High-precision traffic flow big data prediction based on deep learning

Fan Yu

doi:10.1117/12.2674804

2 May 2023 High-precision traffic flow big data prediction based on deep learning

Fan Yu

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126421G (2023) https://doi.org/10.1117/12.2674804
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

In order to efficiently predict and analyze massive traffic big data and improve the intelligent level of road traffic rate and urban traffic, a high-precision parallel convolutional neural network traffic flow big data prediction model based on deep learning is proposed The model first preprocesses the data to obtain an effective data set, converts one-dimensional time series samples and images with regular time intervals into two-dimensional pixel grids with one-dimensional time and one-dimensional location, constructs a parallel convolutional neural network model to predict the traffic flow through a road section, and uses prediction factors to model the traffic flow data The experimental results show that, compared with other models, the model proposed in this paper is superior to the comparison method in terms of average absolute error, average relative error and root mean square error.

Citation Download Citation

Fan Yu "High-precision traffic flow big data prediction based on deep learning", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126421G (2 May 2023); https://doi.org/10.1117/12.2674804

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