Exploring random taste heterogeneity in choice modelling using mixture density network

Capturing heterogeneity in subjects’ decision making process, as accurate as possible, plays an essential role in choice modeling research. In this paper, we investigate the random taste heterogeneity in travel behavior modeling which is an integral part of decision-making process. In contrast to previous works, we use the Mixture Density Network (MDN) which is built from Neural Network and mixture Gaussian model to identify the latent heterogeneity. We assume that the taste variation of individuals follows a series of distribution with certain mean and standard deviation which are dependent on individual social demographic characteristics. We integrated this machine learning method into the discrete choice model and jointly estimated the parameters. Using the stated preference data of Swissmetro, we applied our proposed model and discovered random taste variations which are highly interpretable. We also compared the model with traditional mixed logit model and found the superiority of the proposed model.