Impact of rutting on traffic safety : a synthesis of research findings

Abstract

Quantifying the impact of rutting on traffic safety contributes to the development of objective models for evaluating pavement performance. However, the existing literature shows significant discrepancies in the impact of rutting on traffic safety. To this end, this study analyzed about 40 studies to comprehensively understand the impact of rutting on traffic safety in field observations and simulation studies. This study analyzed the influence of ten factors that may impact the relationship between rutting and traffic safety, such as weather, speed, and road type. It also established rutting limits and developed machine learning-based prediction models for accident rates caused by rutting under varying conditions. These findings reveal distinct trends, with simulation studies generally suggesting a higher impact of rutting on safety compared to field observations. This discrepancy is attributed to the limitations of simulation models in capturing human factors, such as drivers' ability to anticipate and adjust their behavior to mitigate risks. These results provide valuable insights for highway agencies and policymakers to develop more accurate rut limits and maintenance guidelines. These results also underscore the importance of considering rutting in the development of autonomous vehicles to ensure effective handling of rutting under varying conditions. This study highlights the need for more comprehensive field studies using larger datasets that account for various environmental and traffic factors. Additionally, integrating real-world driver behavior into simulation models could improve their accuracy.