  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.
