Structural equation modelling for segmentation analysis of latent variables responsible for environment-friendly feeder mode choice

This paper aims to estimate segments of latent variables associated with mode choice. The estimates of latent segmentation is obtained through Structural Equation Modelling (SEM) approach. The model is used to identify segments of homogeneous latent variables for attitude towards choice of feeder modes. The Scholars from different nations have contributed greatly in identifying conventional factors affecting the mode choice behavior. Apparently, very few studies have focused on analyzing latent variables affecting the choice of a mode. Specifically, feeder modes, which are likely to increase the mode shift to the public transport mode through improved door- bus stop connectivity. Thus, this study was carried out with an aim to identify the effect of latent variables on the mode choice behavior of the riders for proposed feeder modes. The proposed modes are Environment-Friendly for added benefits of pollution control. The selected study area is the city of Vadodara, in Gujarat state of India. For this, 41 latent variables were identified from the literature review. Data was collected through a predetermined and framed questionnaire. The latent variables with their indicators were checked by preliminary Confirmatory Factor Analysis (CFA), using SPSS software. Six hypotheses were framed to determine the interrelationship between the latent variables through Structural Equation Modelling (SEM) using the Analysis of Moment Structures (AMOS) software. Based on the outliers, communalities extraction and, correlation criteria, the indicators were reduced to 26. The results of the segmental analysis model reveal that the four framed latent variables positively affect the mode choice behavior of a rider. Hence, inculcating these latent variables while proposing an Environment-Friendly feeder mode may result in increased mode choice. Which can be predicted through choice models as a future scope. & COPY; 2022 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).