To capture the complex spatiotemporal dependencies in pedestrian trajectories， this paper proposes a trajectory prediction model that combines a bidirectional temporal learning module and a spatiotemporal interaction learning module. The model leverages bidirectional temporal feature modeling and self-supervised learning to extract spatiotemporal interaction features. In the bidirectional temporal learning module， a bidirectional temporal convolutional network is utilized to simultaneously model both historical and future trajectory information， enabling the capture of dynamic trajectory changes. In the spatiotemporal interaction learning module， the test-time training （TTT） layer is employed with a self-supervised learning mechanism to dynamically adjust feature representations during the inference stage， thereby modeling spatiotemporal correlations. Finally， an adaptive fusion strategy is used to combine the features extracted by the two modules， focusing on key features while suppressing irrelevant information. Experimental results demonstrate that the proposed model achieves competitive prediction performance on the ETH and UCY datasets.Highlights:1.Proposes a dual-branch framework fusing BiTCN bidirectional temporal modeling and TTT-based spatiotemporal self-supervised learning for multi-pedestrian trajectory prediction.2.BiTCN with bidirectional convolutions captures both historical and future trajectory dependencies， addressing incomplete feature representation from unidirectional temporal modeling.3.First introduces test-time training （TTT） layers to dynamically adapt feature representations via inference-time self-supervision， effectively modeling complex dynamic pedestrian interactions.4.Achieves state-of-the-art ADE/FDE performance on ETH and UCY benchmarks， outperforming mainstream baselines while maintaining lightweight design with only 22.4% parameters and 9% inference time of Social-LSTM.