Time-interdependency-aware dynamic Bayesian network for traffic prediction

doi:10.11772/j.issn.1001-9081.2025050570

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (5): 1507-1517.DOI: 10.11772/j.issn.1001-9081.2025050570

• Data science and technology • Previous Articles

Time-interdependency-aware dynamic Bayesian network for traffic prediction

Huijie GUO¹, Tianfeng DOU¹, Zhenlin ZHANG¹, Kaiyuan QI², Dong WU², Zhijian QU¹, Zhao LI¹, Chongguang REN¹()

^1.School of Computer Science and Technology，Shandong University of Technology，Zibo Shandong 255049，China
^2.Inspur （Jinan） Data Technology Company Limited，Jinan Shandong 250101，China

Received:2025-05-27 Revised:2025-08-09 Accepted:2025-08-18 Online:2025-08-20 Published:2026-05-10
Contact: Chongguang REN
About author:GUO Huijie， born in 2000， M. S. candidate. Her research interests include artificial intelligence and intelligent systems.
DOU Tianfeng， born in 2000， M. S. candidate. Her research interests include artificial intelligence and intelligent systems.
ZHANG Zhenlin， born in 1999， M. S. candidate. His research interests include artificial intelligence and intelligent systems.
QI Kaiyuan， born in 1984， Ph. D.， senior engineer. His research interests include cloud computing， big data.
WU Dong， born in 1984， engineer. His research interests include cloud computing.
QU Zhijian， born in 1980， Ph.D.， associate professor. His research interests include data analysis， intelligent computing， evolutionary algorithms.
LI Zhao， born in 1983， Ph. D.， associate professor. His research interests include artificial intelligence， edge computing.
Supported by:
National Key Research and Development Program of China(2022YFE0107300);Outstanding Youth Innovation Teams in Higher Education of Shandong Province(2019KJN048)

基于时间依赖建模的动态贝叶斯网络交通预测

郭慧洁¹, 窦天凤¹, 张振琳¹, 亓开元², 吴栋², 曲志坚¹, 李钊¹, 任崇广¹()

^1.山东理工大学计算机科学与技术学院，山东淄博 255049
^2.济南浪潮数据技术有限公司，济南 250101

通讯作者: 任崇广
作者简介:郭慧洁（2000—），女，山东淄博人，硕士研究生，主要研究方向：人工智能与智能系统
窦天凤（2000—），女，山东淄博人，硕士研究生，主要研究方向：人工智能与智能系统
张振琳（1999—），男，山东聊城人，硕士研究生，主要研究方向：人工智能与智能系统
亓开元（1984—），男，山东莱芜人，高级工程师，博士，主要研究方向：云计算、大数据
吴栋（1984—），男，山东聊城人，工程师，主要研究方向：云计算
曲志坚（1980—），男，山东青岛人，副教授，博士，主要研究方向：数据分析、智能计算、进化算法
李钊（1983—），男，山东淄博人，副教授，博士，主要研究方向：人工智能、边缘计算
基金资助:
国家重点研发计划项目(2022YFE0107300);山东省高等学校优秀青年创新团队项目(2019KJN048)

Abstract

Abstract:

Accurate traffic forecasting not only improves the efficiency and safety of the traffic system， but also promotes the sustainable social and economic development. Although a large number of studies have been devoted to modeling spatiotemporal correlation， existing methods still have significant limitations： most models tend to collectively predict the traffic flow of all regions in all time periods， ignoring spatio-temporal heterogeneity， especially the impact of the traffic status of the current region on the future traffic status of related regions. To address this problem， a Time-Interdependency-aware Dynamic Bayesian Network for traffic prediction （TIDBN） method was proposed. Using pre-trained modules， TIDBN employed a time-varying dynamic Bayesian network to capture the complex temporal relationships in time-series data arising from simultaneous and lagged effects. To further improve its ability to capture spatio-temporal correlation， a spatio-temporal attention mechanism was introduced for in-depth analysis. Subsequently， a Graph Convolutional Network （GCN） was utilized to model the spatio-temporal topological structure， generating more accurate traffic predictions. The experimental results show that TIDBN performs excellently on two real traffic prediction tasks， especially for 1-hour prediction. On the PeMS-BAY dataset， the Mean Absolute Error （MAE） of TIDBN is 4% lower than that of the second-best baseline method.

