基于混合图卷积的多通道时空交通流预测模型

doi:10.11959/j.issn.1000-0801.2023173

Abstract

Abstract:

Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.

Key words: The National Natural Science Foundation of China, The “Pioneer” and “Leading Goose” Research ＆ Development Program of Zhejiang Province, intelligent transportation, dynamic graph convolution, multi-head attention, spatial-temporal correlation, multi-channel

CLC Number:

TP393

Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG. Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution[J]. Telecommunications Science, 2023, 39(9): 97-110.

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References 32

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数据集	指标	FC-LSTM	STGCN	DCRNN	ASTGCN	Graph WaveNet	STSGCN	STGODE	MHGCN （本文模型）
PEMS03	RMSE	35.11±0.50	30.12±0.70	30.31±0.25	29.66±1.68	32.94±0.18	29.21±0.56	27.69±0.73	27.36±0.22
	MAE	21.33±0.24	17.49±0.46	18.18±0.15	17.69±1.43	19.85±0.03	17.48±0.15	16.51±0.06	16.17±0.10
	MAPE	23.33±4.23%	17.15±0.45%	18.91±0.82%	19.40±2.24%	19.93±0.49%	16.78±0.20%	18.38±1.10%	17.52±0.26%
PEMS04	RMSE	44.56±0.01	35.55±0.75	38.12±0.26	35.22±1.90	39.70±0.04	33.65±0.20	32.45±0.27	32.23±0.11
	MAE	28.70±0.01	22.70±0.64	24.70±0.22	22.93±1.29	25.45±0.03	21.19±0.10	20.87±0.11	20.55±0.07
	MAPE	19.20±0.01%	14.59±0.21%	17.12±0.37%	16.56±1.36%	17.29±0.24%	13.90±0.05%	14.98±0.42%	14.44±0.21%
PEMS07	RMSE	45.84±0.57	38.78±0.58	38.58±0.70	42.57±3.31	42.78±0.07	39.03±0.27	36.75±0.49	34.82±0.06
	MAE	29.98±0.42	25.38±0.49	25.30±0.52	28.05±2.34	26.85±0.05	24.26±0.14	23.95±0.60	22.05±0.05
	MAPE	13.20±0.53%	11.08±0.18%	11.66±0.33%	13.92±1.65%	12.12±0.41%	10.21±1.65%	10.98±0.75%	9.82±0.08%
PEMS08	RMSE	34.06±0.32	27.83±0.20	27.83±0.05	28.16±0.48	31.05±0.07	26.80±0.18	25.97±0.19	25.89±0.15
	MAE	22.20±0.18	18.02±0.14	17.86±0.03	18.61±0.40	19.13±0.08	17.13±0.09	16.79±0.13	16.62±0.07
	MAPE	14.20±0.59%	11.40±0.10%	11.45±0.03%	13.08±1.00%	12.68±0.57%	10.96±0.07%	11.84±0.52%	11.52±0.10%

数据集	指标		n₁=64			n₁=128			n₁=256
数据集	指标	n₂=64	n₂=128	n₂=256	n₂=64	n₂=128	n₂=256	n₂=64	n₂=128	n₂=256
PEMS03	RMSE	27.30	27.47	27.90	27.73	27.98	27.97	28.51	27.30	27.60
	MAE	16.17	16.43	16.52	16.27	16.37	16.53	16.73	16.14	16.24
	MAPE	17.33%	18.15%	18.26%	18.35%	17.39%	17.20%	21.21%	17.33%	17.77%
PEMS04	RMSE	32.32	32.61	32.65	32.34	32.51	33.32	32.40	32.23	32.67
	MAE	20.72	20.77	20.69	20.71	20.67	21.04	20.79	20.55	20.65
	MAPE	14.50%	14.55%	14.58%	14.59%	14.57%	14.42%	14.58%	14.34%	14.36%
PEMS07	RMSE	35.11	35.08	34.94	35.00	34.73	34.77	35.00	34.71	34.94
	MAE	22.61	22.46	22.44	22.43	22.15	22.18	22.45	22.13	22.38
	MAPE	10.59%	10.08%	10.11%	10.10%	9.92%	10.05%	10.11%	9.84%	10.13%
PEMS08	RMSE	26.25	25.97	26.48	26.24	26.14	26.16	26.13	25.89	26.25
	MAE	16.93	16.83	17.14	16.83	16.84	16.81	16.71	16.67	16.86
	MAPE	11.64%	11.52%	12.00%	11.55%	11.70%	11.75%	11.90%	11.51%	11.46%

数据集	指标	MHGCN-two	MHGCN-S	MHGCN-T	MHGCN-D	MHGCN
PEMS03	RMSE	28.99	27.71	27.68	38.25	27.30
	MAE	17.12	16.80	16.33	22.23	16.14
	MAPE	19.10%	18.98%	16.76%	31.77%	17.33%
PEMS04	RMSE	33.68	32.81	32.98	38.54	32.23
	MAE	20.85	21.08	20.97	25.22	20.55
	MAPE	14.46%	14.69%	14.23%	18.33%	14.34%
PEMS07	RMSE	36.51	35.58	35.57	56.86	34.71
	MAE	22.70	22.88	22.90	33.90	22.13
	MAPE	10.12%	10.39%	10.45%	15.75%	9.84%
PEMS08	RMSE	27.65	26.74	26.70	32.29	25.89
	MAE	17.64	17.12	17.16	21.15	16.67
	MAPE	11.43%	12.50%	11.37%	14.40%	11.51%

Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution

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