The Predictability of Short-Term Urban Rail Demand: Choice of Time Resolution and Methodology

Abstract

The accuracy of short-term demand forecasting is critical for real-time operation management of urban rail transit, which largely depends on the choice of time resolution. Although there have been continuous improvements in forecasting models, the basic issue has not been well addressed. In this regard, the predictability of short-term demand in terms of time resolution setting and the corresponding model selection have been addressed in this study. Two methods have been considered: the demand forecasting with the past demand during the same time slot on the same weekday (the same period method); and that with continuous time series demand exactly before the forecasted time slot (time series method). The predictability for these two methods was respectively measured by the similarity of the same period and the stability of the time series. Consequently, the influence of time resolution on the predictability of short-term demand for urban rail transit has been evaluated. With the methods proposed, this study conducted an analysis on five-week smartcard data in the Beijing subway system. Results suggest that the predictability of short-term demand presented remarkable heterogeneity in both time and space. The predictability of demand forecasting at station level has been summarized into different levels, and the corresponding methods can be selected for each class. Generally, to ensure a desirable accuracy, forecasting can be made at a 10-min and 60-min interval on weekdays and weekends, respectively. The same period method works better for the short-term demand forecasting on weekdays. While the time series method performs better for prediction on weekends. As for short-time OD (origin-destination) demand, the time series method with a 10-min interval, which is supplemented by the same period method, can generate acceptable forecasting results. In brief, this study provides suggestions on the time resolution and method selection for short-term demand forecasting.