Bayesian hierarchical multi-objective optimization for vehicle parking route discovery

Discovering an optimal route to the most feasible parking lot has been a matter of apprehension for any driver. Selecting the most optimal route to the most favorable lot aggravates further during peak hours of the day and at congested places. This leads to a considerable wastage of resources specifically time and fuel. This work proposes a Bayesian hierarchical technique for obtaining this optimal route. 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 have been used to obtain routes which are in close agreement with real-life situations. Statistical analyses have shown the superiority of the weights obtained using the proposed algorithm based on Bayesian technique over the existing frequentist technique.