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Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction
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DOI：10.1109/ACCESS.2020.2994655.png)
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摘要
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In order to eliminate outliers in traffic flow data collection and promote the generalization performance of traffic flow prediction, this paper proposes a dynamic optimization long short-term memory (LSTM) model based on data preprocessing for short-term traffic flow prediction. A new classification algorithm named Asym-Gentle Adaboost with Cost-sensitive support vector machine (AGACS) is used for preprocessing traffic flow data. AGACS tries to employ Cost-sensitive SVM (CS-SVM) as weak component classifier in Asymmetric Gentle AdaBoost, and divide the data collection into outlier data and normal data. Only normal data is used for training LSTM to predict traffic flow and an improved chaotic Particle Swarm Optimization (CPSO) is used for dynamic optimizing hidden layer structure of LSTM to promote the generalization and robustness performance of model. The efficiency of the proposed method is proved with real traffic flow data, and the experimental results show that preprocess collecting data and dynamic optimize model structure are conducive to improve the performance of algorithm, and the proposed method achieved better performance than those of three other classical deep learning prediction methods.
Keyword:
Predictive models
Classification algorithms
Prediction algorithms
Support vector machines
Adaptation models
Heuristic algorithms
Machine learning
Traffic flow prediction
deep learning
long short-term memory
support vector regression
particle swarm optimization
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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分享收藏A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information基于模糊卷积神经网络的交通事故不确定交通流预测新方法.png)
IEEE ACCESS
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IEEE ACCESSIF3.6
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