Research On Railway Ticketing Data Sampling And Short-term Forecast Of Passenger Flow

In the increasingly competitive transportation market, railway transport enterprises must enhance the scientific level of marketing management, and respond to market changes rapidly and flexibly in order to meet the new requirements of market economy and self-development, which are based on timely and accurate analysis and forecast of short-term passenger flow.This paper mainly research on railway ticketing data sampling and short-term forecast of passenger flow, using the sample of sold ticketing data in pre-sale period and real-time ticketing data, forecast every follow-up day's ticketing volume in pre-sale period, and calculate the total passenger flow volume.In order to provide the basis for the ticketing data sampling method and forecast algorithm, the paper first analyzed timing law on various dimensions of historical data, made the assumption of statistical distribution, and then verified it by goodness of fit test.Based on the one-day time series features of ticketing volume in different session, this paper proposed nested sampling method for ticketing data, which is three-stage stratified sampling, and verified the validity of this method based on actual data.In the same way, based on sold ticketing data, using radial basis function (RBF) neural network, the paper proposed a method to forecast follow-up days' ticketing volume in pre-sale period, and made use of real-time ticketing data to forecast intraday ticketing volume, in order to amend the final results.finally, using historical data, the three-stage stratified sampling method mentioned above and short-term forecast method of passenger flow, the paper forecast one-day passenger flow volume of the OD from Wuhan to Guangzhou, for cases of both daily and holiday respectively. The results validated the proposed methods are effective and feasible.