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.

中文翻译：

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用于车辆停车路径发现的贝叶斯分层多目标优化

找到通往最可行停车场的最佳路线一直是任何司机关心的问题，这在一天的高峰时段和拥挤的地方进一步加剧，导致大量时间和燃料浪费。本文提出了一种贝叶斯分层技术，用于获得通往停车场的最佳路线。路线选择基于相互冲突的目标，因此该问题属于多目标优化领域。概率数据驱动方法已被用于克服流行的加权求和技术中权重选择的固有问题。这些相互冲突的目标的权重已经使用基于多项式和狄利克雷先验的贝叶斯分层模型进行了细化。遗传算法已被用于获得最优解。