## 5 Sub-Areas of Traffic Prediction Applications Using RNNs
### OD Demand Prediction  
Short-term OD demand prediction estimates the number of trips between different origins and destinations within a transportation network. Because of the high dimensionality and sparsity of the data, accurately determining OD demand is significantly more difficult than estimating traffic/passenger flow. This prediction is significant for transportation planning and management, providing critical insights into travel patterns and demands. It helps optimize route planning, enhance public transit scheduling, design infrastructure projects, and improve traffic management strategies. Accurate OD demand prediction ensures better resource allocation, reduces congestion, improves travel times, and enhances overall system efficiency and reliability. [PERSON] et al. [99] proposed a complex prediction model that integrates LSTMs and RNNs. The study emphasizes the model's efficiency across different seasons and validates its performance using various error metrics, but it faces challenges with complexity and data requirements. [PERSON] et al. (2019) presented an innovative LSTM approach to predict dynamic OD matrices in a subway network. The advantage of this method is its reliable short-term prediction of OD pairs. Still, it requires a substantial amount of smart card data for training and additional data from nearby transportation systems to enhance prediction accuracy, which can be considered a drawback. [PERSON] et al. (2019) utilized three types of RNNs, including simple RNN units, GRUs, and LSTMs, to predict short-term traffic OD demand. The results indicate that the simpler RNNs, such as simple recurrent units and GRUs, outperform LSTMs regarding accuracy and training time. Ride-hailing services have witnessed dramatic growth over the past decade but have raised various challenging issues, one of which is how to provide a timely and accurate short-term prediction of supply and demand. While the predictions for zone-based demand are extensively studied, much less effort is paid to the predictions for OD-based demand (namely, demand originating from one zone to another). However, OD-based demand prediction is even more important and worth further exploration. [PERSON] et al. (2019) introduced a model called the Multi-Task Matrix Factorized Graph Neural Network (MT-MF-GCN), which combines the GCN and matrix factorization modules. This model is used to predict both zone-based and OD demand simultaneously in ride-hailing services, and it demonstrates outstanding performance in real-world testing. [PERSON] et al. (2019), in contrast to previous studies on ride-hailing demand prediction that primarily focused on inflow or outflow demands of each zone, proposed a Conditional Generative Adversarial Network with a Wasserstein divergence objective (CWGAN-div) for predicting ride-hailing OD demand matrices. They utilize interpretable conditional information to capture external spatiotemporal dependencies, ultimately guiding the model to generate more precise results. RNNs are one of the most popular methods for predicting OD demand in ride-hailing services. However, due to the diversity of these networks, the question of which type is most suitable for this task still needs to be solved. Compared to road systems, the OD distribution in urban rail transit exhibits distinct characteristics, including high dimensionality and sparsity, posing significant challenges for data-driven prediction models. While existing research has made some progress in mining spatiotemporal features, current models still lack objectivity when considering prediction framework embedding, multi-source data fusion, and integration of spatiotemporal feature mining. [PERSON] et al. (2019) proposed a deep learning framework based on a multi-factor fusion channel-wise attention mechanism.