2026-06-19T13:20:19Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/68582024-05-20T07:54:36Zcom_10259.4_104com_10259_2604col_10259_6848
Repositorio Institucional de la Universidad de Burgos
author
Seitbekova, Yerkezhan
author
Assilbekov, Bakytzhan
author
Kuljabekov, Alibek
author
Beisembetov, Iskander
2022-09-15T10:30:34Z
2022-09-15T10:30:34Z
2021-07
978-84-18465-12-3
http://hdl.handle.net/10259/6858
10.36443/10259/6858
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.
eng
Bicicletas
Bicycles
A prediction of bike flow in bike renting systems with the tensor model and deep learning
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/6858/1/Seitbekova_CIT20221_175-184.pdf
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Seitbekova_CIT20221_175-184.pdf