## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Accidents and Congestion Prediction  
Traffic accidents and congestion prediction involves forecasting the likelihood of traffic incidents and the occurrence of traffic jams on specific road segments. Traffic accidents and congestion prediction have been the subject of extensive research in artificial intelligence and machine learning. Various approaches, including RNNs, LSTMs, GRUs, and other deep learning models, have been employed to tackle the challenges of predicting traffic accidents and congestion. Traffic accident prediction is to estimate the likelihood and severity of accidents by analyzing historical data and contextual data (e.g., weather, and road conditions). Its significance lies in enhancing road safety, optimizing traffic management, allocating emergency resources effectively, informing urban planning, guiding insurance risk assessments, raising public awareness, and reducing environmental impact. [PERSON] et al. [121] explored the use of RNNs for predicting the severity of traffic accidents. Their findings indicate that RNNs, within deep learning frameworks, hold promise in predicting injury severity in traffic accidents. Furthermore, [PERSON] et al. [122] demonstrated the superiority of deep learning models, like CNNs and RNNs, in predicting the severity of traffic accidents compared to traditional models. Their research emphasizes the advantages of leveraging deep learning techniques for more accurate and stable predictions. Similarly, [PERSON] et al. [123] introduced an extended-range prediction model using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) the optimized RNN-GRU-LSTM for driver stress and drowsiness, highlighting the potential of hybrid algorithms to enhance prediction performance. To accurately predict the number of traffic accidents and address road safety issues more effectively, [PERSON] et al. [124] have proposed a time series prediction model based on LSTM and the attention mechanism. They use road traffic accident data and meteorological data from the city of Curitiba, Brazil, as their research dataset and improve the internal gating unit structure of the LSTM model. This model is used to fit and predict the traffic accident dataset. The results indicate that the prediction performance of the road traffic accident prediction model based on LSTM and the attention mechanism outperforms that of the classical LSTM model and SVR model; overall, this model has significant practical implications for enhancing road traffic management. [PERSON] et al. [125] proposed a method called a Deep Spatiotemporal GCN (DSTGCN) for traffic accident prediction. The DSTGCN model combines the GCN for spatial data processing and the TCN for time series analysis, further exploring the spatial and temporal dependencies in traffic data. Experimental results show that the DSTGCN outperforms traditional methods in terms of traffic accident prediction accuracy, validating the model's effectiveness in capturing complex traffic patterns and interactions. Traffic congestion prediction is used to forecast traffic flow and identify potential bottlenecks by analyzing historical traffic data, real-time sensor information, weather conditions, and road network characteristics. Its significance lies in enabling proactive traffic management, reducing travel delays, optimizing infrastructure utilization, improving urban planning, enhancing emergency response times, and minimizing environmental impact by reducing vehicle emissions. [PERSON] et al. [126] conducted a comprehensive review of existing research on traffic congestion prediction using artificial intelligence, summarizing the application of various AI methodologies and categorizing them under different branches. [PERSON] et al. [127] proposed an LSTM-based method for predicting traffic congestion, focusing on correcting missing temporal and spatial data in traffic datasets.