A maximum entropy optimization model for origin-destination trip matrix estimation with fuzzy entropic parameters

We formulate a bi-objective distribution model for urban trips constrained by origins and destinations while maximizing entropy. We develop a flexible and consistent approach in which the estimations of generated/attracted parameters are fuzzy with entropic membership functions. Based on a fuzzy-entropy approach, we measure the uncertainty associated with fuzzy variables. We solve the problem by means of compromise programming considering a weighted sum objective function. We compute and extend concepts such as accessibility, attractiveness, and generalized cost, typically obtained in transport economic analyzes. Considering that our formulation is convex, we solve the problem in one step only, maintaining the uniqueness of the the optimization problem solution. We present two numerical examples to illustrate the proposed methodology, analyzing the impact of the results based on strong mathematical and statistical arguments. Finally, we show that our approach has better prediction capabilities than traditional fuzzy models regarding aggregated indicators and structural distribution patterns.