Optimal Ramp Metering Control for Weaving Segments Considering Dynamic Weaving Capacity Estimation

Abstract

On freeway corridors, traffic flow is limited by active bottlenecks. Weaving maneuvers (i.e., intensive lane changes) are a major cause of bottlenecks during high-demand periods. To relieve bottleneck severity, ramp metering (RM) is implemented as an active traffic control method. Ample research has been devoted to developing RM control algorithms and to exploring weaving impacts; however, RM control that is considerate of dynamic weaving impact and its evaluation has received little attention in the published literature. This paper aims to bridge that gap by proposing a proactive control algorithm that uses as inputs dynamic weaving capacity (as opposed to traditional fixed capacity values) for RM control at weaving segments. The control goals are to reduce networkwide travel time and improve traffic flow. Capacity and capacity drop were estimated through fundamental diagrams (FDs). Then, capacity drop sensitivities to on-ramp and mainline demand were analyzed within a field-data-based microsimulation model. The findings were applied to dynamically estimate weaving capacity within a macroscopic traffic flow model. The proposed traffic flow model conducted estimation in a model predictive control (MPC) framework. The RM rates were optimized by sequential quadratic programming (SQP). The proposed RM algorithm was evaluated in macrosimulation and compared with a no-control scenario as well as with a control scenario that used static (as opposed to dynamic) weaving capacity. This analysis contributes to efficient and effective field applications and freeway operational improvements.