  Routing strategies under the aegis of dynamic traffic assignment have been
proposed in the literature to optimize system performance. However, challenges
have persisted in their deployment ability and effectiveness due to inherent
strong assumptions on traveler behavior and availability of network-level
real-time traffic information, and the high computational burden associated
with computing network-wide flows in real-time. This study proposes an
incentive-based decentralized routing strategy to nudge the network performance
closer to the system optimum for the context where all vehicles are connected
and autonomous vehicles (CAVs). The strategy consists of three stages. The
first stage incorporates a decentralized local route switching dynamical system
to approximate the system optimal route flow in a local area based on vehicles'
knowledge of local traffic information. The second stage optimizes the route
for each CAV by considering individual heterogeneity in traveler preferences
(e.g., the value of time) to maximize the utilities of all travelers in the
local area. Constraints are also incorporated to ensure that these routes can
achieve the approximated local system optimal flow of the first stage. The
third stage leverages an expected envy-free incentive mechanism to ensure that
travelers in the local area can accept the optimal routes determined in the
second stage. The study analytically discusses the convergence of the local
route switching dynamical system. We also show that the proposed incentive
mechanism is expected individual rational and budget-balanced, which ensures
that travelers are willing to participate and guarantee the balance between
payments and compensations, respectively. Further, the conditions for the
expected incentive compatibility of the incentive mechanism are analyzed and
proved, ensuring behavioral honesty in disclosing information.
