Research and Application of Intelligent Governance Technology for Non-Motor Vehicle Violations

With the rapid development of the economy and society, the number of motor vehicles and non-motorized vehicles has increased significantly, leading to a continuous rise in traffic accidents. Particularly, illegal behaviors involving non-motorized vehicles not only pose serious threats to the safety of riders and passengers but also frequently trigger traffic incidents. Concurrently, challenges such as insufficient allocation of grassroots traffic police resources, low digital intelligence levels in traffic management, ineffective control of visible violations, difficulties in on-site violation enforcement, and inefficient public education and penalty implementation urgently demand exploration of new management models. This paper aims to utilize artificial intelligence recognition technologies to achieve precise identification and automated judgment of non-motorized vehicle traffic violations. By analyzing road surveillance video data and non-motorized vehicle capture data, the proposed system implements non-motorized vehicle target detection, rider attribute recognition, facial comparison, license plate recognition, and illegal behavior identification to accurately determine violators’ identities. Furthermore, it enhances the speed, precision, and effectiveness of public education for violators through comprehensive measures including multi-channel interventions, advisory reminders, and hierarchical management protocols.