A Spatiotemporal Heterogeneous Two-Stage Fusion Network for Traffic Flow Prediction

doi:10.12141/j.issn.1000-565X.240480

Abstract

Abstract:

In response to the existing traffic flow prediction studies that fail to fully integrate complex spatiotemporal correlations and heterogeneities, this paper designs a traffic flow prediction network based on grid data, namely the spatiotemporal heterogeneous two-stage fusion neural network marked as ST_HTFNN. This network employs a phased and hierarchical spatiotemporal feature extraction architecture, and adopts a new model where the static and dynamic feature extraction stages are serialized. In the static feature extraction stage, a novel Mamba-like linear attention (MLLA) block is introduced as a static heterogeneous fusion unit to achieve spatial correlation and heterogeneity fusion mining. In the dynamic feature extraction stage, a simple and efficient dynamic heterogeneous fusion unit is designed, and dilated convolution is combined with gating mechanisms to adaptively fuse and capture global and local spatiotemporal correlations and heterogeneities. Furthermore, to address the smoothing of road features during the deep convolution process for road-level traffic flow characteristics, a road feature enhancement module is designed to reconstruct and enhance road information. Finally, an external disturbance feature fusion module is designed to integrate the impact of external disturbance features on traffic flow prediction results. Experimental results on three real-world traffic datasets, namely BikeNYC, TaxiCQ and TaxiBJ, demonstrate that the ST_HTFNN model outperforms the existing benchmark methods, respectively with a decrease of 6.13%, 0.8% and 7.01% in the mean absolute error of prediction accuracy.

Key words: traffic flow prediction, grid data, spatiotemporal heterogeneity, dilated convolution, gating mechanism

CLC Number:

TP391

HOU Yue, YIN Jie, ZHANG Zhihao, LU Keke. A Spatiotemporal Heterogeneous Two-Stage Fusion Network for Traffic Flow Prediction[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(5): 82-93.

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模型		时间特性	空间特性	时空特性	多尺度特性	异质性
传统	HA	√
传统	ARIMA	√
2D CNN	ST-ResNet	√	√		√
2D CNN	DeepSTN+	√	√		√
CNN+RNN	Conv-LSTM	√	√
CNN+RNN	SA-ConvLSTM	√	√
3D CNN	ST-3DNet	√	√	√	√	√
	LMST3D-ResNet	√	√	√	√
	ST-3DGMR	√	√	√	√
	ST-3DMDDN	√	√	√	√	√
SSM	Mamba	√

模型	MSE	MAE	SSIM	PSNR
HA	3.146 3	33.918 0	0.747 8	31.923 8
ARIMA	2.047 4	25.216 9	0.849 9	33.352 2
Conv-LSTM	0.303 7	9.963 0	0.966 9	37.964 1
ST-ResNet	0.248 2	9.366 3	0.967 8	38.386 4
DeepSTN+	0.237 4	9.269 6	0.969 9	38.637 6
ST-3DNet	0.235 3	9.181 6	0.970 9	38.501 1
SA-ConvLSTM	0.212 2	8.396 8	0.976 5	39.314 8
LMST3D-ResNet	0.228 0	8.744 8	0.971 7	38.949 6
ST-3DGMR	0.228 2	8.629 2	0.973 1	39.039 7
Mamba	0.268 5	9.989 9	0.965 9	37.830 0
ST-3DMDDN	0.214 4	8.556 9	0.976 5	39.215 4
ST_HTFNN	0.199 7	7.957 0	0.977 3	39.682 1

模型	MAE	每轮训练时间/s	参数量/10³
Conv-LSTM	9.963 0	5	334.658
ST-ResNet	9.366 3	4	713.348
DeepSTN+	9.269 6	9	33 969.560
ST-3DNet	9.181 6	4	681.786
SA-ConvLSTM	8.396 8	10	426.060
LMST3D-ResNet	8.744 8	17	805.540
ST-3DGMR	8.629 2	15	568.356
Mamba	9.989 9	2	153.088
ST-3DMDDN	8.556 9	25	492.083
ST_HTFNN-w/o-S	8.220 4	13	1 081.760
ST_HTFNN	7.957 0	19	1 087.750

模型	MSE	MAE	SSIM	PSNR
ST_HTFNN-w/o-S	0.206 7	8.220 4	0.976 3	39.388 9
ST_HTFNN-w/o-D	0.210 5	8.260 7	0.975 7	39.382 4
ST_HTFNN-w/o-E	0.203 3	8.025 9	0.977 1	39.590 7
ST_HTFNN-w/o-R	0.206 6	8.071 9	0.976 1	39.575 2
ST_HTFNN	0.199 7	7.957 0	0.977 3	39.682 1

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