This paper proposes an adaptive agent model with a hybrid routing selection strategy for studying the road?network congestion problem. We focus on improving those severely congested links. Firstly, a multi?agent system is built, where each agent stands for a vehicle, and it makes its routing selection by considering the shortest path and the minimum congested degree of the target link simultaneously. The agent?based model captures the nonlinear feedback between vehicle routing behaviors and road?network congestion status. Secondly, a hybrid routing selection strategy is provided, which guides the vehicle routes adapting to the real?time road?network congestion status. On this basis, we execute simulation experiments and compare the simulation results of network congestion distribution, by Floyd agent with shortest path strategy and our proposed adaptive agent with hybrid strategy. The simulation results show that our proposed model has reduced the congestion degree of those seriously congested links of road?network. Finally, we execute our model on a real road map. The results finds that those seriously congested roads have some common features such as located at the road junction or near the unique road connecting two areas. And, the results also show an effectiveness of our model on reduction of those seriously congested links in this actual road network. Such a bottom?up congestion control approach with a hybrid congestion optimization perspective will have its significance for actual traffic congestion control.