A Method of Multi Granularity Intercity Passenger Demand Forecasting for Operation Management

Passenger volume is a basic indicator of the demand for inter-provincial passenger transport,which reflects levels of operation and management in this industry.A forecasting model with multi-granularity for passengers' volume in one year or on holidays is developed to promote level of management,travel efficiency of passengers,and capacity of emergency response.A prediction model of passengers' volume using BP neural network based on correlation analysis of influencing factors and annual passengers' volume is developed.Considering special influencing characteristics of passengers' volume on holidays,a forecasting model combines an exponential smoothing model and a seasonal model is proposed.The total and daily volume of inter-provincial passenger transport during holidays is predicted.Taking actual transport data in Beijing as a case study,the accuracy of the prediction model is verified.The results show that the average relative error of the prediction model of annual passenger volume is 0.15%,and the average relative error of the prediction model of daily passenger volume during the Spring Festival is 6.70%.These indicating that the prediction models can reflect the variation trend of passenger volume in different periods,and has good stability.