# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 6 Conclusions  
Traffic signs play a vital role in the safe movement of vehicles. Signs must be clearly visible to drivers to serve their intended purposes. Sign retro-reflectivity gradually deteriorates over time, making them less visible, particularly at night. They need appropriate treatment or warrant timely replacement before the retro-reflectivity index (\(R_{A}\)) reaches the minimum acceptable limits. Therefore, it is vital to accurately predict sign retro-reflectivity for different sign sheeting under various circumstances, weather, and aging conditions. Previous studies in this regard have mostly focused on simple regression-based statistical modeling. However, these models have several unrealistic assumptions that limit their applications for large scale real-world applications. Further, they also suffer from weak prediction accuracy. We discussed some notable papers in this area and highlighted the shortcomings, including the requirement to have a separate model for each combination of sheeting type and color. In order to fill this research gap through this study, we aimed to explore the problem of modeling the degradation of retro-reflection for field traffic signs using neural network based deep learning systems. A handheld Retrosign GR3 retro-reflectometer was used to collect the field signs' readings. Field data was collected on different sign attributes such as sheeting brand, grade/type, color, sign different orientation, observation angles, and sign aging. We implemented three systems with different feature engineering methods. Study analysis showed that a neural network with one-hot encoding achieved the best results with an MAE of 34.809 and an \(R^{2}\) score of 0.976 on unseen test data, outperforming all previously proposed models for traffic signs retro-reflection prediction. Finally, we conducted a number of sensitivity analysis experiments to explore the role and importance of each feature in predicting the \(R_{A}\) value for a given traffic sign. Variable sheeting color and observation angles were identified as the most significant predictor variables for \(R_{A}\).  
The findings of the current study highlighted how different neural-network architectures could capture complex relationships to effectively learn sign degradation patterns. Knowledge gained from this study could serve as essential guidance to transport agencies for effective sign management practices. In future studies, detailed datasets containing information on other explanatory attributes such as weather conditions, sun exposure, air pollution could be considered, which can enhance the prediction performance of models and make them applicable to different situations including usage in different regions and countries.  
Figure 7: Example plots showing retro-reflectivity values as a function age for sample traffic signs in different colors. (**a**) Color = White, (**b**) Color = Green, (**c**) Color = Yellow.  
**Author Contributions:** Conceptualization, A.A., A.J. and I.A.; data curation, A.J.; formal analysis, A.A., A.J. and I.A.; investigation, A.A.; methodology, A.A. and I.A.; supervision, I.A.; validation, A.A., A.J. and I.A.; visualization, A.A. and A.J.; writing--original draft, A.A. and A.J.; writing--review and editing, A.A., A.J. and I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. Research support provided by King Fahd University of Petroleum and Minerals (KFUPM).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or code that support the findings of this study are available from [PERSON] ([EMAIL_ADDRESS]) upon reasonable request.  
The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM) for supporting this work.  
The authors declare no conflict of interest.