Comparing decision tree algorithms to estimate intercity trip distribution. (April 2017) - Pitombo, Cira Souza; de Souza, Andreza Dornelas; Lindner, Anabele
Highlights: An alternative method for estimating travelers' destination choice is proposed. Traditional methods usually do not take into account disaggregate factors. Features of the origin city along with individual factors are here considered. The use of decision tree algorithms (CHAID and CART) is presented. Both decision tree algorithms outperformed the gravity models. Abstract: Traditional trip distribution models usually ignore the fact that destination choices are made individually in addition to aggregated factors, such as employment and average travel costs. This paper proposes a disaggregated analysis of destination choices for intercity trips, taking into account aggregated characteristics of the origin city, an impedance measurement and disaggregated variables related to the individual, by applying nonparametric Decision Tree (DT) algorithms. Furthermore, each algorithm's performance is compared with traditional gravity models estimated from a stepwise procedure (1) and a doubly constrained procedure (2). The analysis was based on a dataset from the 2012 Origin-Destination Survey carried out in Bahia, Brazil. The final selected variables to describe the destination choices were population of the origin city, GDP of the origin city and travel distances at an aggregated level, as well as the variables: age, occupation, level of education, income (monthly), number of cars per household and gender at a disaggregated one. The comparison of the DT models with gravity models demonstrated that the former models provided better accuracy when predicting the destination choices (trip length distribution, goodness-of-fit measures and qualitative perspective). The main conclusion is that Decision Tree algorithms can be applied to distribution modeling to improve traditional trip distribution approaches by assimilating the effect of disaggregated variables.