Longitudinal analysis of public transport usage by older people using a latent Markov model

ABSTRACT

Understanding trends in public transport usage by older people over the years is important for the long-term planning and development of age-friendly public transport. Despite extensive research on travel patterns of older people, very limited studies have explored the evolution oftheir public transport usage over the years. Using six years of smartcard transaction data and anonymous cardholder membership data from Shizuoka, Japan, we develop a latent Markov model to account for unobserved heterogeneity and to analyze the evolution of public transport usage by older people. The results indicate that age, gender, and residential built environment influence trends in publictransport usage by older adults over the years. There are five latentstates for older men and women, respectively, demonstrating theexistence of unobserved heterogeneity. Propensity to use public transport affected by unobserved factors tends to retain the same, especially for older people in the latent states of very low, low, and very high propensities.

KEYWORDS:

• Public transit
• older adults
• longitudinal analysis
• latent Markov model
• unobserved heterogeneity
• smart card data

Author statement

Shasha Liu: Conceptualization, Methodology, Software, Visualization, Writing - Original Draft, Writing - Review & Editing. Toshiyuki Yamamoto: Conceptualization, Writing - Review & Editing, Supervision. Toshiyuki Nakamura: Resources, Project administration.

Acknowledgments

The authors sincerely thank the Shizutetsu Group for providing smart card data for this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [2021PT207]; Open Research Fund from Laboratory of Advanced Public Transportation Science (APTS Lab) of China Academy of Transportation Sciences [2022-APTS-03]; National Natural Science Foundation of China [52102387].