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A household car ownership modeling is crucial in understanding the impact on an individual’s or a family’s travel behavior in traveling demand analysis. Trips or tours as a unit of analysis can be used in the modeling of car ownership demand for analyzing travel needs. Machine learning is widely used to describe a car owner’s decision since the machine learning model was specifically designed to give more accurate predictions through a variety of mechanisms. This research presents car ownership modeling using two types of machine learning models, including decision trees and neural networks. The impacts of socio-demographic attributes on household car ownership demand are discussed and compared against these two models after adding the main attributes of variables from tour-based models. Data was collected from 2,015 households surveyed in Khon Kaen Province, Thailand, conducted in 2015. The outcomes indicate that the machine learning model can be used to predict household car ownership. It also found that when using the default parameters across all datasets, whereas the neural networks provide a more accurate result than the decision tree algorithm. However, in cases where the household car ownership prediction from the dataset with add attributes of the key variable used in tour-based models. In that case, the neural networks algorithm would give a prediction accuracy that corresponded to results as found from the prediction using a dataset with only the household's socio-demographic attributes.

CAR OWNERSHIP DEMAND MODELING USING MACHINE LEARNING: DECISION TREES AND NEURAL NETWORKS