Most of the problems associated with discrete choices involve a small number of alternatives. In cases with many alternatives, however, in addition to the concerns regarding data collection, the calibration process may be a problem as it is usually computationally expensive. Moreover, the available tools may be restricted concerning a large number of parameters found in these models. Therefore, this article presents a procedure to reduce the number of parameters to be estimated in discrete-choice models using many alternatives without affecting the model's overall quality. The strategy uses the Classification and Regression Tree (CART) algorithm and can only be applied to variables related to individuals. To test the feasibility of the procedure, data from a household survey in the city of Santa Maria (RS, Brazil), prepared for the Municipal Urban Mobility Plan, was used. The model, a Multinomial Logit type, was then applied to predict the choice of urban destinations, and its results were compared to those of the calibration without the proposed procedure. The results obtained showed that the strategy, applied to the study case under predefined criteria, did not cause any losses to the overall quality of the model (fit measures). It was concluded that the procedure proves to be viable for large choice sets as long as it can be complemented by a researcher's knowledge, or information from the literature, regarding the influence of variables on the forecast of the phenomenon under study.

大多数与离散选择相关的问题都涉及少量的选择。然而，在有许多替代方案的情况下，除了有关数据收集的问题外，校准过程可能是一个问题，因为它通常计算成本很高。此外，可用工具可能会受限于在这些模型中发现的大量参数。因此，本文提出了一种使用多种替代方法减少离散选择模型中要估计的参数数量而不影响模型整体质量的过程。该策略使用分类回归树（CART）算法，只能应用于与个体相关的变量。为了测试该程序的可行性，来自圣玛丽亚市（巴西 RS）的家庭调查数据，使用了为《市政城市交通计划》准备的文件。然后应用多项 Logit 类型的模型来预测城市目的地的选择，并将其结果与没有建议程序的校准结果进行比较。获得的结果表明，在预定义标准下应用于研究案例的策略不会对模型的整体质量（拟合度量）造成任何损失。得出的结论是，只要研究人员的知识或文献中的信息能够补充有关变量对所研究现象的预测的影响，该过程被证明对于大型选择集是可行的。并将其结果与没有建议程序的校准结果进行比较。获得的结果表明，在预定义标准下应用于研究案例的策略不会对模型的整体质量（拟合度量）造成任何损失。得出的结论是，该程序证明对大选择集是可行的，只要可以由研究人员的知识或文献中有关变量对所研究现象的预测的影响的知识补充即可。并将其结果与没有建议程序的校准结果进行比较。获得的结果表明，在预定义标准下应用于研究案例的策略不会对模型的整体质量（拟合度量）造成任何损失。得出的结论是，只要研究人员的知识或文献中的信息能够补充有关变量对所研究现象的预测的影响，该过程被证明对于大型选择集是可行的。