A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction

Table 2

The performance of our model and other baselines on traffic flow prediction. Bold scores are the best and underlined scores are the second best.

T	5 min			10 min			15 min
Model	MAE	RMSE	VAR	MAE	RMSE	VAR	MAE	RMSE	VAR
MLP	3.4547	4.3616	0.5305	3.4033	4.4673	0.4902	3.7487	4.7843	0.4248
LSTM	3.2121	4.1408	0.5687	3.3041	4.2225	0.5526	3.3171	4.2376	0.5494
GRU	3.1250	4.1229	0.5659	3.3186	4.2368	0.5527	3.3297	4.2511	0.5479
GCN	4.8086	5.9925	0.0836	4.8028	5.9863	0.0855	4.8052	5.9799	0.0876
T-GCN	4.4467	5.4664	0.2541	4.5756	5.6031	0.2186	4.5271	5.6392	0.1879
Ours w/o gate	2.8311	3.7730	0.6380	2.9249	3.8593	0.6221	3.0161	3.9491	0.6049
Ours	2.8274	3.7697	0.6385	2.9168	3.8522	0.6234	2.9951	3.9268	0.6093