period to
Of the additional factors, both VDOT and DMV spending were found to have significant effect in explaining the safety measures
Virginia Department of Transportation

8. VDOT Infrastructure Spending
Capital Projects by Category
Highway Safety; Traffic Operations
New Construction; Roadway and Bridge Reconstruction w/ Capacity or Lighting
Roadway and Bridge Maintenance or Removal
Enhancement/ TAP
Operations & Maintenance Spending by Category
Emergency and Incident Management (with and without snow removal)
Routine Maintenance
Roadway Paving
Traffic and Safety
Facility and Other
2019 Dollars
Also assessed variables for bike and ped models
There was not enough vehicle age data to include in the models, but over time this variable has promise for inclusion
We have found some good information regarding behavioral program and projects at the local level that may be considered in future District based models. It was not available in time to be incorporated into the first model development.
Constrained by available data over similar time periods.
We will investigate additional data to refine the models (variability) for future year (2020 plus) baseline predictions
TAP: transp. Alternative program – sidewalk, bikelanes.
Routine Maintenance – fixing ditches, culverts, mowing and cleaning
Virginia Department of Transportation

9. DMV Highway Safety Office Spending
HSO Behavioral Programs
Impaired Drivers (Alcohol)
Occupant Protection
Speed Control
Pedestrian and Bicyclist
Young Drivers
Training and Education
Statewide (Proportional)*
All Behavioral Programs (Total)
* Includes statewide spending applied proportionally by annual population in each District.
2019 Dollars
Took 10 years of HSO grant spending at the jurisdictional levele and summed to the VDOT district
At jurisdictional level we found some trends but not strong correlation for each program so we included in the model.
Virginia Department of Transportation

10. Findings from Initial Fatality Model
Increases fatal crashes -
District VMT growth
Increasing local functional class percent of VMT
Increasing young population (15-24)
Increasing aging population (75 plus)
Decreases fatal crashes-
Increased Highway 3R Spending
Increased Emergency/Incident Management Spending
Increased Total Behavioral Program Spending
Mixed-effect NB model
The mixed-effects negative binomial model is a count model that accounts for shared unobserved correlations within the district observations.
E&IM is mostlly on IS routes – compare trends on IS
About half of our recent increases in fatalities has been pedestrians.
Virginia Department of Transportation

11. Findings from Serious Injury Model
Increases serious injury crashes -
District VMT growth
Increasing local functional class percent of VMT
Increasing aging population (75 plus)
Decreases serious injury crashes-
Increased Roadway Maintenance Spending
Increasing Average Snowfall in Month
Virginia Department of Transportation

12. Findings from Bike/Ped Model
Increases severe crashes -
District VMT growth
Increasing local functional class percent of VMT
Increasing young population (15-24 years)
Liquor Licenses
Decreases severe crashes-
Increasing Roadway Construction Spending
Increasing rural functional class percent of VMT
Increasing Non-motorized Behavioral Program
Increasing gas price
Increasing Average Snowfall in Month
Virginia Department of Transportation

13. Baseline Prediction - Fatalities
Compares predicted to observed crashes
2019 prediction – 915 fatalities
2020 prediction – 954 fatalities
Note: trend line and prediction is consistent since all time VA low of 703 fatalities in 2014
Note: when the model included 603 highway expansion spending, there was a better fit with the data but 2020 predictions were higher (7626 vs 7520) . So the highway spending was taken out since we are including the expected benefit (reductions) for SMART SCALE and HSIP projects
Virginia Department of Transportation

14. Baseline Prediction - Serious Injuries
Compares predicted to observed crashes
2019 prediction – 7,575 serious injuries
2020 prediction – 7,520 serious injuries
Virginia Department of Transportation

15. Baseline Prediction - Bike/Ped
Compares predicted to observed crashes
2019 prediction –
696 fatalities and serious injuries
2020 prediction –
714 fatalities and serious injuries
Virginia Department of Transportation

16. Step 5: Results – 2020 Data-Driven Targets
Combines the baseline predictions with the expected project benefits to establish data-driven targets
Virginia Department of Transportation

17. Questions?
Mark A. Cole, PE Stephen Read, PE State Highway Safety Engineer Highway Safety Planning Manager (804) (804)
Version 4.0 FY 19 Performance Presentation