  Discovering an optimal route to the most feasible parking lot has been a
matter of concern for any driver which aggravates further during peak hours of
the day and at congested places leading to considerable wastage of time and
fuel. This paper proposes a Bayesian hierarchical technique for obtaining the
most optimal route to a parking lot. The route selection is based on
conflicting objectives and hence the problem belongs to the domain of
multi-objective optimization. A probabilistic data driven method has been used
to overcome the inherent problem of weight selection in the popular weighted
sum technique. The weights of these conflicting objectives have been refined
using a Bayesian hierarchical model based on Multinomial and Dirichlet prior.
Genetic algorithm has been used to obtain optimal solutions. Simulated data has
been used to obtain routes which are in close agreement with real life
situations.
