Accurate multi-step traffic-flow forecasting requires dynamic spatial modeling and adaptation to heterogeneous temporal and node-level patterns. This study proposes FAMoE-ST, a hierarchically frozen attention network with hybrid-memory experts. Historical flow, temporal context, and node identity are encoded as sensor tokens and processed by a six-layer Transformer initialized from GPT-2. To avoid dependence on arbitrary sensor indexing, the causal mask is replaced by all-to-all bidirectional spatial attention and every sensor uses the same GPT position ID. The lower four blocks are frozen, while the upper two blocks are adapted and their feed-forward networks are replaced by four-expert modules. A linear router is fused with a 32-slot memory router; each token retrieves four slots and activates two experts. With 12 observations predicting the next 12 steps, FAMoE-ST achieves MAE/RMSE/MAPE of 18.55/30.35/12.91% on PEMS04 and 14.57/24.08/9.71% on PEMS08. Relative to ST-LLM, MAE decreases by 6.97% and 7.39%, respectively. Component and capacity-matched controls support the roles of restricted adaptation, sparse expert capacity, and memory routing. A separate PEMS04 initialization control finds no advantage from GPT-2 pretraining over random initialization; the contribution is therefore attributed to the proposed structural adaptation rather than to transferred linguistic knowledge.