Revealing Commute Choice Factors: SEM Analysis of Public Transport and Active Modes in Hyderabad, India

• Published in: Journal of urban planning and development 2024-09, Vol.150 (3)
• Main Authors: Tejaswi, S. Padma, Balijepalli, Chandra, Prasad, C. S. R. K.
• Format: Article
• Language: English
• Subjects: Chaining; Decision making; Factor analysis; Mathematical models; Modal choice; Modelling; Public transportation; Technical Papers
• ISSN: 0733-9488; 1943-5444
• DOI: 10.1061/JUPDDM.UPENG-5022

Abstract While extensive research has been conducted to explore factors influencing mode choices and first- or last-mile connectivity, few studies have delved into the underlying hierarchy of decision making processes. An understanding of this hierarchy, which illustrates causal relationships, is crucial for modeling travel decisions, as trip structure depends on choice behavior and vice versa. Traditional mode choice models often neglect these underlying causal relationships, necessitating the development of new models. By incorporating mediating effects of trip chaining and mode choice, alongside traditional factors, a more holistic understanding of mode choice behavior is achieved. In this study, hierarchical relationships are identified between trip chaining and mode choice in Hyderabad, India, using a structural equation modeling (SEM) method, owing to its inherent strength in handling latent causal relationships. SEM analysis provides the total effects of sociodemographic variables on mode choices and trip chain types through these causal relationships. Findings reveal that, for nonwork trips, the decision making process is simultaneous, regardless of the mode chosen. In contrast, for work trips, the decision making process is simultaneous for active and public modes, but the choice of mode precedes trip chaining for private modes. Furthermore, in this study, we learn from those who own private vehicles but use active or public transport by choice and extract the factors that had indeed motivated their choice. Confirmatory factor analysis is employed to validate the identified factors. The identified factors, coupled with the understanding of decision making hierarchy, offer valuable insights for shaping policies that can maximize the potential of active and public transport modes.