Vehicle Identification for Performance Evaluation of LTE-Based Traffic Management against AI and Conventional Techniques

Abstract

These most industrialized countries now focus on traffic management because traffic numbers have risen together with global traffic expansion. The technological progression resulted in a modern traffic management solution which allows counting vehicles as well as monitoring their speeds for enhanced transportation planning systems. The established system has lowered the incidence of accidents stemming from deteriorating traffic conditions. Manual road traffic surveys have been ongoing for many years since automation installations proved difficult to implement. Wireless sensor networks stand as an essential method for realizing applications which include traffic monitoring in the real world. Intelligent traffic management solutions have become essential because of both rapid urban growth and explosive vehicle traffic increases. The implementation of traditional traffic control systems using fixed-timer mechanisms cannot react to current congestion which results in inefficient traffic operation and worsened urban transport problems. The paper evaluates and compares the performance of real-time traffic management through LTE technology with conventional approaches and AI-powered systems for traffic control systems. A combination of wireless sensor networks (WSNs), ultrasonic sensors as well as LTE communication powers the proposed system to conduct immediate vehicle identification and active traffic signal modifications. The LTE-based solution operates with low-latency signal adjustment capabilities because it delivers a response time of 0.35s that surpasses both AI-based systems by 5× and Google API-based solutions by 10×. Vehicle traffic congestion decreases by 30% under this system thus demonstrating better performance than YOLO-based AI models by 50% and making the system three times more effective than traditional methods. Real-time decisions that depend on vehicle density estimation show improved performance at a 98% accuracy rate because this leads to optimal traffic flow as well as decreased emissions and faster emergency responses. This research shows LTE-based adaptive signal control emerges as the preferred technology for intelligent transportation system optimization. It delivers scalable real-time operational performance at a reduced cost to build next-generation ITS. This article functions as a research starter for scientists constructing traffic management solutions for safety.