Distributed Urban Freeway Traffic Optimization Considering Congestion Propagation

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called
potential-homogeneous-area
(PHA). Utilizing the historical traffic density data, we first quantify the effect of congestion propagation and identify PHA by applying the proposed spatiotemporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with the cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a double-consensus-based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of a convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy.