Reinforcement Learning Assisted Triboelectric Self‐Regulating Intelligent Transportation System

Abstract Real‐time vehicle detection and adaptive traffic signal timings control are essential for intelligent traffic management systems, aimed at improving road utilization and enhancing traffic safety. However, conventional traffic monitoring equipment often requires additional power sources and complex deployment. Therefore, this study proposes an intelligent speed bump (ISB) that integrates a multi‐functional triboelectric nanogenerator (M‐TENG) with a bidirectional gear transmission structure and a contact‐sliding‐separation mode. This design can achieve self‐powered vehicle flow perception and provide sensing signals for the dynamic regulation of real‐time traffic flow, thereby alleviating traffic congestion. Furthermore, the process of vehicle passage through the ISB and data collection is simulated on the simulation of urban mobility (SUMO) platform, and traffic signal control is optimized using the Deep Q‐Network (DQN) algorithm. Experimental results show that the proposed ISB‐based intelligent traffic management system reduces average vehicle waiting time and queue length by 97.7% and 71.4%, respectively, significantly alleviating traffic congestion. This study overcomes the dependency of intelligent traffic integration devices on external power sources, providing new insights for the sustainable development of intelligent transportation systems.