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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6858

    Título
    A prediction of bike flow in bike renting systems with the tensor model and deep learning
    Autor
    Seitbekova, Yerkezhan
    Assilbekov, Bakytzhan
    Kuljabekov, Alibek
    Beisembetov, Iskander
    Publicado en
    R-Evolucionando el transporte
    Editorial
    Universidad de Burgos. Servicio de Publicaciones e Imagen Institucional
    Fecha de publicación
    2021-07
    ISBN
    978-84-18465-12-3
    DOI
    10.36443/10259/6858
    Descripción
    Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de Burgos
    Abstract
    Rental bikes are popular in many urban areas to help people expand their mobility. It is important to make the rental bicycle usable and available to the general public at the appropriate time and place. Inevitably, providing the city with a steady supply of rental bicycles becomes a major concern. The most important aspect is the estimation of the number of bicycles required in each bicycle sharing station at any given hour. This paper gives an examination of human mobility as indicated by bicycle renting information of the bike sharing system. In this paper, we proposed a new approach for forecasting the bike inflow and outflow from one station to another during certain time slots. Our method analyses human mobility pattern by two steps: (1) Using Tuckers tensor decomposition to create a 3D tensor to model human mobility and extract latent temporal and spatial characteristics of various stations and time slots. (2) to use a Long-Short Term Memory Neural Network to model the relationship between mobility patterns and the derived latent spatial and temporal features in order to predict bike flow between stations. The main contribution of this study that with the extracted latent characteristics through Tuckers factorization we improve the accuracy of prediction by 16% and decrease the amount of training data that used in prediction. Also, a root mean squared error of prediction is 1,5 bike. We compare our model with baseline models as historical average, ARMA, the feed-forward neural network, and KNN. The proposed method showed the best results.
    Palabras clave
    Bicicletas
    Bicycles
    Materia
    Ingeniería civil
    Civil engineering
    Transporte
    Transportation
    URI
    http://hdl.handle.net/10259/6858
    Versión del editor
    https://doi.org/10.36443/9788418465123
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