On the similarities between random regret minimization and mother logit: The case of recursive route choice models

• Main Authors: MAI, Tien, BASTIN, Fabian, FREJINGER, Emma
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Route choice modeling; Recursive logit; Random regret minimization; Mother logit; Maximum likelihood estimation; Cross-validation; Artificial Intelligence and Robotics; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5286; https://ink.library.smu.edu.sg/context/sis_research/article/6289/viewcontent/1_s2.0_S1755534515300385_main.pdf
• Institution: Singapore Management University

This paper focuses on the comparison of the random regret minimization (RRM) and mother logit models for analyzing the choice between alternatives having deterministic attributes. The mother logit model allows utilities of a given alternative to depend on attributes of other alternatives. It was designed to relax the independence from irrelevant alternatives (IIA) property while keeping the random terms independently and identically distributed extreme value distributed (McFadden et al., 1978).We adapt and extend the RRM model proposed by Chorus (2014) to the case of recursive logit (RL) route choice models (Fosgerau et al., 2013). We argue that these RRM models can be cast as mother logit models and we define such models that are equivalent to the RRM ones considered in this paper. The results show that one of the RRM models and its mother logit equivalent has the best out-of-sample fit indicating that utility functions based on attribute differences best explains the choices in our application.