Research on multi-objective optimization and privacy protection of road transport management based on intelligent technology

Abstract

With the acceleration of urbanization and the promotion of the “dual carbon” goal, the road transport system is facing the triple challenges of efficiency bottlenecks, excessive carbon emissions, and data security risks. In view of the shortcomings of the existing research in dynamic response, multi-objective collaboration and privacy protection, this paper proposes a three-in-one intelligent management framework: (1) construct a real-time dynamic path optimization model based on Deep Reinforcement Learning (DRL), and realize the precise regulation of traffic flow through multi-source data fusion and adaptive reward mechanism; (2) Design a multi-objective optimization model integrating carbon trading mechanism to quantify the synergistic relationship between transportation efficiency, carbon emissions and economic costs; (3) Develop a distributed data management framework based on blockchain, and use zero-knowledge proof and smart contract technology to protect user privacy. The peak simulation experiment based on the fifth ring road section of Beijing shows that the proposed method reduces the average traffic time by 18.7%, the carbon emission by 23.5%, and the risk of data leakage by 76% compared with the traditional algorithm. This study provides theoretical and technical support for the construction of a safe, efficient and low-carbon intelligent transportation system.