Contributing Factors for Focus Crash Types and Facility Types Raghavan Srinivasan University of North Carolina Highway Safety Research Center (UNC HSRC) Roya Amjadi Federal Highway Administration (FHWA) Office of Safety Research and Development (R&D)

Presentation Overview Research Team Study Goals Study Objectives Methodology Focus Crash and Facility Types (FCFTs) Potential Nonintersection FCFTs Potential Intersection FCFTs Contributing Factors Findings Countermeasure Selection Process Questions and Discussion

Research Team and Sponsors Taha Saleem (HSRC) Richard Porter (Vanasse Hangen Brustlin, Inc. [VHB]) Raghavan Srinivasan (HSRC) Daniel Carter (HSRC) Scott Himes (VHB) Thanh Le (VHB) Sponsors: Karen Scurry (FHWA) Roya Amjadi (FHWA)

Study Goals To identify focus crash types, focus facility types, and their associated contributing factors to inform applications of systemic safety improvements, and to develop a guidance to assist safety practitioners. Contributing factors are defined as factors that are associated with either increases or decreases to the expected frequencies of crashes or the injury severities resulting from crashes.

Study Objectives Select reliable and applicable data resources, statistical methodologies, analysis procedures, and tools. Conduct data analysis to identify and validate focus crash types and facility types and their associated contributing factors. Identify potential low-cost safety strategies that may effectively be used as systemic safety improvements.

Methodology Fatality Analysis Reporting System (FARS) and Highway Safety Information System (HSIS) databases were used for selecting the FCFTs. Random forest algorithm was adopted to analyze the data and identify the contributing factors for FCFTs.

Identifying FCFTs Data Collected Variables Analyzed FARS: This is a nationwide database. HSIS: Used data from Ohio, California, and Washington. Variables Analyzed Crash type Area type (rural versus urban) Roadway type Location type (intersection versus nonintersection) Intersection type Traffic control types Lighting (day versus night) Road alignment (horizontal curve versus straight/tangent)

Potential FCFTs Fifteen FCFTs were identified (these findings were within the time and resources of this study). Nine nonintersection FCFTs. Six intersection FCFTs. Process used for identifying and selecting potential FCFTs was consistent with the process described in FHWA’s Systemic Safety Project Selection Tool.

Potential Nonintersection FCFTs Run-off-road (ROR) crashes on rural two-lane roads on horizontal curve sections. Run-off-road crashes on rural two-lane roads on straight sections. Lane departure crashes on rural two-lane roads on horizontal curve sections. Lane departure crashes on rural two-lane roads on straight sections. Head-on crashes on rural two-lane roads on straight sections. Angle crashes on rural two-lane roads on straight sections. Head-on crashes on rural two-lane roads on horizontal curve sections. Rollover/overturn crashes on rural two-lane roads on straight sections. Rollover/overturn crashes on rural two-lane roads on horizontal curve sections.

Potential Intersection FCFTs Angle crashes on rural two-lane roads at four-leg stop- controlled intersections. Angle crashes on urban two-lane roads at four-leg stop- controlled intersections. Angle crashes on urban multilane divided roads at four-leg signalized intersections. Angle crashes on urban multilane undivided roads at four-leg signalized intersections. Angle crashes on rural two-lane roads at three-leg stop- controlled intersections. Angle crashes on rural multilane divided roads at four-leg stop-controlled intersections.

Identifying Contributing Factors Data Include: Roadway data obtained from HSIS: Washington and Ohio (nonintersection data). California and Ohio (intersection data). Climate data obtained from National Oceanic and Atmospheric Administration. Socioeconomic data obtained from the United States Census Bureau.

Identifying Contributing Factors Random forest algorithm was adopted to analyze the contributing factors. Percentage increased in mean squared error (MSE) when removing a variable from the random forest model; this is commonly reported using random forest plots. Random forest for per mi crash frequency of run-off-road (ROR) crashes on Washington rural two-lane roads on horizontal curve sections during daytime Source: FHWA

Identifying Contributing Factors Random forest plots do not directly indicate if the presence of a variable corresponds to an increase or a decrease in expected crash frequencies. Random forest plots combined with the predicted crash frequencies (as a function of the variables) provided the information needed to identify contributing factors. Plot of the random forest predicted angle (ANG) crash frequency versus mainline AADT for 3-leg stop-controlled intersections on rural two-lane roads in California The plot shows mainline AADT as a contributing factor in this case where increases in the mainline AADT is associated with increases in the predicted crash frequency (i.e. the positive slope of the linear best-fit line indicating that an increase in mainline AADT is associated with more ANG crashes at 3-leg stop-controlled intersections on rural two-lane roads) Source: FHWA

Findings Roadway factors associated with higher crash frequencies included: Larger average daily traffic volumes. Steeper vertical grades. Sharper curve radii. Narrower lane and shoulder widths. Unpaved shoulders or no shoulders. Mountainous terrain. Higher speed limits. Wider crossing distances at intersections (captured by lane and median widths on approaches). Absence of left- and right-turn channelization at intersections. Findings were generally consistent with the team’s expectations based on previous research and existing practice.

Findings Findings related to the socioeconomic and weather-related factors showed promise. Findings related to socioeconomic variables are likely representing differences in travel behavior, driving behavior, and driving capabilities that seem important for safety analyses. Findings related to weather are likely representing differences in visibility, road conditions, and driver experience and behavior.

Countermeasure Selection Process The final report (in press) also presented a six-step process for identifying and selecting countermeasures: Identify focus crash type. Identify contributing factors for focus crash type. Assemble a list of potential countermeasures that address the crash type. Identify countermeasures that address the roadway factors associated with the focus crash type. Identify countermeasures with crash modification factors. Select countermeasure.

Countermeasure Selection Process The selection of countermeasures must be broad-based and encompass many different options for addressing the crash type. The Quick Reference Guide for Practitioners provides various examples that demonstrate the use of this process as well as proven safety countermeasures to mitigate the presence of common contributing factors.

Questions? Roya Amjadi, 202–493–3383, roya.amjadi@dot.gov Karen Scurry, 202–897–7618, karen.scurry@dot.gov Taha Saleem, 919–962–3409, saleem@hsrc.unc.edu Raghavan Srinivasan, 919–962–7418, srini@hsrc.unc.edu Richard Porter, 919–741–5566, rporter@vhb.com