This paper proposes an agent model with adaptive
weight-based multi-objective algorithm to manage road-network
congestion problem. Our focus is to construct a quantitative
index series to describe the road-network congestion distribution,
and use such indexes as weights in the multi-objective algorithm
to shunt vehicles on those congested links. First, a multi-agent
system is built, where each agent stands for a vehicle that adapts
its route to real-time road-network congestion status by a twoobjective
optimization process: the shortest path and the minimal
congested degree of the target link. The agent-based approach
captures the nonlinear feedback between vehicle routing
behaviors and road-network congestion states. Next, a series of
quantitative indexes is constructed to describe the congested
degree of nodes, and such indexes are used as weights in the twoobjective
functions which are employed by the agents for routing
decisions and congestion avoidance. In this way, our proposed
agent model with adaptive weight-based multi-objective
algorithm could achieve congestion distribution evaluation and
congestion management at the same time. The simulation results
show that our proposed approach has successfully improved
those seriously congested links of road-network. Finally, we
execute our model on a real traffic map, and the results show that
our proposed model reduce the congestion degree of roadnetwork,
thus have its significant potentials for the actual traffic
congestion evaluation and management.
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