A Hybrid Method Based on Chaotic Phase Space Restructuring and Comparability Principle for Railway Passenger Demand Forecasting

The phase space of railway passenger demand time series data is restructured based on the theory of phase space restructuring in this paper, and the conclusion that railway passenger demand time series data has chaotic property is deduced. According to the annual fluctuation characteristic of China railway passenger demand, a short-term forecasting model of railway passenger demand based on integrating chaotic phase space restructuring and principle of comparability is proposed. In the model, the inside-regularity of passenger demand evolvement is abstracted from the history data of passenger demand, the trend of passenger demand evolvement in the same term of similar year is taken into account, and the forecasting value can be automatically optimized. Test results for using this model to forecast the short-term railway passenger demand of an actual railway station are reported, and show the proposed model is available, effective and superior.