Swarm Learning-Based Dynamic Optimal Management for Traffic Congestion in 6G-Driven Intelligent Transportation System

As city boundaries expand and the vehicles continues to proliferate, the transportation system is increasingly overloaded, greatly increasing people's commuting burden and extending the resulting negative effects to all areas of work and life. It is a big issue that needs to be solved urgently. However, due to the development of infrastructure and technologies in 6G-driven Intelligent Transportation Systems (ITS), it becomes possible to alleviate urban congestion. Existing solutions either optimize the path planning of each vehicle, or only focus on solving the problem of resource allocation of a single road, neither can take advantage of self-organizing networks and easily fall into local optimum. Combining the above reasons, we propose the Direction Decide as a Service (DDaaS) scheme. First, it contains a novel three-layer service architecture based on Swarm Learning (SL), which enables orderly transmission of traffic data and control instructions and protects user privacy. Second, an improved local model and aggregation method is incorporated into DDaaS, which enables to make accurate predictions when the road resources at a single intersection are insufficient. Third, we propose a dynamic traffic control algorithm to provide signal light switching decisions for rapidly changing ITS. Finally, constructing an urban road simulation experiment combined with SUMO, we prove that DDaaS can reduce traffic congestion effectively and has significant advantages compared to other schemes.