Hierarchical Spatial-Temporal Neural Network with Attention Mechanism for Traffic Flow Forecasting

Author:

Lian Qingyun1,Sun Wei2ORCID,Dong Wei2

Affiliation:

1. College of Merchant Ship, Shanghai Maritime University, Shanghai 201306, China

2. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Abstract

Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have primarily employed Recurrent Neural Networks or Graph Convolutional Networks, without fully accounting for the interdependency between spatial and temporal factors. To address this, we introduce a novel Hierarchical Spatial-Temporal Neural Networks with Attention Mechanism model (HSTAN). This model concurrently captures temporal correlations and spatial dependencies using a multi-headed self-attention mechanism in both temporal and spatial terms. It also integrates global spatial-temporal correlations through a hierarchical structure with residuals. Moreover, the analysis of attention weight matrices can depict complex spatial-temporal correlations, thereby enhancing our traffic forecasting capabilities. We conducted experiments on two publicly available traffic datasets, and the results demonstrated that the HSTAN model’s prediction accuracy surpassed that of several benchmark methods.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference30 articles.

1. Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends;Veres;IEEE Trans. Intell. Transp. Syst.,2020

2. Graph Neural Network for Traffic Forecasting: A Survey;Jiang;Expert Syst. Appl.,2022

3. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction;Zhao;IEEE Trans. Intell. Transp. Syst.,2020

4. Bai, J., Zhu, J., Song, Y., Zhao, L., Hou, Z., Du, R., and Li, H. (2021). A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting. ISPRS Int. J. Geo-Inf., 10.

5. Xu, M., Dai, W., Liu, C., Gao, X., Lin, W., Qi, G.J., and Xiong, H. (2020). Spatial-Temporal Transformer Networks for Traffic Flow Forecasting. arXiv.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3