A coordinated ramp metering framework based on heterogeneous causal inference

Abstract

AbstractThe coordinated ramp metering strategy aims to enhance the traffic flow on freeways by integrating the effects of multiple on‐ramps. The success of this strategy depends heavily on the metering effects of each on‐ramp and the cooperation pattern. To ensure the effectiveness of coordination control, it is essential to design the coordinated strategy based on the causal effect of each ramp flow on the freeway bottleneck. Therefore, this study investigated the heterogeneous causal effects of ramp flow on freeway bottleneck and proposed a coordinated ramp metering framework based on these causal effects. Causal inference was conducted using loop detector data. The estimated heterogeneous causal effect, which varies across traffic conditions, measures the change of bottleneck occupancy resulting from a unit increase in each on‐ramp outflow. This information quantifies the importance of each ramp in real time and can be used to update the ramp weight and metering rate of each ramp controller. To comprehensively test the proposed coordination framework, two instances with different road networks were selected. In the first instance, limiting the on‐ramp flow downstream of the bottleneck was found to be helpful in mitigating the bottleneck. Compared with isolated ramp metering, the proposed algorithm reduced travel delay by 26.0% by considering the heterogeneous causal effects of on‐ramp flow both upstream and downstream of the freeway bottleneck. In the next instance, the proposed algorithm reduced travel delay by 5.8%, compared to a well‐performed feedback‐based coordinated ramp metering algorithm. The results of this study suggest that the use of heterogeneous causal inference is effective in improving the performance of coordinated ramp metering algorithms.