The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach

Discrete choices are often analyzed statically. The limitations of static models become more obvious when employing them in more long-term travel demand forecasting. The research gap lies in a theoretical model which is dynamically formulated, and in readily available longitudinal data sources. To address this, a heterogeneous hidden Markov modeling approach (HMM) is proposed in this paper to model dynamic discrete choices. Both longitudinal and cross-sectional heterogeneity are considered. The approach is demonstrated on a travel mode choice application using ten-wave Puget Sound Transport Panel data coupled with some other supplementary data sources. Results indicate that travelers’ long-term life-cycle stages have an enduring impact when shifted to different mode choice states, wherein sensitivities to travel time and cost vary. Empirical results are put in line with static discrete choice models. The paper demonstrates that the family of HMM models provide the best fitting model. The dynamic model has superior explanatory power in fitting longitudinal data and thus shall provide more accurate estimates for planning and policy analyses. The proposed approach can be generalized to study other short/mid-term travel behavior. The estimated model can be easily calibrated and transferred for applications elsewhere.