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The competition in the taxi market is becoming increasingly fierce, but there is a gap between the demand and the number of taxis in some time periods, which not only intensifies ineffective competition among taxis but also brings inconvenience to passengers. This study aims to establish a predictive model to predict the demand for taxis in different time periods in the city. The data was collected from New York City yellow taxi data which was from June 1st, 2022 to June 6th, 2022. After processing the raw data, the optimal parameter selection of the model is determined through ADF testing to improve accuracy. Through ACF and PACF calculation, the data and images are analyzed to find the most suitable p and q values. Use ARIMA model to fit the data and obtain a model with robust fitting parameters. The distribution of predicted values is very consistent with actual data. The model was used for 50 periods of prediction, and through analysis of the research results, the fitting effect of the prediction was good. It was found that the accuracy of the model was high, proving its ability to predict short-term taxi demand.
Li et al. (Wed,) studied this question.