Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

TLDR

Pavement maintenance is critical for ride quality, reducing congestion, pollution, and accidents, and improving road safety is a key goal of pavement management systems. The study investigates the relationship between accident frequency and pavement distress variables using Tennessee’s PMS and Accident History Database. The authors developed 21 Negative Binomial Regression models on four urban interstates with asphalt pavements, 55‑mph limits, and divided medians, using rut depth, IRI, and PSI to predict various traffic accident frequencies. The models showed that PSI consistently predicted accident frequencies better than RD and IRI, with PSI models outperforming others across all accident types, indicating PSI should be integrated into pavement management systems for safety. © 2010 John Wiley & Sons, Ltd.

Abstract

Abstract Pavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting 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 models goodness‐of‐fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system. Copyright © 2010 John Wiley & Sons, Ltd.