## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### Travel Time Prediction  
Travel time prediction involves estimating the duration required for a vehicle to travel between two points in a transportation network. This prediction is significant for transportation as it enhances route planning, improves the accuracy of navigation systems, and aids in traffic management. Providing reliable travel time estimates helps reduce uncertainty for travelers, optimizes logistics and delivery operations, and enhances the efficiency of public transit systems. Accurate travel time predictions contribute to reduced congestion, better resource allocation, and overall improved reliability and performance of the transportation network. There has been a growing interest in using RNNs in travel time prediction in recent years. One of the first successful attempts to apply RNNs in this field was made by [PERSON] et al. (2012). They explore using LSTM neural network models specifically designed for travel time prediction, achieving remarkable results. In addition, [PERSON] et al. [113] proposed a deep feature extraction framework based on an RNN and DNN for bus dynamic travel time prediction, achieving greater efficiency than traditional machine learning models. Furthermore, a study by [PERSON] et al. [114] introduced an LSTM-based method with an attention mechanism for travel time prediction, which demonstrates better accuracy and effective utilization of departure time. Another interesting work by [PERSON] et al. [115] used a convolutional LSTM neural network for multi-output bus travel time prediction, allowing the discovery of complex patterns not captured by traditional methods. Additionally, there have been efforts to combine different neural network models for travel time prediction. [PERSON] et al. [116] proposed a deep hybrid model that combines the GRU and eXtreme Gradient Boosting (XGBoost) through linear regression, showcasing good prediction accuracy for freeway travel time. Moreover, due to the widespread availability of various observation data, such as vehicle data, data-driven travel time prediction methods have been rapidly advancing. In many existing large-scale network studies, speed time series data directly estimated from vehicle data are commonly used as inputs. However, in free-flow conditions, speed variations are not significantly influenced by the number of vehicles, making it challenging to depict the traffic conditions in this mode accurately. To address this issue, a study by [PERSON] et al. [117] introduced traffic density-based travel time prediction with GCN-LSTM, showcasing the superiority of density input in achieving early detection of traffic congestion and improving prediction accuracy. Following this, a study by [PERSON] et al. [118] introduced the Traffic Trajectory Prediction Network (TTP-Net). This novel neural network leverages tensor decomposition and graph embedding to achieve significantly better performance in travel time prediction. Furthermore, the processing capacity of traditional data processing and modeling tools needs to be improved for handling large-scale travel datasets. To overcome the challenges posed by massive data, [PERSON] [119] employed big data analytics engines, Apache Spark and the Apache Mixed Sparse Matrix Network (MXNet), for data preprocessing and modeling. They introduce a hierarchical LSTM model with attention mechanisms for network-level short-term travel time prediction, successfully forecasting unusual congestion and achieving the best prediction results at 30 min and 45 min horizons. Due to GRU's ability to handle long-term traffic sequences, GRUs have been successfully applied to traffic prediction problems. However, existing GRUs do not consider the relationships between various positions in historical travel time sequences. [PERSON] et al. [120] introduced an attention-based GRU model for short-term travel time prediction to address this issue, allowing the GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly.