Accurate traffic forecasting not only improves the efficiency and safety of the traffic system， but also promotes the sustainable social and economic development. Although a large number of studies have been devoted to modeling spatiotemporal correlation， existing methods still have significant limitations： most models tend to collectively predict the traffic flow of all regions in all time periods， ignoring spatio-temporal heterogeneity， especially the impact of the traffic status of the current region on the future traffic status of related regions. To address this problem， a Time-Interdependency-aware Dynamic Bayesian Network for traffic prediction （TIDBN） method was proposed. Using pre-trained modules， TIDBN employed a time-varying dynamic Bayesian network to capture the complex temporal relationships in time-series data arising from simultaneous and lagged effects. To further improve its ability to capture spatio-temporal correlation， a spatio-temporal attention mechanism was introduced for in-depth analysis. Subsequently， a Graph Convolutional Network （GCN） was utilized to model the spatio-temporal topological structure， generating more accurate traffic predictions. The experimental results show that TIDBN performs excellently on two real traffic prediction tasks， especially for 1-hour prediction. On the PeMS-BAY dataset， the Mean Absolute Error （MAE） of TIDBN is 4% lower than that of the second-best baseline method.