## 6 Conclusions  
This study developed machine learning models to predict incident clearance time and used SHAP analysis to identify significant factors impacting crash-related ICT on Louisiana's rural interstates. The two separate approaches were utilized in a complementary framework by taking advantage of the strengths of machine learning models and SHAP analysis. While machine learning models provide better predictions, their methodologies are considered black boxes and do not excel at establishing relationships between response and independent variables. SHAP analysis is flexible and useful for analyzing the impact and direction of the influence of variables on a phenomenon. Machine learning and SHAP analysis were applied to crash datasets to analyze ICT in relation to traffic crashes. This study developed three ML models, namely XGBoost, CatBoost, and LightGBM, to predict incident clearance time in rural interstates in Louisiana, utilizing data collected from the Louisiana DOTD's crash database. Four ICT levels were defined based on 30 min intervals: short (0-30), medium (31-60), intermediate (61-90), and long (greater than 90). Based on different performance evaluation metrics, the results revealed that LightGBM performs better when predicting short-duration incidents, followed by XGBoost and CatBoost. XGBoost outperforms other models in predicting medium, intermediate, and long durations with the highest accuracy. In terms of average performance measures, XGBoost outperforms CatBoost and LightGBM in the collective model's predictive performance. By implementing these developed models, operators at the traffic management centers (TMC) can predict and disseminate accurate information about the duration of the incident to motorists and other agencies involved in managing road incidents. SHAP analysis was performed to identify significant features impacting ICT. It was found that crashes involving injuries, fatalities, heavy trucks, head-on collisions, roadway departure, and older drivers tend to increase the probability of longer ICT. In addition, other factors, like cellphone distraction and dark, rainy, snowy, and foggy conditions, also prolong ICT. The identification of these significant features impacting ICT can help incident management agencies, transportation planners, and policymakers take countermeasures to reduce ICT on rural interstates. For instance, strict implementation of distracted driving laws should be implemented to reduce the use of cell phones while driving. Also, lighting conditions on rural interstates in Louisiana should be improved. Taking seasonal countermeasures like roadside warning signs during foggy, snowy, or rainfall conditions is also recommended. The findings of this study are beneficial for traffic managers, transportation planners, and incident response agencies because they provide good prediction and analytical results for crash-related ICT by using machine learning models and SHAP analysis. The following are the recommendations for future research work:  
* It is recommended to predict different phases of incident duration, such as detection and response time, to better understand the factors responsible for prolonged ICT. * This research study is limited to Louisiana's rural interstates. Future studies are recommended to include urban interstates and other road types across Louisiana to improve the model's applicability. * To improve the model's accuracy and predictive performance, it is recommended that the model be trained on comprehensive data sets spanning more than 10 years
* It is also recommended that real-time traffic and weather data be used during modeling through the use of installed sensors to improve the model's prediction accuracy. The authors confirm their contributions to the paper as follows: study conception and design: M.M. and M.A.R. ; data collection and preparation: M.A.R. and W.A.K. ; analysis and interpretation of results: W.A.K. and M.M. ; draft manuscript preparation: W.A.K., M.M., K.A.T., J.C. and V.G.