In order to solve the problem of insufficient quantitative analysis of the impact of air passengers' travel characteristics on airport landside transport mode choice behavior, we deconstructed air travel characteristics based on three aspects: personal attributes, travel attributes, and transfer scenarios, elucidated the impact of travel characteristics on passengers' actual transfer behaviors, and utilized an artificial neural network (ANN) model to discriminate the degree of importance of travel characteristics variables, and established a passenger transfer transport mode choice multinominal logit (MNL) model, and finally calibrated and tested the model using passenger questionnaire data from a large Chinese airport in 2019, revealing the behavioral characteristics and marginal effects of airport passengers' transfer choices. The results show that: passenger gender, education, and travel purpose differences all have a weak effect on interchange transportation mode choice, and flight time has a non-significant effect on passenger choice; when the probability of taking airport buses decreases continuously with the gradual increase in the number of annual air trips of the passengers; as the time point of the passenger's transportation mode decision is moved further back in the future, the more they are inclined to take cabs, the more they exclude the airport buses; when there are people traveling with them, the passenger's The probability of choosing a cab will become 2.2 times when passengers change from leaving directly to planning to leave after a stopover at the airport, the tendency of choosing airport buses versus online taxis will be more obvious.
Analytical model of airport passenger transportation transfer mode choice based on air travel characteristics
Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023) ; 2023 ; Dalian, China
Proc. SPIE ; 13064
2024-02-20
Conference paper
Electronic Resource
English
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