New York City Taxi Order Volume Forecasts Based on SARIMA and SVR Models

Abstract

Short-term taxi order forecasting is a key reference for dynamic allocation of urban transportation capacity. New York taxi passenger flow exhibits significant intraday cyclical fluctuations, making it difficult for manual dispatch to predict changes in passenger flow. This paper takes hourly taxi order time-series data in New York City as the research object, selecting the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Support Vector Regression (SVR) model to conduct a short-term order prediction comparison. Based on AIC and BIC criteria, the optimal SARIMA parameters are traversed and screened, and SVR data are subjected to Min-Max normalization. Both types of models use multi-step rolling forecasting to generate a complete 7-day prediction sequence, relying on MAE, RMSE, MAPE, and R2 indicators to quantify prediction accuracy. Experimental results show that the optimal SARIMA (1,0,1)(1,1,24) model can accurately fit peak and valley fluctuations in passenger flow, with an R2 of 0.815. SVR models can only capture overall trends and lack the ability to characterize extreme passenger flows. The study demonstrates that for taxi time series data with strong intraday cycles, the SARIMA model offers better prediction stability and accuracy, providing a reference for taxi capacity scheduling.