ENGINEERING DRIVER SAFETY INTO PAVEMENT PRESERVATION
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1 ENGINEERING DRIVER SAFETY INTO PAVEMENT PRESERVATION 2015 TxDOT Short Course Miguel Arellano, P.E. Austin District Pavement Engineer October 13, 2015
2 Table of Contents 1 Goals: Overall Reduction in Crashes and Fatalities Risk Assessment and Planning Assessing Narrow Pavement Widening Safety Performance High-Friction Pavement Surface Conclusions & Future Work 24 2
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. 3
4 Pavement Management Goals: Enhance System Safety Goals: Reduction of overall crash types and severity Reduction of 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 4
5 Roadway Safety Performance Index 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) 5
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 - Publish maps of RSPIs - Functions: query, identify, export, etc. Highway Safety Analysis for the District 6
7 Roadway Safety Performance Index Two Regression Crash Prediction Models Over 20 geometric, pavement conditions, roadway, and traffic characteristics covariates Only Austin District data analyzed Does not consider variables pertaining to the operator such as health or impairment or the condition of the vehicle 7
8 Roadway Safety Performance Index RRRR III - reflects the individual risk of each exposure on a homogenous roadway segment or an intersection RRRR AAA - describes the collective risk which influences the reliability of service on a roadway segment or an intersection and provides an overall safety performance f(.) for RSPI_INV g(.) for RSPI_ACU RSPIs_Ind RSPIs_Acu Crash Count* Severity factor RSPI_Ind * Traffic factor 8
9 Roadway Safety Performance Index 9
10 Roadway Safety Performance Index 10
11 Roadway Safety Performance Index 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 with High-Friction Surface Courses 11
12 Assessing Narrow Pavement Widening Safety Performance Texas: 40,000 lane-miles with narrow two-lane FM/RM roads Crash rate: per 100 million VMT in 2012 Relative higher risk of run-off-the-road (ROR), and head-on (HO) crashes 100% Percentage of crashes happened on selected roads 80% 60% 40% 20% 0% Rural Two-lane FM/RM Roads Statewide Highway ROR Crahes HO Crashes Other Type 12
13 Assessing Narrow Pavement Widening Safety Performance Objectives: To investigate if narrow pavement widening projects can improve overall traffic safety or reduce the risks of certain types and/or severity levels of crashes To identify lane and shoulder widths and configurations and best practices to optimize reduction of risks of certain types and/or severity levels of crashes 13
14 Narrow Pavement Widening Projects Before and after study of 22 narrow widening projects in Austin rural areas from 2004 to 2011 Average length is 3.54 miles Crash data from 2003 to 2012 For each project, a minimum of 12 months of crash data was available for the before/after periods Reference group: 1,585 roadway segments with similar characteristics as the 22 narrow widening projects 14
15 Narrow Pavement Widening Projects Treated sites Reference Group (Untreated sites ) Source: FHWA, Highway Safety Improvement Program (HSIP) Manual
16 Narrow Pavement Widening Projects All ROR HO # of crashes in after period Expected crashes in after period without widening (STDV) (22.1) (15.6) 22.4 (4.4) % reduction in crashes (STDV) 31.5 (5.4) 35.7 (6.8) 55.4 (15.4) Index of effectiveness (STDV) 0.68 (0.05) 0.64 (0.07) 0.43 (0.15) Expected crashes during after period without widening Count of crashes during after period # of Crashes All ROR HO
17 Narrow Pavement Widening Projects All F+I Injury PDO # of crashes in after period Expected crashes in after period without widening (STDV) (22.1) (15.8) (12.7) (15.1) % reduction in crashes (STDV) 31.5 (5.4) 29.5 (8.7) 30.7 (8.0) 31.7 (7.4) Index of effectiveness (STDV) 0.68 (0.05) 0.70 (0.09) 0.69 (0.08) 0.68 (0.07) Expected crashes during after period without widening Count of crashes during after period # of Crashes All F+I Injury PDO
18 Narrow Pavement Widening Projects Findings: Narrow pavement widening projects reduced overall crash rates and the severity of the crashes for all crash types 35.7% reduction in run-off-the-road crashes 55.4% reduction in head-on crashes Integrated safety analysis will provide information and best practices such as lane widths, lanes configurations, shoulder widths, center striping widths, end treatments, and other practices to optimize the reduction in crash types and severity 18
19 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 2007, 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 Crash Rate (# of Crashes per 100 million Vehicle Miles Traveled) IH SH US FM 0 Skid Value 19
20 High-Friction Surface Courses Skid Scores 20% Increase in Overall District Skid Scores Austin Skid Score 20
21 High-Friction Surface Courses Wet Weather Crash History Wet Weather Crash Rate (Crashes per 100 million vehicle miles of travel) Wet Weather Fatal Crash Rate (Crashes per 100 million vehicle miles of travel) WW_Crash_Rate WW_Fatal_Crash_Rate 21
22 High-Friction Surface Courses WW Crash vs. Growth Austin District is experiencing high growth rate and increasing traffic volumes Daily Vehicle Miles Growth Rate = 2.25% per year Wet Weather Fatalities Counts = -5.3% per year DMV WW_Fatal_Crash_Count Daily Vehicle Miles (million) 22
23 High-Friction Surface Courses High-friction surface course policy contributed to Wet Surface Crash Reduction Program: Daily Vehicle Miles (millions) WW_Crash_ Rate WW_Fatal_ Crash_Rate WW_Fatality_ Rate Annual Precipitation % Change 11.6% -26.6% -45.5% -48.0% 2.3% 23
24 Conclusions & Future Work 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 algorithms with real-world data Currently reassessing our narrow widening practices Creating a district standard for specific roadway situations High-friction surface has been hard-wired into the District Pavement Design SOP, as of March
25 QUESTIONS? For more information: Mike Arellano, P.E. Austin District Pavement Engineer (512)
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