Key words: traffic prediction, Bayesian network, Graph Convolutional Network (GCN), attention mechanism, deep learning

摘要：

精准的交通预测不仅能提升交通系统的效率与安全性，还能促进社会和经济的可持续发展。尽管已有大量研究致力于建模时空相关性，但现有方法仍然存在明显不足：大多数模型倾向于集体预测所有区域在所有时间段的交通流量，忽略了时空异质性，特别是当前区域的交通状态对相关区域未来交通状态的影响。为了解决这一问题，提出基于时间依赖建模的动态贝叶斯网络交通预测（TIDBN）方法。TIDBN通过预训练模块，利用时变动态贝叶斯网络捕捉时间序列数据中由于同时和滞后影响而产生的复杂时序关系。为了进一步提升对时空相关性的捕捉能力，引入时空注意力机制进行深入分析；随后，采用图卷积网络（GCN）对时空拓扑结构进行建模，以生成更准确的交通预测。实验结果表明，TIDBN在2个真实的交通预测任务中均表现优异，尤其在1 h预测中，它在数据集PeMS-BAY上的平均绝对误差（MAE）比基线次优方法降低了4%。

关键词: 交通预测, 贝叶斯网络, 图卷积网络, 注意力机制, 深度学习

CLC Number:

TP183

Huijie GUO, Tianfeng DOU, Zhenlin ZHANG, Kaiyuan QI, Dong WU, Zhijian QU, Zhao LI, Chongguang REN. Time-interdependency-aware dynamic Bayesian network for traffic prediction[J]. Journal of Computer Applications, 2026, 46(5): 1507-1517.

郭慧洁, 窦天凤, 张振琳, 亓开元, 吴栋, 曲志坚, 李钊, 任崇广. 基于时间依赖建模的动态贝叶斯网络交通预测[J]. 《计算机应用》唯一官方网站, 2026, 46(5): 1507-1517.

Figures/Tables 11

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节点a	节点b	DTW距离	是否相邻节点	相关性
27	177	124 984.04	否	1
27	36	23 850 096.66	否	0

节点a	节点b	DTW距离	是否相邻节点	相关性
27	177	124 984.04	否	1
27	36	23 850 096.66	否	0

数据集	模型	3个时间步			6个时间步			12个时间步
数据集	模型	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%
METR-LA	ARIMA^［4］	3.99	8.21	9.60	5.15	11.45	13.50	6.90	13.23	17.40
	STGCN^［33］	2.88	5.74	7.62	3.47	7.24	9.57	4.59	9.40	12.70
	DCRNN^［40］	2.77	5.38	7.30	3.15	6.45	8.80	3.60	7.60	10.50
	Graph-WaveNet^［19］	2.69	5.15	6.90	3.07	6.22	8.37	3.53	7.37	10.01
	ASTGCN^［41］	4.86	9.27	9.21	5.43	10.61	10.13	6.51	12.52	11.64
	AGCRN^［42］	2.87	5.58	7.70	3.23	6.58	9.00	3.62	7.51	10.38
	GMAN^［43］	2.80	5.55	7.41	3.12	6.49	8.73	3.44	7.35	10.07
	MTGNN^［44］	2.69	5.18	6.86	3.05	6.17	8.19	3.49	7.23	9.87
	DGCRN^［23］	2.62	5.01	6.63	2.99	6.05	8.19	3.44	7.19	9.73
	D²STGNN^［45］	2.56	4.88	6.48	2.90	5.89	7.78	3.35	7.03	9.40
	MegaCRN^［46］	2.52	4.94	6.44	2.93	6.06	7.96	3.38	7.23	9.72
	RGDAN^［47］	2.69	5.20	7.14	2.96	5.98	8.07	3.36	7.02	9.54
	TRL-DAG^［48］	2.73	5.32	7.27	3.16	6.41	8.68	3.55	7.37	10.28
	TIDBN	2.63	4.90	6.23	2.91	5.66	7.65	3.25	6.53	9.03
PeMS-BAY	ARIMA^［4］	1.62	3.30	3.50	2.33	4.76	5.40	3.38	6.50	8.30
	STGCN^［33］	1.36	2.96	2.90	1.81	4.27	4.17	2.49	5.69	5.79
	DCRNN^［40］	1.38	2.95	2.90	1.74	3.97	3.90	2.07	4.74	4.90
	Graph-WaveNet^［19］	1.30	2.74	2.73	1.63	3.70	3.67	1.95	4.52	4.63
	ASTGCN^［41］	1.52	3.13	3.22	2.01	4.27	4.48	2.61	5.42	6.00
	AGCRN^［42］	1.37	2.87	2.94	1.69	3.85	3.87	1.96	4.54	4.64
	GMAN^［43］	1.34	2.91	2.86	1.63	3.76	3.68	1.86	4.32	4.37
	MTGNN^［44］	1.32	2.79	2.77	1.65	3.74	3.69	1.94	4.49	4.53
	DGCRN^［23］	1.28	2.69	2.66	1.59	3.63	3.55	1.89	4.42	4.43
	D²STGNN^［45］	1.24	2.60	2.58	1.55	3.52	3.49	1.85	4.30	4.37
	MegaCRN^［46］	1.28	2.72	2.67	1.60	3.68	3.57	1.88	4.42	4.41
	RGDAN^［47］	1.31	2.79	2.77	1.56	3.55	3.47	1.82	4.20	4.28
	TRL-DAG^［48］	1.29	2.75	2.78	1.61	3.70	3.66	1.92	4.50	4.56
	TIDBN	1.18	2.44	2.40	1.44	3.16	3.14	1.74	3.96	4.08

