Research On Civil Aviation Passenger Value And Trip Forecast Model Based On Social Network

At present,major airlines have turned to the development strategies which centered on expanding the source of passenger and deepening their value to increase competitiveness.Finding potential high-value passengers to seize passenger resources early and digging into passenger demand to stimulate passenger consumption are extremely significant to them.The value of passenger is affected by their individual value and social relationships,in order to measure the value of passengers and discover potential high-value passengers,the RFMc model is realized to calculate the individual value of passengers,and the MRE model is adopted to analyse the relationship of passengers.Then CAPV-Rank algorithm for measuring the civil aviation passenger value by combining individual attributes and social relations is proposed based on the PageRank algorithm.Experimental results show that the CAPV-Rank algorithm can implement the passenger value measurement in various modes by adjusting the weighting factor.The algorithm can also realize the passenger value forecast and potential high-value passenger mining,providing a flexible and efficient solution for the measurement and forecast of civil aviation passenger value.The trip of passenger is affected by internal factors and social relationships.The individual behavior,passenger's peer relationship and passenger's similarity are analyzed to forecast the passenger airline choice.Based on the traditional link prediction model,the Passenger Airline Choice Prediction(PACP)model combining dynamic individual behavior and social influence is then imposed by introducing influence factor.Experimental results show that the introduction of multiple influencing factors in PACP model does improve the prediction efficiency dramatically,providing an efficient and extensible solution for the forecast of passenger airline choice.