SUCCESSFUL PERFORMANCE PAVEMENT PROJECTS 2015 TxAPA Annual Meeting September 23, 2015 Austin District Mike Arellano, P.E. Date

SUCCESSFUL PERFORMANCE PAVEMENT PROJECTS 2015 TxAPA Annual Meeting September 23, 2015 Austin District Mike Arellano, P.E. Date

AUSTIN DISTRICT SAFETY PERFORMANCE OF HIGH- FRICTION MIXTURES Mike Arellano, P.E. 2015 TxAPA Conference

Table of Contents 1 Background of Austin District Pavement Management Program 4-5 2 Genesis of High-Friction Surface Program 6-11 3 High-Friction Pavement Surface Performance 12-20 4 Conclusions & Future Work 21 5 Questions 22 3

Austin District Pavement Management Program Level 1: Objective Pavement Management Program (4/10-year Plan) Level 2: Goals Enhance System Safety Preserve Pavement Infrastructure Condition Maintain Structural Capacity Level 3: Attributes Truck ADT Fatigue Cracking Skid Values %Remaining Service Life Pavement Failures Maintenance Expenditure..etc 4

Pavement Management Goals: Enhance System Safety Goals: Reduction overall crash types and severity Reduction overall fatality rates Objectives: Develop new methods to identify and assess high-risk roadways Identify roadway attributes potentially contributing to crash rates Address high-risk roadways with appropriate preventative measures or improvements Re-evaluate standards and policies 5

Roadway Safety Performance Index (RSPI) Objective #1: Develop a method to identify high-risk locations for potential road safety improvements: Methodology must be: Performance-based Multiple roadway attributes Proactive Reliable Effective Roadway Safety Performance Index (RSPI) 6

Roadway Safety Performance Index (RSPI) Identified Data Sources Geo- CRIS HINI RHiNo PMIS Other sources Data Integration Web-based inquiry Oracle Database Crash Prediction Models Formulation of RSPIs GIS-enhanced system: - Store and manage related data - Publishing maps of RSPIs - Functions: query, identify, export, and etc. Highway Safety Analysis for the District 7

Roadway Safety Performance Index (RSPI) Two Regression Crash Prediction Models Over 20 geometric, pavement conditions, roadway, and traffic characteristics covariates Only analyzed Austin District data Does not consider variables pertaining to the operator such as health or impairment or the condition of the vehicle 8

RSPIs_Ind RSPIs_Acu Roadway Safety Performance Index (RSPI) RSPI Ind - reflects the individual risk of each exposure on a homogenous roadway segment or an intersection. RSPI Acu - describe the collective risk which influences the reliability of service on a roadway segment or an intersection and provide an overall safety performance f(.) for RSPI_INV g(.) for RSPI_ACU 12 12 10 10 8 8 6 6 4 4 2 2 0 0 50 100 150 200 250 300 350 Crash Count* Severity factor 0 0 20 40 60 80 100 RSPI_Ind * Traffic factor 9

Roadway Safety Performance Index (RSPI) 10

Roadway Safety Performance Index (RSPI) Top 10 most statistically significant covariates in crash risk (in no particular order): ADT (light vehicle & truck) Degree and Length of Horizontal Curve Lane width Median type 3 (3=Unprotected) Number of lanes Widening or lane reconfiguration Pavement type Shoulder width Skid number Speed limit Urban/Rural Resurfacing Projects 11

Long-Term Skid Resistance Performance Long-Term Skid Resistance Performance (2008-2014) TOM_ALL PFC SMA D-GR TYC 0 1 2 3 4 5 6 7 8 Years in Service 12

2015 Wet Weather Crash Summary by Mix Type For 2015 (as of 8/21/2015), the following are the percentage of wet weather crashes by surface mixture type: 59% Dense Graded HMA Surface Many of these roadway were overlaid 3-5 year from 2015 35% Flushed Seal Coats 6% SMA 13

High-Friction Surface Courses High-Friction Surface Courses (2008) Seal Coats, TOM, PFC, SMA, and Concrete pavement Keys to sustainable, high surface friction: Texture (gradation) Aggregate hardness and durability TOM-C Surface Texture 14

Crash Rate (# of Crashes per 100 million Vehicle Miles Traveled) High-Friction Surface Courses Crash Rate vs. Skid Values Skid values have significant impact on crash rates Sensitivity Study illustrates reduction in crash rates with increase in skid resistance (value) In FY 2008, started to implemented new High- Friction Surfaces Courses (Seal Coat, TOM, SMA or PFC) Goal was to reduce overall crash rate, especially wet surface crashes 250 200 150 100 50 IH SH US FM 0 Skid Value 15

High-Friction Surface Courses Skid Scores 20% Increase in Overall District Skid Scores 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Austin Skid Score 16

Wet Weather Crash Rate (Crashes per 100 million vehicle-miles of travel) Wet Weather Fatal Crash Rate (Crashes per 100 million vehiclemiles of travel) High-Friction Surface Courses WW Crash History 30 28 0.4 25 22 23 0.3 20 0.28 19 19 20 0.3 0.22 17 17 17 17 15 10 0.15 0.18 0.16 10 13 0.17 0.2 0.2 0.11 0.10 5 0.08 0.09 0.08 0.1 0.08 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0.1 WW_Crash_Rate WW_Fatal_Crash_Rate 17

DMV High-Friction Surface Courses WW Crash vs. Growth Austin District is experiencing high growth rate and increasing traffic volumes. 35 30 25 24 33 35 30 Daily Vehicle Miles Growth Rate = 2.25% per year Wet Weather Fatal Crash Counts = -5.3% per year 20 15 18 13 21 20 12 22 14 25 10 10 10 10 20 5 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 15 WW_Fatal_Crash_Count Daily Vehicle Miles (million) 18

DMV High-Friction Surface Courses WW Crash vs. Growth Austin District is experiencing high growth rate and increasing traffic volumes. 35 30 25 24 33 35 30 Daily Vehicle Miles Growth Rate = 2.25% per year Wet Weather Fatalities Counts = -5.3% per year 20 15 18 13 21 20 12 22 14 25 10 10 10 10 20 5 0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 15 WW_Fatal_Crash_Count Daily Vehicle Miles (million) 19

High-Friction Surface Courses High-friction surface course policy contribution to the Austin District Wet Surface Crash Reduction Program: Daily Vehicle Miles (millions) WW_Crash_ Rate WW_Fatal_ Crash_Rate WW_Fatality_ Rate Annual Precipitation 2003-2008 28.6 21.3 0.18 0.21 32.3 2009-2014 31.9 15.7 0.10 0.11 33.0 % Change 11.6% -26.6% -45.5% -48.0% 2.3% 20

Conclusions & Future Work High-friction surface course initiative has been a success. It has preserve pavements and more importantly, it has preserved lives and property. System safety program has allowed us to optimize our PM funding and manage our assets responsibly. RSPI, in tandem with other pavement analysis, is an effective tool to identify high-risk roadways and possible roadway attributes contributing to high crash rates. Abundant amount of unintegrated data Continue to refine and calibrate models with real-world data High-friction surface has been officially hard-wired into the District Pavement Design SOP, as of March 2015 21

QUESTIONS For more information: Mike Arellano, P.E. Austin District Pavement Engineer miguel.arellano@txdot.gov (512) 585-3197 22