Evolving Dynamics of Commuter Adoption Behavior of Metro: A Bayesian MCMC Analysis of Stated and Revealed Preferences in Emerging Urban Contexts

Abstract

Rapid motorization in Dhaka has worsened congestion, motivating the launch of Mass Rapid Transit (MRT) as a potential solution. However, metro adoption depends not just on infrastructure but on commuter perceptions, intentions, and actual behavior. To track the dynamic evolution of commuter adoption over time, the study employs a unique three-stage Bayesian framework—Pre-MRT Stated Preference (SP), Post-MRT SP, and Post-MRT Revealed Preference (RP) for MRT line-6. Bayesian logistic regression with Markov Chain Monte Carlo (MCMC) estimation captures posterior distributions and parameter uncertainty, offering insights into the shifting determinants of MRT adoption. The pre-MRT SP model (pseudo R2 = 0.0668) identified affordability as an incentive but highlighted concerns around safety and reliability. Post-MRT, the SP model (pseudo R2 = 0.186) found that socio-demographic factors, including gender and employment, strongly influenced preferences, while the RP model (pseudo R2 = 0.502) showed that actual behavior was most influenced by proximity to stations, education, and security perceptions. Overall, the findings reveal that expectations and actual behavior often diverge, with adoption maturing over time. The evidence indicates that commuter adoption evolves with system maturity, requiring policies that first build affordability and integration, then strengthen safety and reliability, and ultimately enhance accessibility and long-term efficiency.