Study On Prediction Model Of Mountain Tourism Scenic Area Traffic

With the harmony development and dramatical increasing of people's living standard in the society in China,the tourism has showed its vigorousness.The rapid development of tourism,however,increase the difficulties of scientific decision-making by administrator in tourist attraction,especially in mountain type scenic spot.On account of its unique geomorphic feature,it is more difficult and prominent to solve the problems in resource scheduling and protection.In order to decrease the difficulty of coordinated management in mountain type scenic spot,the author build a mountain type prediction model for day passenger flow volume,thus the administrator in the spot was provided scientific decision basis to make the environment of mountain type scenic spot maintain its vigorousness in scientific layout and discriminative decision.The main contents of this dissertation are as follows:(1)According to the collection from domestic and foreign scholars' literature review,regarding the data of six years day-passenger flow volume in Huangshan Mountain scenic spot as the object of study,the author analyzed the influence factor and characteristic of change of day passenger flow volume in mountain type scenic spot.On account of the change of day passenger flow volume in Huangshan Mountains scenic spot,the author divided the day passenger flow volume into regular and holiday,therefore,the author built up different prediction models for day passenger flow volume,which based on different time.(2)To build up the regular prediction model,which bases on the grey system,the author aimed at the characteristic of the regular day passenger flow volume data in Huangshan Mountains.In the research,the author cited the GM(1,1)model as its basic prediction model.This paper offers some optimizations for the flaws in the basic model:promoting the reliability of the results from the basic grey prediction model by optimizing metabolism;promoting the disciplines of the sample series by optimizing the sample series with smooth index;promoting the accuracy of the prediction model by optimizing the sample series with fixing the residual error.At last,the author organized several optimized ways together,and build us the regular day passenger flow volume prediction model,which bases on the combined optimized way of grey system.Proving by the experiment,the regular day passenger flow volume prediction model,which bases on the combined optimized ways of grey system prediction,meets therequirement of the predicted accuracy.(3)According to the highly cyclical data of the holiday day passenger flow volume in Huangshan Mountain,the author builds up a holiday day prediction model,which bases on the neural network.Selecting the holiday day passenger flow volume in Huangshan Mountain as the sample of model,through comparing different accuracy from different prediction model of influence factor and parameter values,the author confirmed the main building elements of model.Approving by the experiment,the prediction model,which bases on the neural network,is better than the grey system's prediction model for the holiday.