# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 6 Conclusions  
Researchers have long strived to enhance the assessment of pavement conditions, aiming for automation, quantification, and objectivity. While the detection of pavement distresses has increasingly become automated and quantified, assessing the impact of these distresses remains largely subjective. Consequently, there is a pressing need to quantify the impact of pavement distresses for a more objective assessment of pavement performance. This study focused on understanding the impact of rutting on traffic safety using 38 field observations and simulation-based literature. Multiple studies have examined the influence of rutting on traffic safety, yet there remains a lack of consensus on its impact. Therefore, this study investigates the sources of discrepancies in reported results regarding the relationship between rutting and traffic safety. Ten factors were analyzed: study type, weather conditions (i.e., pavement wetness), lighting conditions, road type, driving trajectory, road geometry, speed, traffic volume, vehicle type, and study location. The study type was found to significantly influence the conclusions drawn. Simulation studies generally suggest 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. Wet conditions, curvature, and the presence of other defects were found to exacerbate the hazards posed by ruts. Moreover, driving across ruts, night-time driving, high speeds, and the use of light vehicles were identified as factors that intensify the severity of rut-related issues. Additionally, this study involved developing rut depth limits and prediction models for accident rates using rutting depth. This study presents rut depth limits considering different environmental and traffic conditions. Also, four ML-based prediction models were developed for evaluating accident rates using field observation data, with the DT model exhibiting the best performance. The relative weight analysis revealed that study location, weather conditions, road type, and road geometry are important factors for predicting accident rates using rut depth. This study represents a significant step toward achieving a more objective assessment of pavement conditions, which can improve the cost-benefit analysis of maintenance planning. The findings offer critical insights for highway agencies and policymakers to establish accurate rut limits and maintenance guidelines. They highlight the need for autonomous vehicle systems to address scenarios involving rutting under varying conditions. Furthermore, this study emphasizes the necessity of comprehensive field observation studies with larger datasets that cover a broader range of rut depths and account for diverse environmental and traffic factors. Future research should also address discrepancies between field observations and simulations by incorporating real-world driver behavior into simulation models to enhance their accuracy. Additionally, leveraging ML algorithms in future studies, supported by more comprehensive datasets, can significantly improve the applicability and predictive accuracy of models estimating accident rates influenced by pavement conditions. Overall, this study advances the understanding of rutting's impact on traffic safety, providing valuable insights for both current roadway management practices and future autonomous vehicle technologies. A.F. : Conceptualization, Methodology, Data collection, Formal analysis, Visualization, Validation, Methodology, Writing. M.-N.W. : Data collection, Formal analysis, Visualization, Writing. T.Z. : Review and editing, Visualization, Validation, Supervision, Funding acquisition. N.F. : Review and editing, Visualization, Validation. All authors have read and agreed to the published version of the manuscript. This research received no external funding.