Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity

The major challenges in dynamic activity-based models include predicting activity-related choices and understanding inherent heterogeneous preferences. The dynamic discrete choice model (DDCM) has been used for daily activity-travel planning. However, ignoring unobservable heterogeneity can bias the estimation and prediction results. To solve this problem, we propose a DDCM that accounts for unobserved heterogeneity to capture useful hidden information on travelers’ characteristics. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. The algorithm iteration depends mainly on the mapping relationship between posterior distributions and conditional choice probabilities. Meanwhile, a less complex log-likelihood function is proposed in the maximization step to estimate two types of parameters simultaneously. The proposed techniques are verified using household travel survey data from Chongqing (China). Two unobserved types of travelers, time and cost sensitivity, are identified based on the Bayesian information criterion (BIC) value. Time- and segment-varying sensitivity analyses are conducted to present choice probability differences of mode and activity scheduling under a one-unit increase in the number of schoolchildren and cars. Different impacts on activity-travel patterns, such as trip frequency, mode share, and activity schedule, generated by changes in auto ownership, are analyzed. Finally, the adjusted rho-squared value, BIC values compared with other single-effect models, and aggregate validation results demonstrate the exceptional performance of the proposed model.