With the continuous expansion of comprehensive road transport networks in major cities, accurate traffic flow prediction—an essential component of intelligent urban transport systems has become increasingly challenging. Spatio-Temporal Graph Neural Networks (STGNNs), which combine time-series models like the Transformer with spatial relationship learning models such as Graph Neural Networks (GNNs), have shown significant promise in addressing this challenge. However, they still face issues like insufficient exploitation of long-term dependencies, high structural and computational complexity, and underutilization of periodic patterns in traffic data. To address these limitations, we propose LL-MLP, a spatio-temporal model based on an encoder-decoder architecture. Our approach extends the look-back window to better capture long-term dependencies, introduces learnable parameter matrices to enhance spatial information extraction, and incorporates periodic information as a dynamic covariate to further leverage temporal patterns. Extensive experiments on four real-world traffic datasets demonstrate that LL-MLP achieves performance on par with or exceeding that of existing model architectures.