Today mobile users learn and share their traffic observations via crowdsourcing platforms (e.g., Google Maps and Waze). Yet such platforms myopically recommend the currently shortest path to users, and selfish users are unwilling to travel to longer paths of varying traffic conditions to explore. Prior studies focus on one-shot congestion games without information learning, while our work studies how users learn and alter traffic conditions on stochastic paths in a distributed manner. Our analysis shows that, as compared to the social optimum in minimizing the long-term social cost via optimal exploration-exploitation tradeoff, the myopic routing policy leads to severe under-exploration of stochastic paths with the price of anarchy (PoA) greater than \(2\). Besides, it fails to ensure the correct learning convergence about users' traffic hazard beliefs. To mitigate the efficiency loss, we first show that existing information-hiding mechanisms and deterministic path-recommendation mechanisms in Bayesian persuasion literature do not work with even \(\text{PoA}=\infty\). Accordingly, we propose a new combined hiding and probabilistic recommendation (CHAR) mechanism to hide all information from a selected user group and provide state-dependent probabilistic recommendations to the other user group. Our CHAR successfully ensures PoA less than \(\frac{5}{4}\), which cannot be further reduced by any other informational mechanism. Additionally, we experiment with real-world data to verify our CHAR's good average performance.

翻译：如今，移动用户通过众包平台（如谷歌地图和Waze）学习并分享其交通观测信息。然而，这类平台短视地向用户推荐当前最短路径，而自私的用户不愿探索具有不同交通状况的较长路径。以往研究主要关注无信息学习的一次性拥塞博弈，而本研究探讨用户如何在随机路径上以分布式方式学习并改变交通状况。分析表明，与通过最优探索-利用权衡实现长期社会成本最小化的社会最优方案相比，短视路由策略导致对随机路径的严重探索不足，其无政府状态价格（PoA）大于\(2\)。此外，该策略无法确保用户交通风险信念的正确学习收敛。为缓解效率损失，我们首先证明贝叶斯说服文献中现有的信息隐藏机制和确定性路径推荐机制即使产生\(\text{PoA}=\infty\)也无法奏效。据此，我们提出一种新型组合隐藏与概率推荐（CHAR）机制：向选定用户组隐藏全部信息，同时为另一用户组提供状态相关的概率推荐。我们的CHAR机制成功将PoA控制在\(\frac{5}{4}\)以下，这一数值无法被任何其他信息机制进一步降低。此外，我们通过真实世界数据实验验证了CHAR机制良好的平均性能。