Spatial And Temporal Statistical Model Of Tourist Flow In Scenic Spots Based On Mobile Phone Signaling Data

With the progress of society,China's tourism industry has been expanding rapidly.While the development of the tourism industry improves people's living standards and promotes social and economic development,it also brings a series of challenges.The surge in passenger flow has put tremendous pressure on scenic facilities and the ecological environment,and seriously affected the quality of tourists' travel.Accurate tourist flow statistics can improve the management capabilities of the scenic area,reduce the negative impact of the surge in passenger flow,and ensure the sustainable and healthy development of the scenic area.Therefore,it is of great significance to construct a scientific and accurate tourist flow statistical model for scenic spots and obtain complete and comprehensive passenger flow data.Mobile phone signaling data has the advantages of low investment cost,strong continuity,large amount of data,and wide coverage,which can more comprehensively reflect the travel trajectory of tourists from the spatial and temporal dimensions.Based on the mobile phone signaling data,this paper conducts research on the construction of the temporal and spatial statistical model of passenger flow in scenic spots.The main research contents and conclusions are as follows:(1)Research on the principle of tourist flow statistics in scenic spots based on mobile phone signaling data.The positioning principle of mobile phone signaling data is analyzed,and the accuracy characteristics and noise characteristics of mobile phone signaling data are described.Define the concept of passenger flow in scenic spots,and elaborate the basic ideas of time and space statistics of passenger flow in scenic spots.(2)A method to identify the staying state of signalling trajectory based on the scope of scenic area is proposed.According to the scope of the scenic area,this paper puts forward the concept of scenic spot associated stations set,through fusion with the base station antennas Voronoi subdivision mapping relationship with its service scope,associated stations set is determined,according to the scope of the scenic area scenic area in front of the passenger flow statistics domain for identification problem into a focus for identification,sharply reduce the passenger flow statistics computational complexity,and stay for recognition algorithm of clustering rule set provides a theoretical basis.On this basis,a stop recognition algorithm is designed,and the user signalling data is clustered according to time sequence by means of basic constraint rules and ping-pong point recognition rules.(3)The temporal and spatial statistical model framework of tourist flow in scenic spots is constructed.According to the original signaling data,the Hadoop big data platform was used as the technical support to complete the tourist flow statistics of scenic spots based on the massive signaling data.From the perspective of tourist behavior characteristics,the touch rules of core base station set and resident rules of associated base station set are used to constrain tourist data bidirectional,and the acquisition of tourist stay data and track data is completed through scenic spot stay data extraction and job and residence data elimination.Finally,the total daily passenger flow,time-segment passenger flow and playing time of the scenic spot are statistically analyzed from the time dimension.In addition to the statistical indicators of time dimension,based on the characteristics of mobile phone signaling data,this paper conducts statistics on the distribution of passenger flow and tourist play routes in local areas within the scenic spot,and uses the signaling track data of tourists throughout the day to calculate the source of passenger flow.(4)The experiment and result analysis of stop recognition algorithm and passenger flow statistical model.Collect the real stay data of volunteers in the scenic spot,and use the stay recognition algorithm in this paper to identify the stay in the scenic spot by the volunteer signaling trajectory.The results show that it has higher accuracy and completeness,and is better than the currently widely used ST-DBSCAN algorithm.In the verification of the passenger flow statistics model,using the mobile phone signaling data set provided by an operator in Nanjing,the Niushoushan Cultural Tourism Area was selected as the research area to conduct passenger flow time-space statistics.Comparing the statistical results of the total passenger flow in a single day and the passenger flow by time period with the data of Niushoushan gates,it shows a good linear fitting relationship.In addition to the traditional statistical indicators of passenger flow,statistical indicators that cannot be obtained by conventional passenger flow statistical methods such as the length of visits,the distribution of passenger flow in the scenic area,the play routes in the scenic area,and the place of origin of tourists are also obtained.These indicators can be used for scenic area planning and marketing strategies Important reference.