# Deep Learning for Predicting Traffic in V2X Networks
## 1 Introduction  
In telecommunications, 5G networks and beyond have high requirements in terms of delays, security, reliability of connections, etc. As the number of devices in the telecommunications network increases, leading to a large increase in information flow, the challenge is to find more efficient ways to manage and control the network. As the number of devices in the telecommunications network increases, leading to an extensive increase in the flow of information, the challenge is to find more efficient ways to manage and control the network. Reliable, intelligent methods are required for 5G for adapting network protocols and resource management for different facilities in various environments. Artificial intelligence (AI), defined as any method or system that recognizes its environment and performs actions that increase the chances of success in achieving a particular goal, is a practical solution for developing new complicated communication systems [1, 2]. All aspects that make machines smarter belong to the broad field of AI. An AI system that can learn autonomously based on an algorithm is called machine learning (ML). ML is an advanced subfield of AI that produces intelligent systems that continuously learn and become smarter without human intervention. Since intelligent behavior requires extensive knowledge, ML is used in most existing AI applications. ML then uses these data to provide intelligent information or predict a specific outcome when new entries are introduced. ML is well-suited for solving complex problems such as optimizing wireless networks or detecting attacks on the network. Large datasets can be processed using an ML technique called deep learning (DL). DL has recently gained popularity because it can quickly solve some types of difficult problems in numerous applications and can use both supervised and unsupervised learning algorithms. It depends on multilayer neural networks (MLNN) and methods that handle extensive datasets, and some of these methods outperform traditional artificial neural networks (ANNs) in processing the previous information. Current DL advances promise to solve highly complex, previously unsolvable problems. DL algorithms are important forpredicting traffic behavior because they can learn from large amounts of data and identify patterns more accurately than other methods. Potential traffic predictions enable solutions to improve quality of service (QoS) before it drops. Because DL uses historical data to improve its decision making and achieve higher accuracy, it can be used for predictive analytics. A more secure network is enabled by predicting a potential massive occurrence of data streams at unusual times, which can be best described as data stream-based intrusion. Predicting such large data streams can also eliminate the risk that could affect the performance of the IoT system [3; 4; 5]. Internet of Things (IoT) solutions for infrastructure, applications, and security are creating new experiences and enabling more advanced operations based on acquired data at the intelligent edge. The IoT enables connectivity between daily things via sensors, and information can be collected and shared over the Internet. As the number of IoT devices increases, leading to an increase in applications, the amount of data collected will also increase, and IoT services will be developed for operators in various sectors. IoT systems connect with other devices and collect huge amounts of data every day. In addition, IoT systems can be programmed to start specific activities depending on predefined conditions or reactions to collected data from various applications. Moreover, human intervention is required to investigate the aggregated information, produce sensitive information, and create intelligent implementations. Furthermore, they can make decisions and learn from the data they collect. For advanced automated deployments, smart IoT devices must also be equipped with the capabilities to allocate resources, communicate, and provide network services [6; 7].