Research On Xibaipo Memorial Tourist Flow Forecast And Control System Based On Indoor Location

Tourism is called as the "never decline" sunrise industry. With its growth, variousproblems follow. Especially in the tourist season, the number of visitors will greatly exceedthe scenic reception capacity, which can easily lead to congestion, stampede and otheraccidents. This paper studies on tourist attractions that have narrow space and few entrances.Based on advanced information technology, we obtain real-time information on thewhereabouts of tourists and predict the future of the specific area of dense information, andthen avoid accidents.Based on the detailed analysis of the factors affecting Xibaipo memorial crowd density,this paper predicts the visitors density of the Museum from the perspective of in and outtourists flow of the Museum.In the admission tourists flow forecasting, the paper sums upthe number of factors that affect the tourists’ age, travel purpose, travel time, travel routes,sightseeing speed, attractions heat and other factors. Then it use advanced technology ofBeidou to forcast real-time access and calculate tourists walking path, speed and other data.Combining of historical data, we analyze the heat spots, attractions and tour time and otherinformation, proposed time prediction model based on tourist routes and tourist characteristics,and correct the genetic algorithm to achieve a certain moment the number of visitors into themuseum’s forecast. Forecast for the museum out visitor numbers, the paper design modelbased WIFI location fingerprinting method to achieve venues indoor positioning technology,the measured positioning accuracy can reach3meters, witch can meet the needs of visitorsXibaipo memorial density Surveillance. Combining the above two methods, it ultimatelyachieves tourists reasonable diversion to avoid accidents. Experiments show that the methodbased on the location of the fingerprint database to build the model, compared with artificialmethods, can improve the efficiency of database construction to a large extent. Geneticalgorithm parallelism optimize the characteristics of the combination of factors, witch canavoid the randomness of random tree traditional search results, improving the success rate andthe accuracy of the prediction of the route.