Abstract
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.
Highlights
In transportation, route choice modeling should deal with the problems of facing many attributes, how decision makers choose which way to go from origins to destinations [1]–[3]
From this extensive overview of car route choice modeling [4], we find it always thinks that decision makers use utility-based rules to choose, which may be unrealistic in a specific situation when people are more likely to compare attributes of different alternatives [5]–[7]
In this paper, the main method is to use the characteristics of regret weight in GRRM to get the hybrid RUM-RRM model, which generates a bridge connecting the canonical linear-additive random utility maximization model and the random regret minimization model
Summary
Route choice modeling should deal with the problems of facing many attributes, how decision makers choose which way to go from origins to destinations [1]–[3]. Since the introduction of random utility maximization (RUM [8]), the interest in discrete choice models that provide behavioral alternatives to it is increasing because it is considered as appropriate on an attribute decision rule. This utilitarian category of the discrete choice model has been most used and earned the main developer a Nobel Prize. The associate editor coordinating the review of this manuscript and approving it for publication was Baozhen Yao. in recent years, Chorus et al [9] put forward a new decision rules called random regret minimization (RRM), which is a counterpart of the RUM model.
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