## 1 Introduction
### Traffic Management Systems
These methods use neural networks and fuzzy logic to model and visualize traffic flows, aiding in better traffic management decisions [26]. AI's application in traffic management aims to overcome congestion, safety concerns, and environmental impacts. Deep Reinforcement Learning (DRL) is one approach explored to optimize traffic signal configurations, improving traffic throughput and alleviating congestion [27; 28]. AI's potential to revolutionize urban traffic management lies in its ability to provide real-time, adaptive solutions to complex traffic issues [29; 30]. Another approach used in the literature is autonomous vehicles (AVs), which aim to reduce traffic congestion, air pollution, and fuel consumption [31]. One innovative approach to managing AVs is converting their movement into a simulated train model on a virtual track. This method, employing a \"particle swarm\" optimization algorithm, aims to improve road safety and the integration of AVs with human-driven vehicles, ensuring efficient traffic management [32]. Additionally, a proportional-integral-derivative controller with an adaptive slide surface was proposed for trajectory tracking of fully actuated vessels, showcasing the potential of advanced control algorithms in AV management [33]. In combining AVs and human-driven vehicles, mixed traffic flow remains a significant research focus. A new cellular automata model revealed that a 30% AV penetration rate significantly increases traffic flow, though benefits plateau between 40% and 60%. Equipping human-driven vehicles with communication devices enhances traffic efficiency at lower AV penetration rates [34]. Introducing AVs as bus bays can improve these metrics, especially in heavy traffic conditions [35]. Optimized route selection and traffic control using deep neural networks, aided by Golden Levy optimization, can further enhance AV traffic management [36]. Simulation studies using actual traffic data in mixed traffic conditions indicate that AVs can adapt to different driving styles, improving road safety and efficiency [37]. Employing real-time data, ML-based traffic management strategies show promise in reducing congestion and emissions, offering significant benefits for urban transportation systems [38]. Despite the potential of AVs to transform urban traffic, low AV adoption rates pose challenges, highlighting the need for further research to address these limitations and ensure seamless integration into existing traffic systems [39]. Another proposal for public traffic management is the IoT approach. [PERSON] and [PERSON] [40] use stationary sensors and the k-means clustering algorithm to classify road segments based on traffic density and speed. Efficient traffic management in smart cities can be achieved by pre-estimating vehicle numbers at crowded junctions using image processing and IoT technology, which conveys real-time vehicle counts to control centers [41]. A dynamic IoT-based traffic management system using ultrasonic sensors regulates traffic light durations based on real-time congestion levels. This system was developed in the following three phases: simulation, IoT system development, and hardware implementation [42]. The rise of IoT offers new opportunities for inventions, such as an energy-saving electrical device surveillance and control system. This system uses wireless technology for small areas and wired configuration for larger areas to improve fault detection in lighting appliances [43]. An image processing system can calculate fines and connect drivers with the traffic system via IoT technology [44]. A smart traffic management system using IoT and solar-powered streetlights can reduce energy wastage in traffic monitoring. This system adjusts signal timings based on traffic densities and activates streetlights only when needed [45]. Intelligent traffic management systems using IoT and Long Range (LoRa) technology can monitor and control traffic flow, reducing congestion and emissions while providing real-time air quality data [46].