数据集	模型	3个时间步			6个时间步			12个时间步
数据集	模型	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%
METR-LA	ARIMA^［4］	3.99	8.21	9.60	5.15	11.45	13.50	6.90	13.23	17.40
	STGCN^［33］	2.88	5.74	7.62	3.47	7.24	9.57	4.59	9.40	12.70
	DCRNN^［40］	2.77	5.38	7.30	3.15	6.45	8.80	3.60	7.60	10.50
	Graph-WaveNet^［19］	2.69	5.15	6.90	3.07	6.22	8.37	3.53	7.37	10.01
	ASTGCN^［41］	4.86	9.27	9.21	5.43	10.61	10.13	6.51	12.52	11.64
	AGCRN^［42］	2.87	5.58	7.70	3.23	6.58	9.00	3.62	7.51	10.38
	GMAN^［43］	2.80	5.55	7.41	3.12	6.49	8.73	3.44	7.35	10.07
	MTGNN^［44］	2.69	5.18	6.86	3.05	6.17	8.19	3.49	7.23	9.87
	DGCRN^［23］	2.62	5.01	6.63	2.99	6.05	8.19	3.44	7.19	9.73
	D²STGNN^［45］	2.56	4.88	6.48	2.90	5.89	7.78	3.35	7.03	9.40
	MegaCRN^［46］	2.52	4.94	6.44	2.93	6.06	7.96	3.38	7.23	9.72
	RGDAN^［47］	2.69	5.20	7.14	2.96	5.98	8.07	3.36	7.02	9.54
	TRL-DAG^［48］	2.73	5.32	7.27	3.16	6.41	8.68	3.55	7.37	10.28
	TIDBN	2.63	4.90	6.23	2.91	5.66	7.65	3.25	6.53	9.03
PeMS-BAY	ARIMA^［4］	1.62	3.30	3.50	2.33	4.76	5.40	3.38	6.50	8.30
	STGCN^［33］	1.36	2.96	2.90	1.81	4.27	4.17	2.49	5.69	5.79
	DCRNN^［40］	1.38	2.95	2.90	1.74	3.97	3.90	2.07	4.74	4.90
	Graph-WaveNet^［19］	1.30	2.74	2.73	1.63	3.70	3.67	1.95	4.52	4.63
	ASTGCN^［41］	1.52	3.13	3.22	2.01	4.27	4.48	2.61	5.42	6.00
	AGCRN^［42］	1.37	2.87	2.94	1.69	3.85	3.87	1.96	4.54	4.64
	GMAN^［43］	1.34	2.91	2.86	1.63	3.76	3.68	1.86	4.32	4.37
	MTGNN^［44］	1.32	2.79	2.77	1.65	3.74	3.69	1.94	4.49	4.53
	DGCRN^［23］	1.28	2.69	2.66	1.59	3.63	3.55	1.89	4.42	4.43
	D²STGNN^［45］	1.24	2.60	2.58	1.55	3.52	3.49	1.85	4.30	4.37
	MegaCRN^［46］	1.28	2.72	2.67	1.60	3.68	3.57	1.88	4.42	4.41
	RGDAN^［47］	1.31	2.79	2.77	1.56	3.55	3.47	1.82	4.20	4.28
	TRL-DAG^［48］	1.29	2.75	2.78	1.61	3.70	3.66	1.92	4.50	4.56
	TIDBN	1.18	2.44	2.40	1.44	3.16	3.14	1.74	3.96	4.08

模型	METR-LA		PeMS-BAY
模型	MAE	RMSE	MAE	RMSE
w/o sem	3.35	6.64	1.82	4.12
w/o con	3.46	6.73	1.93	4.24
w/o DBG-Dyn	3.85	7.34	2.65	4.67
w/o DBG-Stat	3.64	7.13	2.54	4.53
w/o GCN	3.52	7.03	2.43	4.42
TIDBN	3.25	6.53	1.74	3.96

Time-interdependency-aware dynamic Bayesian network for traffic prediction

基于时间依赖建模的动态贝叶斯网络交通预测

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