LAUSR

There are two ways to predict and evaluate decision-makers’ route choice behavior: random utility maximization (RUM) and random regret minimization (RRM). In this paper, the main purpose is to use the characteristics of regret weight in GRRM to get a hybrid RUM-RRM model. To illustrate the asymmetry of RRM model, this paper uses a route choice case to interpret three main properties of RRM-based model: independence of irrelevant alternatives, semi-compensatory and compromise effect. Then the same scenario is used to interpret why and how the hybrid model can be obtained from the regret weight. What’s more, the current empirical studies only used a stated preference survey to test and estimate the model. So GPS-based big data in Guangzhou is used to test the aforementioned models, which can get rid of the weakness of using the stated survey data. The result shows that although the RUM model outperforms most of the RRM models, using regret weight to get the hybrid model, it can also find better model fitness and coefficients consistent with our understanding of attributes. Finally, a value is used to help traffic designers choose a better position of U-turn on the road.