Abstract: To address the insufficient prediction accuracy of single models in complex environments,this study proposes a novel prediction architecture named Channel Attention-enhanced Dual-stream Memory Network(CA-MLNet),which integrates improved state space models with memory networks.The core innovations of this work include:(1)Reconstruction of the Structured State-Space Model,it enhances its spatial feature selection capability in dynamic environments;(2)Innovative integration of xLSTM's mLSTM module as an auxiliary temporal modeling unit,it establishes a dual-branch feature fusion architecture.The improved SSM module effectively captures long-range spatial dependencies,while the mLSTM module enhances local temporal feature extraction through exponential gating mechanisms.These components achieve complementary advantages via adaptive weight fusion mechanisms.Experimental results on the GeoLife GPS Trajectories dataset demonstrate that the proposed model achieves a prediction accuracy of 99.01%,representing an 8.18% improvement over the baseline Mamba model and a 3.14% enhancement compared to the xLSTM architecture.Ablation experiments verify that the SSM module modification contributes 43.6% to spatial feature selection accuracy,with dual-module collaboration reducing trajectory offset errors.This approach provides a high-precision solution for intelligent traffic early warning systems.

Key words: Channel attention-enhanced dual-stream memory network(CA-MLNet), Trajectory prediction, Exponential gating mechanisms, Adaptive weight fusion, Smart security