Today mobile users learn and share their traffic observations via
crowdsourcing platforms (e.g., Waze). Yet such platforms simply cater to
selfish users' myopic interests to recommend the shortest path, and do not
encourage enough users to travel and learn other paths for future others. Prior
studies focus on one-shot congestion games without considering users'
information learning, while our work studies how users learn and alter traffic
conditions on stochastic paths in a human-in-the-loop manner. Our analysis
shows that the myopic routing policy leads to severe under-exploration of
stochastic paths. This results in a price of anarchy (PoA) greater than $2$, as
compared to the socially optimal policy in minimizing the long-term social
cost. Besides, the myopic policy fails to ensure the correct learning
convergence about users' traffic hazard beliefs. To address this, we focus on
informational (non-monetary) mechanisms as they are easier to implement than
pricing. 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 (non-monetary) mechanism. Besides the parallel network, we
further extend our analysis and CHAR to more general linear path graphs with
multiple intermediate nodes, and we prove that the PoA results remain
unchanged. Additionally, we carry out experiments with real-world datasets to
further extend our routing graphs and verify the close-to-optimal performance
of our CHAR.

移动用户通过众包平台学习和分享交通观察，然而这类平台仅迎合自私用户的短视利益以推荐最短路径，未鼓励足够多的用户为他人出行和学习其他路径。我们的研究通过人机交互式地分析用户如何学习和改变随机路径上的交通情况，证明了短视路由策略导致随机路径的严重未探索，造成高于 2 的社会成本。我们提出了一种信息隐藏和概率推荐机制，名为 CHAR，成功确保了低于 5/4 的 PoA，并证明了在更一般的线性路径图中 PoA 结果保持不变。我们的实验验证了 CHAR 在真实数据集上接近最优的性能。