A new forecasting system for high-speed railway passenger demand based on residual component disposing

Accurate passenger demand forecasting of high-speed railway is of great significance for railway line planning and daily operation management. Capturing the random factors hidden in complex data is the key to achieve accurate forecasting. In view of this, this paper presents a new system with deterministic and probabilistic forecasting capacities based on the residual component disposing. First, the passenger demand is decomposed into trend, seasonality components and remainder using seasonal and trend decomposition using loess. Most likely some weak but indispensable information may still be contained in the remainder component, and random incidents occurred in the past may also occur in the future. Thus, moving block bootstrap is employed to bootstrap the remainder and generate one thousand similar samples by virtue of extrapolating the disaggregated subseries, thereby simulating the stochastic feature of future series. The forecasts of similar sample are acquired through reaggregating the results of extrapolations. Eventually, the bagging predictors is applied to attain the deterministic and probabilistic prediction. Two real-world case study manifests that the proposed hybrid system provides a more accurate assessment of passenger demand than all the benchmark models in various aspects, and the theoretical framework established in this paper is of certain enlightening significance for dealing with complex data.