Error distributions assumptions in random regret choice models: towards error Frechit specifications

Abstract

Recently introduced regret-based choice models in transportation research have mainly adopted the assumption of identically and independently distributed unobserved regrets. The central argument underlying this paper is that this assumption is difficult to defend considering the fundamental nature of the concept of regret, which states that regret is generated through the comparison of choice alternatives on an attribute-by-attribute basis. To support this stance, we identify and diagnose specification errors in classic regret-based choice models, and provide an alternative specification. Results show that relaxing the assumption of identically and independently distributed error terms and applying the proposed error components Frechit structure leads to a substantial improvement in model performance of the regret-based models for the data used.