An artificial neural network based approach to investigate travellers' decision rules

This study develops a novel Artificial Neural Network (ANN) based approach to investigate decision rule heterogeneity amongst travellers. This complements earlier work on decision rule heterogeneity based on Latent Class discrete choice models. We train our ANN to recognise the choice patterns of four distinct decision rules: Random Utility Maximisation, Random Regret Minimisation, Lexicographic, and Random. Next, we apply our trained ANN to classify the respondents from a recent Value-of-Time Stated Choice experiment in terms of their most likely employed decision rule. We cross-validate our findings by comparing our results with those from: (1) single class discrete choice models estimated on subsets of the data, and (2) latent class discrete choice models. The cross-validations provide strong support for the notion that ANNs can be used to identify underlying decision rules in choice data. As such, we believe that ANNs provide a valuable addition to the toolbox of analysts who wish to investigate decision rule heterogeneity. The substantive contribution of this study is that we provide strong empirical evidence for the presence of decision rule heterogeneity amongst travellers.