The weighted sum method is a simple and widely used technique that scalarizes multiple conflicting objectives into a single objective function. It suffers from the problem of determining the appropriate weights corresponding to the objectives. This paper proposes a novel Hierarchical Bayesian model based on multinomial distribution and Dirichlet prior to refine the weights for solving such multi-objective route optimization problems. The model and methodologies revolve around data obtained from a small-scale pilot survey. The method aims at improving the existing methods of weight determination in the field of Intelligent Transport Systems as data driven choice of weights through appropriate probabilistic modelling ensures, on an average, much reliable results than non-probabilistic techniques. Application of this model and methodologies to simulated as well as real data sets revealed quite encouraging performances with respect to stabilizing the estimates of weights. Generation of weights using the proposed Bayesian methodology can be used to develop a bona-fide Bayesian posterior distribution for the optima, thus properly and coherently quantifying the uncertainty about the optima.

加权和方法是一种简单且广泛使用的技术，它将多个相互冲突的目标定标为单个目标函数。它具有确定与目标相对应的适当权重的问题。提出了一种基于多项式分布和狄利克雷的新颖的贝叶斯层次模型，以优化权重以解决此类多目标路线优化问题。该模型和方法围绕着从小规模试点调查中获得的数据。该方法旨在改进智能交通系统领域中现有的权重确定方法，因为通过数据驱动的权重选择将通过适当的概率模型确保平均而言比非概率技术可靠得多的结果。该模型和方法在模拟和真实数据集上的应用显示出在稳定权重估计方面的令人鼓舞的性能。使用建议的贝叶斯方法生成权重可用于为优化建立一个真正的贝叶斯后验分布，从而适当，连贯地量化关于该优化的不确定性。