To address the difficulty in multi-scale collaborative modeling and the insufficient interaction continuity in traffic simulation systems， a multi-scale micro-meso-macro integrated traffic simulation framework was proposed. In the framework， with a core logic of data-driven processing， scale classification， policy execution， trajectory control， simulation feedback， a modular modeling architecture was constructed for multi-source information fusion and behavior policy decoupling； by establishing a mesoscale bridging layer， the bidirectional information propagation between micro behaviors and macro trends was achieved， and a Social Value Orientation （SVO） swarm Monte Carlo Tree Search （MCTS） cooperative decision-making mechanism was incorporated. At the same time， at the macroscopic level， a dynamic OD （Origin-Destination） matrix generation module was constructed to empower the simulation system with adaptive control capabilities to the temporal evolution of traffic flow. Experimental results demonstrate that the proposed method achieves substantial improvements in both behavioral rationality and scenario adaptability of the system while maintaining simulation efficiency. Compared with the traditional single-scale models and single-vehicle decision models without collaborative strategies， the method reduces the conflict rate by 19.8% on average， and increases the Throughput Ratio （TR） by 34.6% comprehensively， providing an effective technical path for intelligent coordination and resource optimization in complex traffic scenarios.

In order to solve the problem that pedestrian trajectory prediction research only focuses on interactive information of historical trajectories and ignores interactive information of endpoints， a pedestrian trajectory prediction model based on Graph Convolutional Network （GCN） and Endpoint Induction was proposed， named GCN-EI. Firstly， a classification method was employed on the training set to learn the weighted distribution of potential future endpoints for pedestrians. Subsequently， the possible endpoints were connected with their corresponding historical trajectories， and the interactive features of pedestrians were extracted over a longer time span by using the GCN with attention mechanism and endpoint conditions. Meanwhile， an individual feature module was used to extract the internal motion characteristics of pedestrians. Finally， the future trajectory of pedestrian was predicted by the temporal inference convolution. Test results on ETH and UCY datasets show that compared to STITD-GCN （Spatio-Temporal Interaction and Trajectory Distribution GCN） model， the proposed model has the Average Displacement Error （ADE） and Final Displacement Error （FDE） decreased by 4.5% and 5.0%， respectively； moreover， compared to PCCSNet （Prediction via modality Clustering， Classification and Synthesis Network） model using classification method， it has the FDE decreased by 9.5% .