Method of Evaluating and Predicting Traffic State of Highway Network Based on Deep Learning

The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means...

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Bibliographic Details
Published in:Journal of advanced transportation 2021-03, Vol.2021, p.1-9
Main Authors: Liu, Jiayu, Wang, Xingju, Li, Yanting, Kang, Xuejian, Gao, Lu
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Case studies
Communications traffic
Deep learning
Learning theory
Long short-term memory
Machine learning
Methods
Neural networks
Performance evaluation
Prediction models
Roads & highways
Standard deviation
Traffic
Traffic congestion
Traffic flow
Traffic information
Traffic models
Transportation
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Summary:The accurate evaluation and prediction of highway network traffic state can provide effective information for travelers and traffic managers. Based on the deep learning theory, this paper proposes an evaluation and prediction model of highway network traffic state, which consists of a Fuzzy C-means (FCM) algorithm-based traffic state partition model, a Long Short-Term Memory (LSTM) algorithm-based traffic state prediction model, and a K-Means algorithm-based traffic state discriminant model. The highway network in Hebei Province is employed as a case study to validate the model, where the traffic state of highway network is analyzed using both predicted data and real data. The dataset contains 536,823 pieces of data collected by 233 continuous observation stations in Hebei Province from September 5, 2016, to September 12, 2016. The analysis results show that the model proposed in this paper has a good performance on the evaluation and prediction of the traffic state of the highway network, which is consistent with the discriminant result using the real data.
ISSN:0197-6729
2042-3195
DOI:10.1155/2021/8878494