## 2 Related Work  
Distributed machine learning architecture has attracted researchers in the past due to its high scalability, and reliability. However, very limited papers have discussed traffic flow prediction while using distributed machine learning and parallel analysis to deal with big traffic data challenges. Providing an optimal traffic flow prediction for a large-scale traffic network where numerous vehicle detection systems are geographically installed on highways to record traffic and collect its data is challenging. Recent studies have investigated diverse Artificial Intelligent (AI) and deep learning techniques for improving traffic flow prediction and event detection in vehicular networks. [PERSON] et al. [32], presented a comprehensive analysis of machine learning applications in vehicular networks, including mobility management, handover optimization, and predictive routing to address dynamic network challenges in high-mobility scenarios. [PERSON] et al. [33], explored AI and ML-based incident detection systems in road transport, highlighting significant advancements in real-time vehicle monitoring, route optimization, and predictive fleet maintenance, all of which enhance road safety. [PERSON] et al. [34], proposed a soft GRU-based recurrent neural network model for predicting congestion in smart cities, utilizing historical traffic data and sensor inputs to enhance traffic flow and reduce congestion. These studies demonstrate the growing role of AI-driven methodologies in optimizing traffic management and improving transportation efficiency. A distributed machine learning method was developed in [35] to predict traffic flow and deal with big traffic data challenges. A deep convolutional neural network model was implemented to be used in their parallel training approach. The framework was implemented by using Apache Spark cluster to perform parallel distributed machine learning. Real traffic data was obtained from California Department of Transportation Performance Measurement System (PeMS) to be utilized for this study. The main dataset, collected from 12 highways, was decomposed into sub datasets to be utilized by different Spark nodes (machines) to perform parallel computations. 9 months of traffic data records in 2016 were used for training and one month was used for testing. The models trained on the sub datasets and provided local training parameters that were sent to the master node to average these parameters and provide global learning parameters. These global learning parameters were sent back to the slave nodes as sub-sequence iteration processes. This process continued until the best accuracy was achieved. The performance of the model was evaluated by using MSE, RMSE, and MAE in different time intervals. Based on the results, the proposed model improved the efficiency of the training process. A Distributed Automatic Long Short-Term Memory Customization Algorithm (DALC) for predicting highway traffic speed flow was developed in [36]. The authors started with designing the algorithm for a single detector based on [PERSON] decision process formulation to define the best two hyperparameters manually. The two hyperparameters were the number of hidden layers and epochs. Based on the [PERSON] decision process formulation, they designed the ALC algorithm for a single detector. Then they proposed that each detector should have its own algorithm that can work in parallel due to different traffic speed patterns. After analyzing five weekdays of traffic speed records starting from 16 October 2017, the traffic patterns in the morning were different than the traffic patterns in the night. So, they proposed that each detector should have two models. They utilized a dataset obtained from California Department of Transportation Performance Measurement System (PeMS) that was collected from 60 detectors. They utilized Apache Hadoop YARN and Spark to build a private cluster that consisted of 30 nodes where each node had two DALC algorithms.