Análise de desempenho de algoritmos de aprendizagem de máquinas para análise desagregada de viagens intermunicipais
DOI:
https://doi.org/10.14295/transportes.v26i3.1614Keywords:
Trip Distribution, Genetic Algorithms, Decision Tree, Gravitational Model.Abstract
This paper proposes a disaggregated analysis of intercity destination choices, through the application of Machine Learning (ML) algorithms (Classification And Regression Tree - CART and Genetic Algorithms - GA). An Origin-Destination Survey was carried out by the Center of Transportation and Environmental Studies (UFBA) in 2012/2013 in eleven municipalities in the state of Bahia, Brazil. It was carried out a calibration of a Multinomial Logit Model with GA algorithm, bringing the advantage of association of the destination choices to values of estimated coefficients of the random utility functions, without the problems related to the calibration of the traditional logit models, such as Irrelevant Alternatives (IIA) assumption. The performance of each ML algorithm was compared to a traditional approach (Gravitational Model). The results showed that the ML algorithms presented better predictions for destination choices, and GA presented advantages in obtaining the estimated parameters related to the covariates. The main conclusion is that such algorithms can be applied in trip distribution step, incorporating the effect of the disaggregated variables, without rigorous assumptions of the traditional disaggregated models.Downloads
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