Application of Bayesian stochastic learning automata for modeling lane choice behavior on high-occupancy toll lanes on State Road 167

This paper investigates the learning behavior of users of State Road 167 high-occupancy toll lanes by use of toll transaction data collected over a 6-month period. The Bayesian stochastic learning algorithm theory was used to model drivers' sequential lane choice decisions. Reward and penalty parameters were used to update users' lane choice probabilities. The results showed that the effect of reward parameters that increased the probability of selection of an alternative after a satisfactory experience was more obvious than the effect of penalty parameters that decreased the probability of selection of an unfavorable choice. Low magnitudes of learning parameters might indicate strong habit formation of users. Moreover, the posterior distributions of learning parameters indicated that user perception heterogeneity existed when the outcomes of choices were evaluated. Finally, user familiarity was investigated with a subsample of less experienced users, and it was shown that the learning rates of more familiar users were lower than those of less familiar users.