Abstract
A model that anticipates the passenger flow on trains will help passengers to avoid overcrowded trains in their journey planning. Such a model will also help rail industry to understand the current use of train capacity and plan the distribution of rolling stock, personnel and facilities. However, the existing studies only developed the models for forecasting the passenger flow in stations, which cannot reflect the true passenger number on trains. In this paper, a hierarchical modelling framework for passenger flow prediction is proposed. It includes two layers of fuzzy models, where a global model is used to predict for ordinary circumstances and a number of local models are used to predict the variations in passenger number due to specific factors, such as events and weather. A new data sifting method is proposed to obtain the most informative and representative data for model training, which greatly improves the modelling efficiency. The proposed method is then validated using a case study of forecasting the passenger flow of London Underground trains.
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