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arXiv · 2504.01192

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

Ya-Ting Yang·Yunian Pan·Quanyan Zhu
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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.

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Ya-Ting Yang, Yunian Pan, Quanyan Zhu. 2025-04-01. Preference-Centric Route Recommendation: Equilibrium, Learning, and Provable Efficiency. https://arxiv.org/abs/2504.01192

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