Attitudinal Market Segmentation for Transit Riders Using Factor Analysis

Abstract

Increasing the ridership of urban transit system is crucial for sustainable development of transportation. Previous studies have succeeded in using attitudinal market segmentation to understand the travel demand. However, those related to transit market mostly depend on complex statistical methods including structural equation modeling (SEM) to extract latent attitudinal variables. It adds difficulty to their real-world applications. This study uses simple principle component analysis (PCA) method to help the extraction of attitudinal variables. Results show that PCA is able to provide significant market segmentation. Policy implementations are tailored to the four submarkets, which is valuable to transit system development.