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For railway companies, the benefits from revenue management activities, like inventory control, dynamic pricing, and so forth, rely heavily on the accuracy of the short-term forecasting of the passenger flow. In this paper, based on the analysis of the relevance between final booking amounts and shapes of the booking curves, a novel short-term forecasting approach, which employs a specifically designed clustering algorithm and the data of both historical booking records and the bookings on hand, is proposed. The empirical study with real data sets from Chinese railway shows that the proposed approach outperforms the advanced pickup model (one of the most popular models in practice) during the early and middle stages of booking horizon when bookings are not concentrated in the final days before departure

Short-Term Forecasting of Railway Passenger Flow Based on Clustering of Booking Curves