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Estimating safety effects of pavement management factors utilizing Bayesian random effect models.
Ximiao Jiang1, Baoshan Huang, Russell L Zaretzki
1Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee, USA.
Traffic Injury Prevention
|August 16, 2013
Summary
Maintaining good pavement quality, specifically low pavement roughness, significantly reduces traffic crash risk. This study highlights the importance of pavement management factors in road safety, using real-time data for accurate crash risk assessment.
Area of Science:
- Transportation Engineering
- Road Safety
- Pavement Management
Background:
- Traditional pavement management studies often use aggregated data, potentially losing critical information and biasing results.
- Research on pavement factors influencing traffic crashes, especially for fair to good quality roads, is limited.
- Real-time, location-specific data offers a more accurate approach to assessing crash risk.
Purpose of the Study:
- To estimate the effects of pavement management factors on traffic crash occurrence using real-time data.
- To investigate crash risk on roadways with overall fair to good pavement quality.
- To compare various statistical models for analyzing crash data and pavement factors.
Main Methods:
- Utilized crash and pavement quality data from Tennessee state routes (2004-2009).
- Employed Bayesian methods and Markov Chain Monte Carlo (MCMC) simulation to construct six models (Poisson, NB, OREP, ORENB, TREP, TRENB).
- Accounted for temporal and spatial correlations in the data, comparing models using the deviance information criterion.
Main Results:
- Pavement Serviceability Index (PSI) and Pavement Distress Index (PDI) significantly impacted crash occurrence.
- Rutting depth was not a significant factor in crash frequency.
- Lane width, median width, terrain type, and posted speed limit also influenced crash frequency.
Conclusions:
- Reducing pavement roughness is crucial for lowering the likelihood of traffic-related crashes.
- Temporal correlation among observations was significant.
- The One Random Effect Negative Binomial (ORENB) model demonstrated superior performance in this analysis.
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