## 1 Introduction
### Traffic Management Systems  
Some studies collectively contribute to advancing traffic management systems through innovative sensor technologies, real-time data acquisition, and intelligent algorithms. [PERSON] et al. [16] introduce a new algorithm for optimizing traffic sensor networks. The algorithm considers multiple types of sensors and constructs an output matrix to enhance system observability. By integrating different sensor types, the system can achieve optimal sensor placement. The method was validated using an urban freeway model, demonstrating comprehensive coverage and strong representativeness. A dual-subsystem framework for traffic management was proposed by [PERSON] and [PERSON] [17], collecting real-time traffic data, including vehicle count, speed, classification, and environmental data. The Control Station processes these data, enabling macroscopic and microscopic traffic flow evaluation and automated response to traffic issues. The system offers a cost-effective solution for real-time traffic monitoring. Furthermore, integrating the Internet of Things (IoT) into traffic management systems aims to address issues such as traffic congestion, rule violations, and accidents. [PERSON] et al. [18] develop an IoT-based Smart Traffic Controlling System to monitor and resolve traffic problems by collecting real-time data and communicating it to authorities. The system enhances traffic signal operation, reduces idle times at red lights, and improves overall traffic safety and management. [PERSON] et al. [19] propose a new model to predict traffic patterns and fill data gaps to make accurate traffic predictions even with limited data, enhancing intelligent transportation systems. Their study also integrates GPT-4 for user interaction, providing personalized and contextual responses. Another work [20] extensively reviews current traffic flow sensor technologies by detailing various sensors' operational strengths and limitations, aiding in informed decision-making for specific applications. Furthermore, it discusses integrating alternative data sources like Wi-Fi, Bluetooth sensing, and connected vehicle data, highlighting their growing role in modern traffic management systems. Advances in Light Detection and Ranging (LiDAR) technology have enabled it to detect vehicles, cyclists, and pedestrians at intersections. A methodology to transform LiDAR data into Purdue Probe Diagrams is presented by [PERSON] and [PERSON] [21], allowing for performance measures such as arrivals on green, split failures, downstream blockage, and control delay level of service. A case study demonstrates the reliability of these measures, showing close agreement with connected vehicle data, thus validating the methodology. Artificial intelligence (AI) is revolutionizing traffic transport, mainly through advancements in computer vision and image processing. To address these challenges, advancements in intelligent vehicle technologies, such as sensors and communication networks, offer transformative potential [22]. For instance, platooning for autonomous vehicles, which travel in groups at high speeds with minimal distances, aims to enhance road traffic efficiency. This requires precise vehicle positioning, which can be improved using images captured by onboard cameras and Global Positioning System (GPS) data [22]. Innovative systems utilize ML algorithms for route optimization, cargo volume forecasting, predictive fleet maintenance, and real-time vehicle tracking [23]. AI methodologies are anticipated to assist aviation personnel in traffic, potentially leading to single-pilot operations and AI-based air traffic management [24]. AI-based traffic surveillance technologies already assist in real-time traffic incident detection and alert systems using computer vision [25]. AI simulation techniques have been applied to assess pedestrian flow at intersections, helping predict traffic capacity and optimize signal control.