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

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

2019 Virginia Traffic Serious Injury Target Virginia Department of Transportation

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

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 17-67 (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

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

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 2012-2017. Of the additional factors, both VDOT and DMV spending were found to have significant effect in explaining the safety measures Virginia Department of Transportation

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

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

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

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

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

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

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

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

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

Questions? Mark A. Cole, PE Stephen Read, PE State Highway Safety Engineer Highway Safety Planning Manager Mark.Cole@VDOT.Virginia.gov Stephen.Read@VDOT.Virginia.gov (804)786-4196 (804)786-9094 Version 4.0 FY 19 Performance Presentation 8.15.18