Impact of Reduced Maintenance on Road Safety
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1 Impact of Reduced Maintenance on Road Safety Zhanmin Zhang, Ph.D. Associate Professor and Fellow of the Clyde E. Lee Endowed Professorship in Transportation Engineering
2 Project Quantitative Relationship between Crash Risks and Pavement Skid Resistance
3 Project Team Project Director: Project PMC: Research Supervisor: Researchers: Terry Paholek Wade Odell Mark Shafer Leo Ramirez Debra Vermillion Scott Reaves Zhanmin Zhang Michael R. Murphy Hui Wu Zhe Han
4 Expert Workshop Group Member
5 Background In the past 5 years, over 12,000 fatalities, 48,700 incapacitating injuries, 198,300 non-incapacitating injuries, and 415,900 possible injuries have occurred on the statemaintained highway network in Texas TxDOT is responsible for the maintenance of over 195,000 lane miles of highways under severely constrained budget The goal is to evaluate the impact of pavement skid resistance on crash risk so that minimum skid resistance threshold values can be established to ensure that highway safety will not be compromised with reduced maintenance conditions
6 Research Scope and Objectives Develop a methodological procedure to determine the quantitative relationship between crash rates and pavement skid number (SN) values Determine guidelines for SN thresholds to assist TxDOT in making critical maintenance decisions Address all types of state-maintained highways in Texas Analyze wet weather related crashes as a separate group for comparison purposes
7 CRR is expressed as a function: Where: CRR = Crash Rate Ratio CRR Concept Cumulative percentage of total crashes below skid number SN = Cumulative percentage of total lane miles below skid number SN
8 Data Integration
9 CRR Calculation (2011) Total Crashes % % 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Lane Miles % 90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Skid Number Distribution of Total Crashes by SN Skid Number Distribution of Lane Miles by SN Skid Number Cumulative percentage of total crashes Cumulative percentage of lane miles CRR
10 CRR Calculation (2011) CRR Statewide Average SN=38, CRR= Skid Number
11 Crash Risk vs. Skid Number (08-11 ) CRR Skid Number
12 CRR-SN Relationship (08-11 ) Exponential curve model used in analysis: CRR=3.894 e SN (R 2 = 0.992)
13 Statewide CRR-SN Relationship (08-11 ) Lane miles: 274,182 Total Crashes Count: 337,851 Wet Crashes Count: 36,790 CRR-SN for total crashes: CRR=3.894 e SN CRR Total Crashes Wet Weather Crashes CRR-SN for wet crashes: Skid Number CRR=5.023 e SN when SN < 39 CRR=3.894 e SN when SN 39
14 Threshold Values for SN
15 Statewide Threshold Values for SN CRR Immediate Action Field Test 1.00 Vigilance 0.50 Do Nothing 0.00 Skid Number
16 Stratified Groups Low AADT (0,2,500] Group 1 Curve Medium AADT (2,500,4,500] Group 2 Low Speed Limit (0,55] High AADT >4,500 Low AADT (0,2,500] Group 3 Group 4 All Highway Segments Non-Curve Curve Medium AADT (2,500,4,500] High AADT >4,500 Low AADT (0,2,500] Medium AADT (500,4,500] Group 5 Group 6 Group 7 Group 8 High Speed Limit (55,80] High AADT >4,500 Low AADT (0,2,500] Group 9 Group 10 Non-Curve Medium AADT (2,500,4,500] Group 11 High AADT >4,500 Group 12
17 Recommended Thresholds Total Crashes Wet Weather Crashes SN 1 SN 2 SN 3 SN 1 SN 2 SN 3 Highway Group ID Statewide
18 Conclusions Compared to traditional regression models that are developed to describe the relationship between pavement skid resistance and crash risks, CRR-SN curve is specifically designed to support management of skid resistance at the network level The development of CRR-SN curves provides both researchers and engineers an easy way to quantify and understand the overall relationship between skid resistance and crash risks By choosing the break points of CRR according to the safety goals and objectives of the transportation agencies, the critical threshold values of SN can be directly determined
19 QUESTIONS?
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