Stated Preference Analysis With Latent Variables for Higher Speed Rail

Recently, the necessity to redefine the mode selection model (modal split model) has emerged owing to the diversification of transportation systems. In addition to socio-economic factors, preference factors and user characteristics significantly affect the selection of transportation mode. Therefore, user preferences are reflected in the modal split model. The latent class model is highly descriptive, and its suitability can be improved. With the advent of high-speed rail (Great Train eXpress [GTX]), Seoul has established a competitive and complementary relationship for the demand of passengers between general and great express rail. Therefore, a modal split model reflecting the characteristics of the GTX, which is redefined by applying a latent variable to the stated preference analysis, is necessary. Latent class analysis uses the Bayesian information criterion and log-likelihood to distinguish the travel property (Cluster1), station property (Cluster2), and transfer property (Cluster 3). After comparing the time values for each cluster, the GTX preference is analyzed based on the inner and outer circle situations. A model that accurately reflects the characteristics and preferences of passengers is proposed in this paper. In the future, strategies can be established for the inner and outer circle situations, and higher operational efficiency can be achieved by combining the GTX and subway.

Automated summary

Recently, the need to redefine the modal split model has arisen due to the diversification of transportation systems. This paper proposes a modal split model that reflects the characteristics and preferences of passengers by applying latent class analysis.