Open AccessShort-term Demand Forecasting of Shared Bicycles Based on Seasonal Grey Markov Model
Author(s) -
Hengzi Liu,
Yong He,
Tailong Song,
Pengfei Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1903/1/012059
Subject(s) - residual , markov chain , randomness , markov model , statistics , term (time) , mathematics , hidden markov model , value (mathematics) , mean absolute percentage error , computer science , econometrics , mean squared error , algorithm , artificial intelligence , physics , quantum mechanics
According to the characteristics of periodicity, nonlinearity and randomness of shared bicycle riding data, a seasonal grey Markov model was proposed. In the new prediction model, the seasonal GM (1,1) model was used to get the prediction results firstly. Then, Markov model was used to modify the prediction residual. In the process of residual correction, the residual sequence was selected according to the new information priority principle, and the residual was modified by the expectation of the median value in the state interval, which improves the prediction accuracy of the model. Finally, the model was applied to the demand forecast of the Citi Bike shared bicycles in New York, the first three weeks of June 2019 during the Saturday peak hours. The numerical results show that the mean absolute percentage error (MAPE) of predicted value of seasonal grey Markov model is 2.72%, which is superior to the traditional GM (1,1) model, seasonal GM (1,1) model and grey Markov model.