局部-全局联合感知的时空自适应交通集成预测方法

doi:10.11947/j.AGCS.2026.20250340

测绘学报 ›› 2026, Vol. 55 ›› Issue (2): 206-221.doi: 10.11947/j.AGCS.2026.20250340

• 空间智能与智慧城市 • 上一篇

局部-全局联合感知的时空自适应交通集成预测方法

王立增¹^,²(), 程诗奋¹^,²(), 杨一涛³, 王培晓¹^,², 陆锋¹^,²^,⁴^,⁵

^1.中国科学院地理科学与资源研究所地理信息科学与技术全国重点实验室，北京　100101
^2.中国科学院大学资源与环境学院，北京　100049
^3.英国利兹大学地理学院，英国　利兹　LS29JT
^4.福州大学数字中国研究院，福建　福州　350108
^5.江苏省地理信息资源开发与利用协同创新中心，江苏　南京　210023

收稿日期:2025-08-26 修回日期:2025-12-18 发布日期:2026-03-13
通讯作者: 程诗奋 E-mail:wanglz@lreis.ac.cn;chengsf@lreis.ac.cn
作者简介:王立增（2001—），男，博士生，研究方向为时空数据挖掘与地理空间智能。 E-mail：wanglz@lreis.ac.cn
基金资助:
中国科学院基础与交叉前沿科研先导专项(XDB0740100-02);国家自然科学基金(42371469)

LGA-EL: a spatio-temporal adaptive ensemble method with local-global awareness for traffic prediction

Lizeng WANG¹^,²(), Shifen CHENG¹^,²(), Yitao YANG³, Peixiao WANG¹^,², Feng LU¹^,²^,⁴^,⁵

^1.State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^2.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
^3.School of Geography, University of Leeds, Leeds LS29JT, UK
^4.The Academy of Digital China, Fuzhou University, Fuzhou 350108, China
^5.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Received:2025-08-26 Revised:2025-12-18 Published:2026-03-13
Contact: Shifen CHENG E-mail:wanglz@lreis.ac.cn;chengsf@lreis.ac.cn
About author:WANG Lizeng (2001—), male, PhD candidate, majors in spatio-temporal data mining and geospatial artificial intelligence. E-mail: wanglz@lreis.ac.cn
Supported by:
Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0740100-02);The National Natural Science Foundation of China(42371469)

摘要/Abstract

摘要：

交通预测是建设智能交通系统的核心需求。在复杂交通场景中，不同预测模型在不同空间区域和时间段的表现存在显著差异，单一模型难以稳定适应多样化的预测需求。现有集成学习方法通过融合多模型优势提升预测稳定性，但通常依赖全局固定或局部最优的集成策略，忽略了全局时空相关性和时空异质性对模型集成过程的协同约束，从而限制了模型的预测性能和泛化能力。为此，本文提出了一种局部-全局上下文联合感知的时空自适应交通集成预测方法，通过自适应调整集成参数，优化基模型在不同交通状态下的表现。本文方法通过嵌入路网拓扑结构和交通状态演化特征，从局部和全局视角联合感知监测站点的时空信息，协同表达集成过程的时空相关性和时空异质性。在此基础上，该方法自适应地求解各时空位置的集成参数，结合基模型的输出特征进行加权整合，从而获得最终的预测结果。在交通流量、交通速度的短期和长期预测任务上的试验表明，本文方法在预测精度和计算效率方面均优于6种主流的集成预测方法。进一步的可解释性分析表明，本文方法能够精准捕捉不同交通状态下的模型性能差异，通过自适应的集成权重发挥不同模型的优势，显著增强集成学习在交通预测任务中的性能与稳健性。

关键词: 交通预测, 集成学习, 时空感知, 全局时空相关性, 时空异质性

Abstract:

Traffic prediction is a core requirement for building intelligent transportation systems. In complex traffic scenarios, different prediction models exhibit significant performance variations across spatial regions and time periods, making it difficult for any single model to stably adapt to diverse prediction demands. Existing ensemble learning methods enhance prediction stability by leveraging the strengths of multiple models. However, they typically rely on globally fixed or locally optimal ensemble strategies, which overlook the synergistic constraints of global spatio-temporal correlations and spatio-temporal heterogeneity in the ensemble process, limiting the predictive performance and generalization ability. Therefore, this study proposes a spatiotemporal adaptive ensemble learning method with local-global awareness (LGA-EL) for traffic prediction tasks, which optimizes the performance of the base models under different traffic conditions by adaptively adjusting the ensemble parameters. The method first embeds road network topology and traffic state evolution characteristics to jointly capture spatio-temporal information of monitoring stations from both local and global perspectives, thereby collaboratively representing the spatio-temporal correlation and heterogeneity in the ensemble process. Based on the embedding vectors, the method adaptively solves the ensemble parameters for each spatio-temporal location and dynamically weights the output features of the base models to generate the final prediction. Experiments on short-term and long-term prediction tasks for traffic flow and speed demonstrate that the proposed method outperforms six mainstream ensemble prediction methods in terms of both prediction accuracy and computational efficiency. Further interpretability analysis shows that the method can accurately capture performance differences among models under different traffic states, leveraging the strengths of various models through adaptive ensemble weights, significantly enhancing the performance and robustness of ensemble learning in traffic prediction tasks.

Key words: traffic prediction, ensemble learning, spatio-temporal awareness, global spatio-temporal correlation, spatio-temporal heterogeneity

中图分类号:

P208

王立增, 程诗奋, 杨一涛, 王培晓, 陆锋. 局部-全局联合感知的时空自适应交通集成预测方法[J]. 测绘学报, 2026, 55(2): 206-221.

