## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Traffic Flow Prediction  
Traffic flow prediction involves estimating the number of vehicles traversing a specific road segment within a certain timeframe. It is crucial for transportation planning and management because it allows for the optimization of traffic signals, better planning of road maintenance, and effective real-time traffic management. This, in turn, leads to reduced congestion, improved fuel efficiency, shorter travel times, enhanced safety, and increased overall reliability of the transportation network. The field of traffic flow prediction has witnessed significant advancements in recent years, particularly with the utilization of RNNs and their variants, such as LSTMs and GRUs. These methods have shown promise in accurately predicting traffic flow patterns, surpassing traditional approaches like ARIMA models. Figure 8: Example of hybrid RNN and attention models. The literature demonstrates a growing interest in harnessing deep learning techniques for traffic flow prediction, focusing on enhancing prediction models' performance and accuracy. [PERSON] et al. [30] introduced LSTM and GRU neural network methods for traffic flow prediction, showcasing their superiority over ARIMA models. Their work highlights the potential of RNN-based deep learning methods in capturing the intricate patterns inherent in traffic flow data. Similarly, [PERSON] et al. [82] proposed a hybrid deep learning model featuring attention-based Conv-LSTM networks, which aims to automatically extract intrinsic features of traffic flow data, resulting in improved prediction performance. This underscores the effectiveness of advanced deep learning architectures for traffic flow prediction. [PERSON] et al. [83] explored using an enhanced GRU neural network, known as the Bi-GRU prediction model, for short-term traffic flow prediction. Their study provides insights into the characteristics of this model and its effectiveness in addressing short-term traffic flow prediction tasks. [PERSON] et al. [84] also introduced the Attentive Attributed Recurrent GNN (AARGNN) for traffic flow prediction, incorporating GNNs to consider multiple dynamic factors. This multi-faceted approach illustrates the increasing complexity of models designed to capture the dynamic nature of traffic flow. [PERSON] et al. [85] proposed a multi-attention predictive RNN (MAPredRNN), which leverages dynamic spatiotemporal data fusion for traffic flow prediction. Their approach emphasizes effectively fusing spatiotemporal data for accurate traffic flow prediction. Moreover, [PERSON] et al. [86] conducted a comparison between the LSTM model and traditional RNNs and demonstrated the LSTM model's higher prediction accuracy and effectiveness in traffic flow prediction. Previous studies have often integrated spatiotemporal prediction models with GNNs and temporal processing modules to capture the spatiotemporal dependencies of traffic networks. However, they have encountered issues related to static spatial connectivity and the loss of global temporal dependency information. To address these challenges, [PERSON] et al. [80] introduced the DSTGRN, which utilizes spatial and temporal attention mechanisms for fine-grained modeling, improving accuracy in predicting traffic flow data. RNNs and their variants, like LSTMs, are designed to model long-term temporal correlations in sequences. However, multi-regime models consider multiple states in the traffic system, each with distinct characteristics, necessitating separate models. [PERSON] et al. [87] proposed a hybrid Hidden Markov LSTM model that combines their strengths, improving traffic flow prediction, especially in complex and non-stationary scenarios. Due to various factors, such as weather, accidents, and road control affecting traffic flow, it exhibits significant and irregular fluctuations. This challenge is particularly pronounced when predicting traffic flow trends at the minute and hour levels. To address this issue, [PERSON] et al.