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Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features

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Abstract

With the more and more in-depth research on intelligent transportation, many scholars have proposed their models for accurate prediction of traffic. In this paper, we analyze the advantages and disadvantages of the existing models and propose our own model. In our model, the temporal and spatial factors are taken into account. Gate Recurrent Unit (GRU) and Gated Linear Units (GLU) are used to learn the short-term temporal features of traffic data, and Graph Convolutional Network (GCN) is used to learn the spatial features of traffic data. In order to fully learn short-term feature changes, a multi time step perception layer is proposed. A new network GCGRU is proposed to learn the long-term features of traffic data. As the sensor will be affected by urban canyon, weather, and other factors, there will be missing value and noise in the collected data. We created a short-term trend based missing value filling up algorithm to fill in missing values and use Singular Spectrum Analysis (SSA) algorithm to eliminate noise of training data set. In order to reduce the process of adjusting parameters manually in the model training process, we propose k-block search method based on fuzzy extreme points. Finally, the model is compared with the existing traffic forecasting models, and the analysis results show that our model has advantages in many indicators.

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Data Availability

https://doi.org/10.21227/9awj-4d85 updated.

Code Availability

https://doi.org/10.21227/9awj-4d85 updated.(Code and data are put together)

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Funding

This work was supported in part by the National Natural Science Foundation of China: [ NO.61772327, NO.61532021]

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Authors and Affiliations

  1. College Of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, China

    Chunhui Xu, Anqin Zhang & Yu Chen

  2. Inventec Appliances (Pudong) corp, Shanghai, 200090, China

    Chunchen Xu

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  1. Chunhui Xu
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  2. Anqin Zhang
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  3. Chunchen Xu
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  4. Yu Chen
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Contributions

(All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chunhui Xu, Anqin Zhang and Chunchen Xu. The first draft of the manuscript was written by [Chunhui Xu] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.)

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Correspondence to Anqin Zhang.

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Xu, C., Zhang, A., Xu, C. et al. Traffic speed prediction: spatiotemporal convolution network based on long-term, short-term and spatial features. Appl Intell 52, 2224–2242 (2022). https://doi.org/10.1007/s10489-021-02461-9

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