## 6 Conclusions  
Traffic congestion is a critical issue that affects various areas, including transportation mobility, physical and mental health, economic growth, and the environment. Therefore, predicting traffic congestion is an important area of research that has the potential to alleviate congestion-related issues. Traffic authorities can utilize traffic congestion predictions to improve transportation efficiency and safety by providing users with up-to-date information on roads and adjusting road infrastructures to redirect traffic and prevent congestion. Similarly, commuters, logistic companies, and emergency response teams can leverage traffic forecasts and road conditions to schedule their routes through non-congested regions. In this study, we provide a comprehensive review of various techniques for predicting traffic congestion, including statistical models, machine learning, and hybrid approaches. Additionally, we propose a deep neural network architecture, RHRNet, which uses image data from the TOPIS website to predict city-wide traffic congestion. The proposed RHRNet model utilizes a tiny-HRNet backbone that leverages a multi-scale feature extraction technique to learn better spatial feature representations, and a ConvLSTM-based decoder that learns time-series feature representation and aggregates all the multi-scale feature maps to obtain accurate prediction results. Moreover, we utilize an inexpensive data collection method by capturing images from the open-source TOPIS website for the central Seoul region. We conducted experiments on several state-of-the-art (SOTA) deep learning architectures, including PredNet, UNet, ConvLSTM, and Autoencoder, and compared their performance with our proposed RHRNet. Our investigation shows that the RHRNet model outperforms the others in terms of accuracy, precision, and recall for all three prediction horizons (10, 30, and 60 min) compared to all others. Specifically, RHRNet achieved an approximately 4%, 2.5%, and 1.5% improvement in accuracy over the next-best model PredNet, for the 10-, 30-, and 60-min prediction horizons, respectively. Similarly, RHRNet outperforms other SOTA models by a large margin in terms of precision and recall for all three congestion categories (Jam, Slow, and Free) across all prediction horizons. The superior results obtained by the proposed RHRNet demonstrate that by incorporating multi-scale information for feature extraction and employing recurrent neural network-based decoder the model can effectively learn from historical traffic patterns and make accurate predictions. Figure 5: Road-wise recall and precision comparison for traffic congestion prediction on 3 December 2019. (**a**) Prediction precision and recall for prediction horizon of 10 min. (**b**) Prediction precision and recall for prediction horizon of 30 min. (**c**) Prediction precision and recall for prediction horizon of 60 min. (**d**) Color code for traffic congestion prediction models. Although the findings of this study are promising, there is still room for enhancing the computational efficiency of the model. In the current dataset, a significant portion comprises background information, and computational resources are being wasted on learning them. In the future, we intend to eliminate the background information from the dataset to improve the efficiency of the model. Furthermore, incorporating external factors such as weather information could enhance the prediction accuracy of the model. Additionally, incorporating a Vision Transformer as a backbone, given its recent success in computer vision tasks, may potentially enhance the performance of the model in future studies. Conceptualization, S.R. and H.K. ; methodology, S.R. ; software, S.R., Y.-C.K. and N.R. ; validation, S.R., S.B. and H.K. ; formal analysis, S.R. ; investigation, S.R. ; resources, H.K. ; data curation, S.R. ; writing--original draft preparation, S.R., Y.-C.K. and N.R. ; writing--review and editing, S.R., Y.-C.K., S.B., N.R. and H.K. ; visualization, S.R. ; supervision, H.K.