A high-order hidden Markov model and its applications for dynamic car ownership analysis
Published in Transportation Science, 2018
Recommended citation: Xiong et. al., (2018). "A high-order hidden Markov model and its applications for dynamic car ownership analysis." Transportation Science. 52 (6), 1365-1375. https://pubsonline.informs.org/doi/abs/10.1287/trsc.2017.0792
This paper extends the dynamically formulated hidden Markov models to a high-order hidden Markov model (HO-HMM) formulation. In the HO-HMM, the Markovian assumption that the future states (interpreted as the states of preferences or attitudes) depend only on the current state has been relaxed. Instead, the HO-HMM generalizes that the future states will depend on a number of states occurring beforehand.