Effects of Asphalt Pavement Conditions on Traffic Accidents in Tennessee Utilizing Pavement Management System

Maintenance of pavement is essential to ensure good riding quality and avoid the happening of congestion, air pollution, and especially traffic accident. Improving road safety through proper pavement engineering and maintenance is one of major objectives for pavement management system. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency in highway segment and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, twenty-one negative binomial regression models for various accident types were calibrated with different pavement distress and condition variables including Rut Depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicate that the RD models did not perform well, except for accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the three groups of models goodness-of-fit results, it is found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that PSI models should be considered as a comprehensive method which can integrate both highway safety and pavement condition measurements into the pavement management system.