基于Attention-T-GRU的短时交通流预测

兰州理工大学学报 ›› 2025, Vol. 51 ›› Issue (4): 107-113.

• 自动化技术与计算机技术 • 上一篇下一篇

基于Attention-T-GRU的短时交通流预测

张玺君^*, 苏晋, 陈宣, 尚继洋, 崔勇

兰州理工大学计算机与通信学院, 甘肃兰州 730050

收稿日期:2022-11-12 出版日期:2025-08-28 发布日期:2025-09-05
通讯作者: 张玺君(1980-),男,甘肃临洮人,博士,副教授.Email:zhangxijun198079@sina.cn
基金资助:
国家自然科学基金(62162040),甘肃省科技计划资助自然科学基金重点项目(22JR5RA226),甘肃省科技计划(21ZD4GA028),甘肃省高等学校创新基金(2021A-028)

Research on short-term traffic flow forecast based on attention-T-GRU

ZHANG Xi-Jun, SU Jin, CHEN Xuan, SHANG Ji-yang, CUI Yong

School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

Received:2022-11-12 Online:2025-08-28 Published:2025-09-05

摘要/Abstract

摘要： 针对路网中交通流较大的关键路段需要准确的交通流预测结果,在考虑交通流时空相关性的基础上,选取预测道路的同向相邻道路,提出单条路段的短时交通流预测组合模型.首先,根据研究道路与其上下游道路的相关性构建速度矩阵;其次,将速度矩阵输入注意力机制网络提取道路之间的空间联系;最后,将注意力机制输出的数据分解为若干个序列T输入GRU网络中提取时间序列特征,构成ATGRU(Attention-T-GRU)组合模型完成路网的短时交通流预测.使用西安市的交通流数据对提出的ATGRU组合模型进行验证,结果表明,ATGRU模型相比T-LSTM、CNN-LSTM及ACGRU等模型有更高的预测精度.

关键词: 短时交通流预测, 时空特性, 注意力机制, 组合模型

Abstract: Since some key road sections need accurate prediction results, the prediction modeling of a single road was carried out, and the adjacent roads in the same direction of the predicted road are selected for research considering the temporal and spatial correlation of traffic flow. First, a velocity matrix was constructed according to the correlation between the research road and its upstream and downstream roads. Secondly, the velocity matrix was input into the attention mechanism network to extract the spatial connection among the roads. Finally, the data output of the attention mechanism was decomposed into several sequences T, which was input into the GRU network to extract features, forming the short-term traffic flow prediction of the ATGRU (Attention-T-GRU) combination model. The proposed ATGRU combined model is verified by using the traffic flow data in Xi’an. The results show that the ATGRU model has a better prediction accuracy compared to models such as T-LSTM, CNN-LSTM and ACGRU.

Key words: short-term traffic flow forecast, spatio-temporal feature, attention mechanism, combination model

中图分类号:

U491.14

张玺君, 苏晋, 陈宣, 尚继洋, 崔勇. 基于Attention-T-GRU的短时交通流预测[J]. 兰州理工大学学报, 2025, 51(4): 107-113.

ZHANG Xi-Jun, SU Jin, CHEN Xuan, SHANG Ji-yang, CUI Yong. Research on short-term traffic flow forecast based on attention-T-GRU[J]. Journal of Lanzhou University of Technology, 2025, 51(4): 107-113.

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基于Attention-T-GRU的短时交通流预测

Research on short-term traffic flow forecast based on attention-T-GRU

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