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Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model.
- Source :
-
Sensors (14248220) . Dec2022, Vol. 22 Issue 23, p9456. 13p. - Publication Year :
- 2022
-
Abstract
- Accurately forecasting the demand of urban online car-hailing is of great significance to improving operation efficiency, reducing traffic congestion and energy consumption. This paper takes 265-day order data from the Hefei urban online car-hailing platform from 2019 to 2021 as an example, and divides each day into 48 time units (30 min per unit) to form a data set. Taking the minimum average absolute error as the optimization objective, the historical data sets are classified, and the values of the state vector T and the parameter K of the K-nearest neighbor model are optimized, which solves the problem of prediction error caused by fixed values of T or K in traditional model. The conclusion shows that the forecasting accuracy of the K-nearest neighbor model can reach 93.62%, which is much higher than the exponential smoothing model (81.65%), KNN1 model (84.02%) and is similar to LSTM model (91.04%), meaning that it can adapt to the urban online car-hailing system and be valuable in terms of its potential application. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 23
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 160741566
- Full Text :
- https://doi.org/10.3390/s22239456