计算机科学
流量(计算机网络)
聚类分析
深度学习
交通生成模型
数据挖掘
互联网
交通工程
方案(数学)
人工智能
互联网流量工程
互联网流量
交通分类
智能交通系统
浮动车数据
实时计算
工程类
计算机网络
交通拥挤
运输工程
万维网
数学分析
数学
作者
Chen Chen,Ziye Liu,Shaohua Wan,Jintai Luan,Qingqi Pei
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-06-01
卷期号:22 (6): 3776-3789
被引量:63
标识
DOI:10.1109/tits.2020.3025856
摘要
In Internet of Vehicles (IoV), accurate traffic flow prediction is helpful for analyzing road condition and then timely feedback traffic information to managers as well as travelers. Traditional traffic flow predictions are generally suffering from the performance degradation by over-fitting and manual intervening, which cannot support large-scale and high-dimensional urban road network data. To address this issue, in this paper, a traffic flow prediction framework for urban road network based on deep learning is proposed. Firstly, the feature engineering is introduced to extract the features from a large volume of traffic dataset, with the anomaly nodes eliminated. Next, the big traffic dataset is compressed through the spectral clustering compression scheme. Finally, we designed a hybrid traffic flow prediction scheme based on LSTM (Long Short Term Memory) and Sparse Auto-Encoder (SAE). Experimental results show that our proposed model is superior to other models with an average prediction accuracy approaching 97.7%.
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