Public Transit Passenger Quantity Forecast Based on Improved Multi-Variable Grey Self-Adaptive Model

The research of urban public transit passenger quantity possesses important value for the rapid development of public transport and the effective relief of urban traffic pressure. The passenger transport system is a multi-level and nonlinear complicated system affected by multi-factors. Because the relation between urban public transit passenger flow and the affecting factors has grey characteristics, in addition the existing statistics data are of short and incomplete, we put forward an improved grey forecast model to predict urban public transit passenger quantity. First, according to the calculation of grey incidence, the key factors, called forecast variables, of urban public transit passenger transportation quantity are found out. These factors are the region total output value and the amount of service bus. Next, on the basis of the grey incidence result, construct the improved multi-variable grey self-adaptive MGM (1, n) model. Increase the forecast value to the known sequence on by one; meanwhile, replace individually the old data with relative large forecast error. Filling vacancy in proper order and replacing in this way until achieving the forecast goal. Finally, employ the model in public transit passenger quantity forecast in Shijiazhuang city, Hebei province, and obtain good forecast results. Case study indicates that the improved model can not only reflect the mutual influence and restrict relations of multi-variables in the passenger transport system, but also overcome the traditional forecasting method's shortage failing to response to the outside influence factors. The error between the forecast results and the real one is small; therefore, the improved model has good application value in the passenger quantity forecast of public transit.