Enhanced navigational insights and their impact on driver route choice: A hybrid utility-regret analysis with heterogeneity

Abstract

Active Traffic Management systems provide traffic information to drivers, guiding their route selections to help ease congestion. Given the high reliance of most drivers on navigation, the effectiveness of how this information is displayed is crucial. This study considers the timing of information dissemination and display format attributes and explores the interaction effects between individual characteristics and travel traits. A survey involving 831 participants, consisting of Revealed Preference and Stated Preference data, is conducted in Nanjing, China. Using a mixed latent class model, the population is categorized into different classes based on their decision-making rules, while incorporating unobserved heterogeneity within segment-level models. We consider both Random Utility and Random Regret theories. Our study reveals that there are significant differences in route choice behavior influenced by demographic factors, with younger, higher-income, and frequent drivers favoring utility-maximizing decisions, and older, lower-income individuals opting for choices that minimize regret. Variations in adherence are observed when information is presented before, during, or towards the end of the journey. Excessively complex information may increase decision-making pressure on drivers. The parameter estimations are also conducted trade-off analysis across various exogenous variables. The findings inform the improvement of navigation applications, personalized route recommendations, and congestion pricing.