An LSTM-Based Method with Attention Mechanism for Travel Time Prediction

计算机科学 机制(生物学) 期限(时间) 人工神经网络 构造(python库) 人工智能 树(集合论) 短时记忆 时间序列 机器学习 循环神经网络 任务(项目管理) 短时记忆 工作记忆 工程类 认知 数学分析 哲学 物理 认识论 神经科学 生物 程序设计语言 系统工程 量子力学 数学
作者
Xiangdong Ran,Zhiguang Shan,Yufei Fang,Lin Chen
出处
期刊:Sensors [Multidisciplinary Digital Publishing Institute]
卷期号:19 (4): 861-861 被引量:85
标识
DOI:10.3390/s19040861
摘要

Traffic prediction is based on modeling the complex non-linear spatiotemporal traffic dynamics in road network. In recent years, Long Short-Term Memory has been applied to traffic prediction, achieving better performance. The existing Long Short-Term Memory methods for traffic prediction have two drawbacks: they do not use the departure time through the links for traffic prediction, and the way of modeling long-term dependence in time series is not direct in terms of traffic prediction. Attention mechanism is implemented by constructing a neural network according to its task and has recently demonstrated success in a wide range of tasks. In this paper, we propose an Long Short-Term Memory-based method with attention mechanism for travel time prediction. We present the proposed model in a tree structure. The proposed model substitutes a tree structure with attention mechanism for the unfold way of standard Long Short-Term Memory to construct the depth of Long Short-Term Memory and modeling long-term dependence. The attention mechanism is over the output layer of each Long Short-Term Memory unit. The departure time is used as the aspect of the attention mechanism and the attention mechanism integrates departure time into the proposed model. We use AdaGrad method for training the proposed model. Based on the datasets provided by Highways England, the experimental results show that the proposed model can achieve better accuracy than the Long Short-Term Memory and other baseline methods. The case study suggests that the departure time is effectively employed by using attention mechanism.
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
hauru完成签到,获得积分10
刚刚
1秒前
2秒前
erhao完成签到,获得积分10
2秒前
苹果笑寒发布了新的文献求助20
2秒前
2秒前
YamDaamCaa应助要减肥的断秋采纳,获得30
3秒前
Lucas应助电脑桌采纳,获得10
3秒前
3秒前
我不李解发布了新的文献求助10
3秒前
ljw发布了新的文献求助10
3秒前
4秒前
golfgold发布了新的文献求助10
5秒前
5秒前
心灵美的元枫完成签到,获得积分10
5秒前
5秒前
芒草lx完成签到,获得积分20
5秒前
福禄小哥完成签到,获得积分10
6秒前
脑洞疼应助JXHX采纳,获得10
6秒前
宁阿霜发布了新的文献求助10
7秒前
卡卡西应助LXL采纳,获得20
7秒前
Lucas应助开心夜云采纳,获得10
7秒前
开心荔枝完成签到,获得积分10
8秒前
8秒前
神锋天下发布了新的文献求助30
8秒前
霸王丸完成签到,获得积分10
9秒前
Hello应助社恐小青年采纳,获得10
9秒前
9秒前
bocai发布了新的文献求助10
9秒前
一直完成签到,获得积分10
10秒前
10秒前
00发布了新的文献求助10
10秒前
852应助略略略采纳,获得10
10秒前
紧张的世德完成签到 ,获得积分10
11秒前
桐桐应助ccyh采纳,获得10
11秒前
12秒前
12秒前
ajing发布了新的文献求助10
13秒前
情怀应助我不李解采纳,获得10
13秒前
CLY完成签到,获得积分10
14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
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
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 3971091
求助须知:如何正确求助?哪些是违规求助? 3515797
关于积分的说明 11179488
捐赠科研通 3250872
什么是DOI,文献DOI怎么找? 1795536
邀请新用户注册赠送积分活动 875891
科研通“疑难数据库(出版商)”最低求助积分说明 805207