To reduce the time cost of training large scale parking data prediction model, this paper focuses on simplification of parking network topology from the graph coarsening perspective. First, merge adjacent parking lots into hypernodes via spectral distance to downscale the size of original parking network and form the coarsened graph while trying to keep the spatial characteristics of topology. Next, train an autoencoder to further compress the merged data of hypernode, getting a low-dimensional and denser representation which is more effective for spatiotemporal prediction. Then carry out prediction on the coarsened graph and obtain the results of hypernodes which are coarser than the intended predicted parking data. Finally decode the coarsened results via the pretrained autoencoder, restoring back to the original form of parking data to complete the whole task. The experimental results show that our proposed method improves the training efficiency by 1.61-2.73 times and reduces the error rate by 43.9%-51.5% on the real-world datasets compared to the traditional large scale parking data prediction methods.


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    Titel :

    Large-Scale Parking Data Prediction: From A Graph Coarsening Perspective


    Beteiligte:
    Wang, Yixuan (Autor:in) / Ku, Yixuan (Autor:in) / Liu, Qi (Autor:in) / Yang, Yang (Autor:in) / Peng, Lei (Autor:in)


    Erscheinungsdatum :

    24.09.2023


    Format / Umfang :

    1338487 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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