## 6 Discussion and Conclusions
Additionally, we should propose a method covering traffic flow prediction with travel time estimation, origin-destination with graph data, or traffic flow prediction with the mobility on demand (MOD). Moreover, we should increase the evaluation matrices to validate the results from different angles. In this research article, our finding are to predict traffic flow, to reduce traffic congestion. Our model predicts accurately compared to previous methods, but in real-life practice, we still have some gaps. For example, traffic congestion creates a deadlock in traffic flow, traveling waiting time increases, the health of the public is psychologically affected by the waiting on the road, environmental pollution increases, and transportation fails to deliver goods on time. We can use artificial intelligence technologies to reduce the traffic congestion and reduce the cost of traffic congestion. **Major Concern of Traffic based on Artificial intelligence and Deep Learning Methodology. ** Our proposed methodology is based on traffic AD terminology and refers to artificial intelligence (AI) and deep learning (DL) applications. We can apply traffic infrastructure traffic AD due to its capabilities to collect traffic data, analyze it, and create solutions. The traffic optimization problem is solved with government bodies, universities, and different organizations. Traffic AD is still experimental. Traffic AD systems currently offer cities the authority to enhance traffic management and monitoring of traffic volume. A secure, stable traffic AD can be able to monitor traffic flow in the future autonomously. Traffic AD operates by collecting information from related traffic flow systems that deliver input on live traffic or historical traffic behavior. It uses deep learning approaches to process, understand, and learn about traffic infrastructure to comprehend this unstructured traffic volume. To solve traffic congestion, the AD then uses these observations. Traffic AD systems, including photographs and videos, are proficient in processing and analyzing various data kinds. Traffic AD systems, aided by computer vision techniques, can identify objects from traffic cameras in images and videos by breaking them down into characteristics and matching them to modules like \"car,\" \"bike,\" or \"pedestrian.\"  
Objects classifications and detection can differentiate between cars and pedestrians, between unlike vehicle kinds, and between buildings and living beings. Traffic AD can count vehicles, assess and re-route traffic accordingly if a road is congested. At a certain point, traffic AD will begin detecting trends to well understand the traffic environment after accumulating enough data. It can learn to distinguish between incidents, such as a rush-houror car accident, that may cause congestion. It may conduct the predictive investigation and use the knowledge to enhance traffic movement as traffic AD discovers at which times rush hour occurs. **Use Cases of Traffic AD. ** We can practice traffic AD to enhance the traffic flow and benefits for drivers as follows. **Traffic Patterns. ** Traffic AD systems can detect traffic trends using predictive analytics and avoid or mitigate road congestion before it occurs. Through their ITS, smart cities may incorporate a traffic AD system, or construct the AD into their advanced traffic management system (ATMS). **Traffic Lights. ** Traffic AD systems can optimize traffic lights and reduce intersection waiting times. In images from traffic cameras, the AD detects cars. The traffic data are sent to a control center, where algorithms analyze traffic compactness. It can go through traffic lights to re-route traffic flow, based on real-time information, if the system detects congestion. **Improved Safety. ** Emergency services and public transport can be made safer and more efficient by traffic AD systems. Traffic AD differentiates between road user types and can accordingly prioritize the flow of traffic.