Experimental Verification and Efficacy Analysis of Multi-Technology Integration in Intelligent Transportation

Abstract

With the acceleration of urbanization and the surge in motor vehicle ownership, traditional traffic management models are facing numerous challenges such as low efficiency and delayed response. Relying on cutting-edge technologies including deep learning, computer vision, and edge computing, intelligent traffic monitoring and management technologies have driven innovations in core functions like traffic state perception, traffic flow prediction, and incident detection. This paper systematically sorts out the mainstream technology systems in the field of intelligent traffic, focusing on analyzing the principles and characteristics of key technical methods such as multi-category vehicle detection, small target tracking, edge-cloud collaborative monitoring, intelligent traffic signal automation, and traffic noise analysis. It integrates experimental data from existing studies to conduct comparative analysis from the dimensions of datasets, evaluation indicators, and result effectiveness. Finally, it examines the core challenges in the current application of these technologies and puts forward prospects for future development directions, providing theoretical references for the optimization and upgrading of intelligent traffic systems.