Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility

Subway short-term ridership forecasting plays an important role in intelligent transportation systems. However, limited efforts have been made to forecast the subway short-term ridership, accounting for dynamic volatility. The traditional forecasting methods can only provide point values that are unable to offer enough information on the volatility/uncertainty of the forecasting results. To fill this gap, the aim of this paper is to incorporate the dynamic volatility into the subway short-term ridership forecasting process that not only generates the expected value of the short-term ridership but also obtains the prediction interval. Four kinds of the integrated ARIMA and GARCH models are constructed to model the mean part and volatility part of the short-term ridership. The performance of the proposed method is investigated with the real subway short-term ridership data from three stations in Beijing. The model results show that the proposed model outperforms the traditional model for all three stations. The hybrid model can significantly improving the reliability of the predicted point value by reducing the mean prediction interval length of the ridership, and improve the prediction interval coverage probability. Considering the different traffic patterns between weekday and weekend, the short-term ridership is also modeled, respectively. This paper can help management understand the dynamic volatility of the subway short-term ridership, and have the potential to disseminate more reliable subway information to travelers through the information systems.

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