  Effective traffic optimization strategies can improve the performance of
transportation networks significantly. Most exiting works develop traffic
optimization strategies depending on the local traffic states of congested road
segments, where the congestion propagation is neglected. This paper proposes a
novel distributed traffic optimization method for urban freeways considering
the potential congested road segments, which are called
potential-homogeneous-area. The proposed approach is based on the intuition
that the evolution of congestion may affect the neighbor segments due to the
mobility of traffic flow. We identify potential-homogeneous-area by applying
our proposed temporal-spatial lambda-connectedness method using historical
traffic data. Further, global dynamic capacity constraint of this area is
integrated with cell transmission model (CTM) in the traffic optimization
problem. To reduce computational complexity and improve scalability, we propose
a fully distributed algorithm to solve the problem, which is based on the
partial augmented Lagrangian and dual-consensus alternating direction method of
multipliers (ADMM). By this means, distributed coordination of ramp metering
and variable speed limit control is achieved. We prove that the proposed
algorithm converges to the optimal solution so long as the traffic optimization
objective is convex. The performance of the proposed method is evaluated by
macroscopic simulation using real data of Shanghai, China.
