Simulation environments featuring data-driven traffic agents have become essential tools for training and assessing autonomous driving (AD) systems, offering a safer and more cost-effective alternative to real-world testing. However, prevalent data-driven simulations often focus on modeling individual traffic participants or indirectly representing their joint future motion distribution in a latent space. These approaches may encounter challenges due to the combinatorial explosion in the number of agents or may lack explainability. To tackle these issues, we introduce a novel learning-based traffic simulation framework aimed at directly modeling joint behaviors in the 2D plane. Our framework utilizes a graph-based scene representation to accommodate an arbitrary number of agents and road elements. It directly generates multiple joint goal sets for all agents in a given scenario and subsequently produces reference lines for each goal set. To enhance interaction with the ego, we design a predictive step simulation module to forecast the ego’s short-term motion and prevent collisions between the step rollout and the prediction during reference line tracking. Experimental results on the large-scale Waymo Open Motion Dataset validate that the proposed model can directly generate multiple socially consistent goal sets, showcasing enhancements in collision avoidance, road compliance, and overall realism. This approach shows promise in advancing the development and deployment of AD technology in a safer and more efficient manner.