In order to solve the problems in the transfer process connection and collaboration of metro-bus two-layered network faced by the passengers making route selection in the urban transportation network, such as the far distance between some transfer stations, the unclear connection orientation and the imbalance between supply and demand in local transfer, a collaborative optimization method for urban traffic networks based on two-layered complex networks was presented. Firstly, the logical network topology method was applied to the topology of the urban transportation network, and the metro-bus two-layered network model was established by the complex network theory. Secondly, with the transfer station as research object, a node importance evaluation method based on K-shell decomposition method and central weight distribution was presented. This method was able to realize coarse and fine-grained divison and identification of metro and bus stations in large-scale networks. And a collaborative optimization method for two-layered urban traffic network with mutual encouragement was presented, that is to say the method in the complex network theory to identify and filter the node importance in network topology was introduced to the two-layered network structure optimization. The two-layered network structure was updated by identifying high-aggregation effects and locating favorable nodes in the route selection to optimize the layout and connection of stations in the existing network. Finally, the method was applied to the Chengdu metro-bus network, the existing network structure was optimized to obtain the optimal optimized node location and number of existing network, and the effectiveness of the method was verified by the relevant index system. The results show that the global efficiency of the network is optimized after 32 optimizations, and the optimization effect of the average shortest path is 15.89% and 16.97%, respectively, and the passenger transfer behavior is increased by 57.44 percentage points, the impact on the accessibility is the most obvious when the travel cost is 8000-12000 m with the optimization effect of 23.44% on average. At the same time, with the two-layered network speed ratio and unit transportation cost introduced, the response and sensitivity difference of the traffic network to the collaborative optimization process under different operational conditions are highlighted.