@article{ref1,
title="Short-term demand forecasting of urban online car-hailing based on the K-nearest neighbor model",
journal="Sensors (Basel)",
year="2022",
author="Xiao, Yun and Kong, Wei and Liang, Zijun",
volume="22",
number="23",
pages="e9456-e9456",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22239456",
url="http://dx.doi.org/10.3390/s22239456"
}