Generalized Multi‐Hop Downstream Traffic Pressure for Heterogeneous Perimeter Control

ABSTRACT

Perimeter control (PC) prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous PC allows all access points to a protected region to have identical permitted inflow. However, homogeneous PC performs poorly when the congestion in the protected region is heterogeneous (e.g., imbalanced demand) since the homogeneous PC does not consider specific traffic conditions around each perimeter intersection. When the protected region has spatially heterogeneous congestion, one needs to modulate the perimeter inflow rate to be higher near low‐density regions and vice versa for high‐density regions. A naïve approach is to leverage 1‐hop traffic pressure to measure the traffic conditions around perimeter intersections, but such metrics are too spatially myopic for good PC. To address this issue, we formulate multi‐hop downstream pressure grounded in Markov chain theory, which ‘looks deeper’ into the protected region beyond perimeter intersections. In addition, we formulate a two‐stage hierarchical control scheme that can leverage this novel multi‐hop pressure to redistribute the total permitted inflow provided by a pre‐trained deep reinforcement learning homogeneous control policy. Experimental results show that our heterogeneous PC approach leveraging multi‐hop pressure significantly outperforms homogeneous PC in scenarios where the origin–destination flows are highly imbalanced with high spatial heterogeneity. Moreover, a sensitivity analysis shows that our approach is robust to turning ratio uncertainty.