  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.