Lizeng WANG, Shifen CHENG, Yitao YANG, Peixiao WANG, Feng LU. LGA-EL: a spatio-temporal adaptive ensemble method with local-global awareness for traffic prediction[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(2): 206-221.

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数据集名称	预测变量	研究区域	时间间隔/min	监测站点数量	时间步数量	时间跨度
TR-Wuhan	交通流量/辆	中国武汉市	5	175	8064	2021-02-01—2021-02-28
PEMS07（M）	交通速度/（km/h）	美国加利福尼亚州	5	228	12 672	2012-05-01—2012-06-30

模型结构	参数名称	最优设置
局部-全局时空感知模块	局部嵌入向量维度e_l	24/24
	全局嵌入向量维度e_g	32/48
	多尺度时空融合层结构	（64）/（64）
时空自适应加权模块	时空自适应加权网络结构	（128，32）/（256，64）
训练策略	损失函数超参数δ	0.1/10
	优化器	Adam
	随机失活率	0.2/0.1
	批处理大小	64
	学习率	5×10^－5/1×10^－4

类型	方法	短期预测任务（15 min）			长期预测任务（60 min）
类型	方法	MAE/辆	RMSE/辆	MAPE/（%）	MAE/辆	RMSE/辆	MAPE/（%）
基模型	STGCN	4.079	7.445	16.869	7.756	15.259	28.210
	DCRNN	4.353	8.041	17.065	7.155	14.348	27.062
	GMAN	4.862	8.926	18.887	7.558	14.831	28.201
其他单一模型	MTGNN	4.014	7.551	16.812	7.070	14.133	26.402
	ASTGCN	3.992	7.313	16.755	6.986	13.799	25.931
	AGCRN	3.963	7.271	16.193	6.891	13.151	25.314
集成方法	Avg-EL	4.063	7.438	15.866	6.731	13.296	24.749
	AW-EL	4.053	7.420	15.835	6.717	13.259	24.747
	LinReg-EL	4.010	7.379	15.856	6.689	13.316	24.792
	GTWR-EL	3.969	7.280	15.653	6.651	13.185	24.660
	DDPG-EL	3.962	7.292	15.711	6.647	13.232	24.772
	MI-EL	3.941	7.228	15.406	6.606	13.082	24.563
	LGA-EL	3.915	7.166	15.283	6.495	12.785	23.615

类型	方法	短期预测任务（15 min）			长期预测任务（60 min）
类型	方法	MAE/（km/h）	RMSE/（km/h）	MAPE/（%）	MAE/（km/h）	RMSE/（km/h）	MAPE/（%）
基模型	STGCN	1.799	3.286	4.133	2.984	5.825	7.315
	DCRNN	1.780	3.386	4.023	2.993	6.094	7.364
	GMAN	1.826	3.264	4.206	2.837	5.257	7.028
其他单一模型	MTGNN	1.716	3.144	4.170	2.758	5.291	6.723
	ASTGCN	1.765	3.221	4.288	3.190	5.693	7.957
	AGCRN	1.683	3.160	3.958	2.696	5.371	6.708
集成方法	Avg-EL	1.718	3.164	3.930	2.813	5.273	6.790
	AW-EL	1.720	3.169	3.927	2.802	5.224	6.768
	LinReg-EL	1.723	3.158	3.927	2.797	5.204	6.635
	GTWR-EL	1.703	3.147	3.901	2.740	5.196	6.601
	DDPG-EL	1.695	3.122	3.859	2.771	5.193	6.624
	MI-EL	1.681	3.114	3.856	2.718	5.122	6.599
	LGA-EL	1.664	3.091	3.776	2.658	5.038	6.480

数据集	方法	短期预测任务（15 min）			长期预测任务（60 min）
数据集	方法	MAE	RMSE	MAPE	MAE	RMSE	MAPE
TR-Wuhan	Avg-EL	3.645	3.654	3.675	3.499	3.848	4.583
	AW-EL	3.395	3.414	3.486	3.302	3.577	4.575
	LinReg-EL	2.368	2.885	3.613	2.889	3.989	4.749
	GTWR-EL	1.355	1.567	2.363	2.338	3.035	4.238
	DDPG-EL	1.181	1.728	2.724	2.280	3.379	4.671
	MI-EL	0.654	0.853	0.798	1.532	1.679	3.473
	平均提升	2.100	2.350	2.776	2.640	3.251	4.382
PEMS07（M）	Avg-EL	3.135	2.306	3.939	5.504	4.471	4.567
	AW-EL	3.218	2.456	3.865	5.134	3.569	4.252
	LinReg-EL	3.414	2.140	3.846	4.975	3.200	2.330
	GTWR-EL	2.261	1.787	3.213	2.987	3.046	1.830
	DDPG-EL	1.800	1.001	2.160	4.072	2.990	2.171
	MI-EL	0.982	0.746	2.083	2.202	1.646	1.801
	平均提升	2.468	1.740	3.184	4.146	3.154	2.825

局部-全局联合感知的时空自适应交通集成预测方法

LGA-EL: a spatio-temporal adaptive ensemble method with local-global awareness for traffic prediction

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参考文献 37

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Metrics

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数据集名称	方法	参数量	训练时间/s	预测时间/s
TR-Wuhan	DDPG-EL	398 128	108.672	1.081
	MI-EL	322 110	62.985	0.728
	LGA-EL	177 424	15.344	0.498
PEMS07（M）	DDPG-EL	519 368	185.344	1.734
	MI-EL	525 510	122.849	1.662
	LGA-EL	434 656	63.047	1.123

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