The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach
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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.
KeywordsDynamic mode choice Hidden Markov Transition matrix Heterogeneous
This research is financially supported by a National Science Foundation (NSF) CAREER Award, “Reliability as an Emergent Property of Transportation Networks”, and the U.S. Federal Highway Administration (FHWA) Exploratory Advanced Research Program. The authors are grateful to Neil Kilgren and Carol Naito affiliated with Puget Sound Regional Council for kindly provide Puget Sound Transportation Panel data and supplemented Puget Sound regional planning skimming matrices. The authors would like to thank Chen Dong affiliated with the Department of Mathematics, Univ. of Maryland, for his advice in addressing various hidden Markov model estimation issues. The opinions in this paper do not necessarily reflect the official views of NSF or FHWA. They assume no liability for the content or use of this paper. The authors are solely responsible for all statements in this paper.
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