## 4 Conclusions
Beyond expanding the dataset and enhancing the model's accuracy, future studies could explore the integration of other environmental and traffic-related variables to capture a more holistic view of road safety dynamics. In addition, it is also important to explore microscopic traffic simulation to evaluate the safety benefits of improved distance management between vehicles as a result of having real-time friction information. Lastly, evaluating the framework's adaptability and effectiveness across diverse geographic regions and varying weather conditions could also contribute to its robustness. Conceptualization, Q.X. and T.J.K. ; Methodology, Q.X. and T.J.K. ; Validation, Q.X. and T.J.K. ; Formal Analysis, Q.X. and T.J.K. ; Writing--Original Draft Preparation, Q.X. and T.J.K. ; Writing--Review and Editing, Q.X. and T.J.K. ; Visualization, Q.X. and T.J.K. All authors have read and agreed to the published version of the manuscript. This research was funded by NSERC grant number RGPIN-2017-06448. Data Availability Statement: Data generated or analyzed during this study are available from the corresponding author upon reasonable request. The authors would like to thank the City of Edmonton for providing the data used in this study. This study is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors declare no conflict of interest. * City of Edmonton Motor Vehicle Collisions. Available online: [[https://www.edmonton.ca/transportation/traffic_safety/motor-vehicle-collisions](https://www.edmonton.ca/transportation/traffic_safety/motor-vehicle-collisions)]([https://www.edmonton.ca/transportation/traffic_safety/motor-vehicle-collisions](https://www.edmonton.ca/transportation/traffic_safety/motor-vehicle-collisions)) (accessed on 27 August 2023). * Esri Canada (2020) Esri Canada \"Light Gray Canvas Base\". [Basemap] Scale Not Given. \"World Topographic Map\". Available online: [[https://www.arcgis.com/home/item.html?id=291](https://www.arcgis.com/home/item.html?id=291) da5 eab3a0412593b6 cd38437989f]([https://www.arcgis.com/home/item.html?id=291](https://www.arcgis.com/home/item.html?id=291) da5 eab3a0412593b6 cd38437989f) (accessed on 26 April 2022). * [PERSON] (2023) [PERSON]; [PERSON] Developing Machine Learning-Based Approach for Predicting Road Surface Frictions Using Dashcam Images--A City of Edmonton, Canada, Case Study. _Can. J. Civ. Eng._**2023**. [CrossRef]
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