Design of Air Passenger Travel Choice Intention Prediction System Based on Deep Learning

Abstract

Under the Beijing-Tianjin regional comprehensive transportation system, the flow of air passengers between multiple airports in the region is more frequent. The fundamental reason for the flow of air passengers is that there are differences in the level of service quality provided by airports and airlines in the region. Passengers' choice intention is the consumption and purchase decision of passengers on aviation services. By constructing a Logit model, this paper analyzes the degree of influence on the travel choice intention of air passengers in the Beijing-Tianjin region from five aspects: individual passenger demographic characteristics, travel purpose, ground transportation characteristics, airport operation capacity, and airport soft power. Passengers can effectively predict the choice of air travel mode in the Beijing-Tianjin region. The results show that Beijing Capital Airport is favored by business travelers; Beijing Daxing International Airport is favored by travelers because of its fast security check-through speed; for Tianjin Binhai International Airport, the convenience of getting in and out of the airport by car and the speed of airport security check-through are two significant factors. Indicators do not affect the selection of airports; reasonable follow-up arrangements when airport flights are delayed are the only significant but negatively correlated factor; the research design results provide new ideas for the analysis of passenger travel mode selection behavior in multiple airport areas, enriching the data-driven research on transportation choices.