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

Abstract

: 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 .