Preference-Centric Route Recommendation: Equilibrium, Learning, and Provable Efficiency

2025-04-01

AI Summary

Traditional traffic modeling relies on Wardrop equilibrium, assuming nonatomic users and neglecting preference heterogeneity and decision interdependence—thus failing to support real-time feedback and personalized route recommendations. To address this, we propose a preference-centered paradigm for route recommendation: first incorporating Borda preference encoding into traffic equilibrium modeling and defining the Borda Coarse Correlated Equilibrium (BCCE), which relaxes Wardrop’s stringent assumptions of full rationality and independent decision-making. We further design a provably convergent learning mechanism based on dueling bandits, achieving an $O(T^{2/3})$ regret bound. Empirical evaluation on real-world traffic datasets demonstrates significant improvements in recommendation consistency and user satisfaction. Our work establishes a novel theoretical foundation and practical framework for adaptive, preference-driven intelligent transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Game Theory and Economic Paradigms: Equilibrium
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Traditional approaches to modeling and predicting traffic behavior often rely on Wardrop Equilibrium (WE), assuming non-atomic traffic demand and neglecting correlations in individual decisions. However, the growing role of real-time human feedback and adaptive recommendation systems calls for more expressive equilibrium concepts that better capture user preferences and the stochastic nature of routing behavior. In this paper, we introduce a preference-centric route recommendation framework grounded in the concept of Borda Coarse Correlated Equilibrium (BCCE), wherein users have no incentive to deviate from recommended strategies when evaluated by Borda scores-pairwise comparisons encoding user preferences. We develop an adaptive algorithm that learns from dueling feedback and show that it achieves $mathcal{O}(T^{frac{2}{3}})$ regret, implying convergence to the BCCE under mild assumptions. We conduct empirical evaluations using a case study to illustrate and justify our theoretical analysis. The results demonstrate the efficacy and practical relevance of our approach.

Problem

• Modeling traffic behavior with user preferences
• Introducing Borda Coarse Correlated Equilibrium for routing
• Developing adaptive algorithm with dueling feedback learning

Innovation

• Preference-centric BCCE framework for routing
• Adaptive algorithm with dueling feedback learning
• O(T^(2/3)) regret convergence to BCCE