GMHANN: A Novel Traffic Flow Prediction Method for Transportation Management Based on Spatial-Temporal Graph Modeling

计算机科学 流量(计算机网络) 数据挖掘 图形 智能交通系统 交叉口(航空) 循环神经网络 数据建模 实时计算 人工智能 人工神经网络 工程类 运输工程 理论计算机科学 计算机安全 数据库
作者
Qing Wang,Weiping Liu,Wang Xiu,Xinghong Chen,Guannan Chen,Qingxiang Wu
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:25 (1): 386-401 被引量:2
标识
DOI:10.1109/tits.2023.3306559
摘要

Traffic flow prediction significantly affects the intelligent transportation for digitized urban transportation management and urban traffic control. Considering the complexity and strong non-linearity shown by traffic flow data, the establishment of model regarding spatial correlations as well as time dynamics can remarkably help to accurately predict traffic flow. A lot of current methods are mainly focused on using the historical time series information of observations to extract sequence features. Such forecasting will cause the lack of information and lead to poor accuracy of the forecast results. Although some studies applied spatial-temporal information, but they are not very accurate. In network-based problems, we would consider the constraint of road networks. Specifically, intersection flows, road speed and travel time are related to road networks. Also, they restrict the long-term prediction of traffic flow. For addressing above issues, a graph multi-head attention neural network (GMHANN) is proposed for the purpose of traffic flow prediction. In design, the GMHANN has an encoder-decoder structure. By the encoder, the data are compressed into a hidden space representation, which, relying on the decoder, is reconstructed as output. Furthermore, we put forward a novel gated recurrent unit (GRU) module (AGRU) based on multi-head attention for the effective extraction of the spatial and temporal features exhibited by traffic flow data. Other state-of-the-art methods are employed for evaluating four public datasets, which reveals that our proposed method outperforms others.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
感谢有你发布了新的文献求助10
1秒前
wh2740完成签到,获得积分10
2秒前
SHAO应助可乐冰淇淋采纳,获得30
5秒前
5秒前
脑洞疼应助JUdy采纳,获得10
7秒前
何白发布了新的文献求助10
7秒前
Lu发布了新的文献求助10
8秒前
hugdoggy完成签到,获得积分10
10秒前
10秒前
桃子发布了新的文献求助30
13秒前
14秒前
Liufgui给风之星的求助进行了留言
14秒前
15秒前
Lucas应助桔子采纳,获得30
17秒前
JUdy发布了新的文献求助10
20秒前
15858833895发布了新的文献求助10
21秒前
许晓蝶完成签到,获得积分10
21秒前
花花完成签到 ,获得积分10
21秒前
wayne完成签到 ,获得积分10
24秒前
24秒前
hua完成签到,获得积分10
25秒前
可乐冰淇淋完成签到,获得积分10
26秒前
26秒前
贝湾完成签到,获得积分10
27秒前
量子星尘发布了新的文献求助10
28秒前
29秒前
水中鱼发布了新的文献求助10
30秒前
lin完成签到,获得积分10
31秒前
陌予完成签到 ,获得积分10
31秒前
32秒前
Fengliguantou发布了新的文献求助20
32秒前
桔子发布了新的文献求助30
32秒前
萨日呼完成签到,获得积分10
32秒前
彭栋完成签到,获得积分10
36秒前
Lu发布了新的文献求助10
36秒前
36秒前
41秒前
桔子完成签到,获得积分10
43秒前
二般人完成签到 ,获得积分10
48秒前
忐忑的阑香完成签到,获得积分10
49秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989115
求助须知:如何正确求助?哪些是违规求助? 3531367
关于积分的说明 11253688
捐赠科研通 3269986
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882078
科研通“疑难数据库(出版商)”最低求助积分说明 809105