## 2 Related Research  
This section discusses related research in the field of traffic flow optimization. A study by [PERSON] and [PERSON] [12] focuses on determining the queue model and performance measures of a motorcycle parking area during rush hours at Hanoi University of Science and Technology (HUST), specifically the D3-D5 parking lot. Data collection is done using the observation approach, which involves recording the number of arrivals and service time. The arrival distribution and service time distribution are tested using the one-sample Kolmogorov-Smirnov test with SPSS Statistics. The analysis of the collected data reveals that the arrival distribution follows a Poisson distribution, while the service time distribution follows an exponential distribution. By applying Kendall Notation and considering the results of the data analysis, the queue model for the parking lot is determined to be (M/M/4): (FIFO/\(\infty\)/\(\infty\)). This notation indicates that the arrivals and service times both follow the Markovian process; the system has four servers, and the queue operates on a first-in, first-out (FIFO) basis with infinite queue capacity and infinite population capacity. The proposed queue model aligns well with the actual observed queue model at the D3-D5 parking lot. This finding provides valuable insights into understanding the queuing behavior and performance of the motorcycle parking area during rush hours at HUST. The research of [PERSON] et al. [13] proposes the development of a system that allows the visualization of information posted on social networks about traffic incidents. Feature engineering methods, such as vector counting and TF-IDF, were applied to process tweets into structured data. Machine Learning models were created for traffic-related tweet classification using SVM, Naive Bayes, Random Forest, and XGBoost. The prediction models resulted in a classification model that detects incident or non-incident tweets and a categorization model that determines the type of incident (accident, hazard, or obstacle). This system has advantages, such as speeding up the detection and visualization of traffic incidents, which can significantly help the country's traffic authorities and the public. [PERSON] et al. [14] present a new queuing model with multiple servers aimed at optimizing traffic signals to enhance the sustainability of urban mobility. The model focuses on analyzing the queue information for different movement patterns based on the arrival rate of cars on each road at the intersection. By utilizing queuing theory concepts, the collected data is processed and evaluated. The performance metrics considered in this proposed model include arrival rate, waiting time, average number of cars in the queue, and intersection utilization. These metrics are analyzed and assessed using ground truth data to determine the effectiveness of the queuing model. The numerical results obtained from the analysis are visually presented and indicate that the proposed approach of the queuing model reduces delays by improving the throughput of the intersection. Consequently, this leads to smoother traffic flow and decreased congestion for users of the Giporoso intersection in Kigali. The findings highlight the potential benefits of implementing the proposed queuing model in optimizing traffic signal operations and enhancing urban mobility sustainability. [PERSON] et al. [15] introduce the concept of a birth and death process to analyze vehicle behavior and propose a traffic model based on queuing theory to simulate road traffic. They specifically focus on two scenarios: the steady state and the congested state of the traffic system. In the steady state, they obtain statistical variables such as the number of vehicles and the waiting time for vehicles to pass through a specific section of the road. These variables provide valuable information to drivers and contribute to their decision-making process.