## 2 Related Work
Their algorithm was compared with five different distributed machine learning algorithms that were built by Apache Spark where these algorithms utilized the same dataset. The proposed algorithm outperformed the other algorithms in terms of average absolute relative error. To implement a traffic flow prediction model that satisfies real world application requirements, [PERSON], and [PERSON] focused on improving the prediction model features by implementing a hybrid deep learning model that combines Graph Convolutional Network(GCN) and the deep aggregation structure to predict traffic flow [37]. This approach used parallel training that combined several edge devices with a central system to improve the accuracy and efficiency of the system. To improve the performance of the model during different lengths of time lag, a refinement learning approach was applied. The proposed system used traffic data obtained from PeMS and was evaluated in terms of MAE, R square, and RMSE. In [38], the authors propose hybrid designs that integrate Convolutional Neural Networks (CNNs), Graph Convolutional Networks (GCNs), and transformer-based models for large-scale road networks. These hybrid models effectively combine spatial and temporal analyses, leveraging attention mechanisms to dynamically capture dependencies in traffic data. The study highlights significant improvements in prediction accuracy and scalability, demonstrated through evaluations on datasets such as PeMS-Bay and Metr-La, with models like SAGCN-SST and GMAN showing notable performance. [PERSON] et al. [39], present the Improved Arithmetic Optimization Algorithm-based Deep Learning Traffic Congestion Control (IAOADL-TCC) model, which employs a hybrid CNN-LSTM architecture to optimize traffic flow prediction. The model applies a hyper-parameter tuning approach using arithmetic optimization to enhance the CNN-ALSTM framework, achieving a notable average accuracy of 98.03% on a Kaggle road traffic dataset. The IAOADL-TCC model outperformed previous approaches, including UAV-CNN, SVM, and fuzzy logic models, by effectively capturing spatio-temporal patterns. Another study in [40], explores the development of traffic flow prediction methods, transitioning from statistical models like ARMA to advanced deep learning approaches such as LSTM, CNN, and GCN. Hybrid models, including IGA-LSTM and DAFF-Net, have improved effectiveness by capturing spatio-temporal patterns and addressing inefficiencies. GCN-based models, such as FAST-GCN, effectively manage non-Euclidean traffic data, leading to notable improvements in accuracy. However, challenges persist in handling sparse data and ensuring real-time adaptability. [PERSON] came up with a decentralized deep learning method in order to come up with scalable and real-time feedback of traffic flow predictions [41]. They used dataset obtained from Caltrans Performance Measurement System (CPMS) in California where data of 48 days from 51 different locations were used to train their model and the next 12 days were selected for validation. To avoid the class imbalance problem of their dataset where heavy traffic was significantly lower than the light traffic, they obtained a regularized euclidean loss function. CNN and LSTM were used to build their decentralized deep learning architecture. The accuracy of both models was high, but it decreased during rush hours due to the unbalanced dataset. An improved hybrid traffic flow prediction was proposed in [42]. The hybrid model combined a decomposition and prediction process to predict traffic flow. The process started as follows: a decomposition ensemble empirical mode was utilized to decompose an original nonlinear and complex highway traffic flow data. Then, a new reconstructed component was obtained from an improved weighted permutation entropy (IWPE). To create the reconstructed component, a gray wolf optimizer was used to optimize the least-squares support vector machine. The proposed model provided useful traffic flow information based on their analysis and results.