## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Speed Prediction  
Traffic speed prediction involves forecasting the average speeds of vehicles on specific road segments over a given period. This prediction is significant for transportation because it is crucial in traffic management, road safety, and infrastructure planning. By accurately predicting traffic speeds, authorities can optimize traffic signal timings, manage congestion, and provide real-time traffic information to drivers. This leads to improved travel times, reduced fuel consumption, and enhanced overall efficiency and safety of the transportation network. Deep learning models have been developed to accurately forecast traffic speed based on different data sources, such as remote microwave sensor data, network-wide traffic patterns, and spatiotemporal correlations. Among the range of deep learning models applied to this problem, RNNs, LSTMs, and GRUs have emerged as popular choices due to their ability to capture temporal dependencies effectively. [PERSON] et al. (2019) proposed an LSTM neural network for traffic speed prediction using remote microwave sensor data, which demonstrates superior prediction performance in terms of accuracy and stability. This work lays the foundation for the application of LSTM networks in traffic speed prediction. In a different direction, [PERSON] et al. (2015) introducedstructural RNNs for traffic speed prediction, demonstrating the potential benefits of embedding topological information in the road network to improve the learning process of traffic features. Timely and accurate traffic speed prediction is essential for traffic management, but existing methods face challenges in feature extraction from large-scale traffic data. [PERSON] et al. (2016) further advanced this research by introducing a deep bidirectional and unidirectional LSTM for network-wide traffic speed prediction. The study shows that a deep Bi-LSTM neural network achieves superior prediction performance for the entire traffic network. Similarly, [PERSON] et al. (2017) presented the LC-RNN model, which integrates the RNN and CNN to achieve more accurate traffic speed prediction. This approach highlights the potential benefits of combining different neural network architectures for this task. [PERSON] et al. (2018) also introduced a hybrid spatiotemporal feature selection algorithm of a CNN-GRU model for short-term traffic speed prediction, showcasing the importance of combining spatial and temporal features for accurate predictions. [PERSON] et al. (2019) proposed an attention-based multi-encoder-decoder neural network for freeway traffic speed prediction, leveraging convolutional LSTMs to capture spatiotemporal relationships of multiple input sequences. Their focus on capturing spatiotemporal relationships through attention mechanisms further advanced the state of the art in traffic speed prediction. [PERSON] et al. (2010) proposed a hybrid deep learning approach for large-scale traffic speed prediction. The model comprises a Conv-LSTM module, an attention mechanism module, and two Bi-LSTM modules. The introduction of the attention mechanism module enhances Conv-LSTM's performance by automatically capturing the importance of different historical periods and assigning corresponding weights. Additionally, two Bi-LSTM networks are designed to extract daily and weekly periodic features and capture trends from forward and backward traffic data. The model demonstrates good predictive performance but has a complex structure and slower prediction speed. Additionally, traditional traffic forecasting methods have significant limitations in capturing the dynamic characteristics of complex traffic networks. Therefore, there is a need for a predictive model that can efficiently represent spatial dependencies within the traffic network, model non-linear temporal dynamics simultaneously, and perform long-term forecasting for multiple time steps. [PERSON] et al. (2011) introduced a novel graph deep learning model that incorporated an attention mechanism for predicting traffic speeds in the network.