A Study on the Share Rate of High-capacity Public Transportation on the Airport Landside Based on Mixed Logit Model

Scientifically predicting the share rate of high-capacity public transportation on the airport landside can provide quantitative support for the scale and capacity allocation of landside transportation facilities. Existing methods often have difficulty effectively depicting the significant differences in factors such as travel time value, travel purpose, and tolerance for transfers among travelers. Prediction results usually have issues such as being biased towards the average and lacking accuracy. Based on the analysis of the characteristics of public transportation passenger flow, this study takes into account the individual heterogeneity of travelers and develops a mixed logit generalized cost (MLGC) method for public transportation share rate by combining the mixed logit (ML) model with the generalized cost model. The fuzzy c-means (FCM) algorithm is used to select travel duration, average transfer times, and average waiting time as features. Based on the elbow rule, landside travelers are clustered into three heterogeneous groups. Taking travel time and travel cost as random variables, the parameters are classified into 4 specific attribute parameters according to private mode, rail transit, airport bus, and airport shuttle bus. Each variable is processed for binary classification. Meanwhile, elements such as perception of travel, waiting perception, and crowded environment are incorporated into the generalized cost function to construct a transfer impedance coefficient that includes spatial semantics. In addition, a three-level testing system was established, and model comparison and verification were carried out based on the Akaike information criterion (AIC) and log-likelihood values. Taking the Capital Airport as the empirical object, the model is calibrated using 2 067 valid questionnaires. The results showed that the McFadden value of the MLGC model reached 0.255, the fitting coefficient R² is 0.924, and the log-likelihood value and AIC perform better than others. Sensitivity analysis indicates that when the train interval is reduced by 70%, the share rate increases to 26%, and when the transfer travel time is reduced by 90%, the overall share rate of public transportation exceeds private transportation. Moderately lowering ticket prices could significantly enhance the competitiveness of rail transit. The MLGC model can effectively reveal the influencing mechanism of the impact of airport landside public transportation choices. Reasonable train intervals, optimized ticket pricing mechanisms, and convenient transfer conditions can significantly increase the share rate of public transportation.