Toward Controllability-Aware Performance Measures: A Case Study on Controllable Highway Congestion

Abstract

Advancements in emerging intelligent transportation systems (ITS) have shown immense benefit in reducing congestion, emissions, and accidents and enable lower-cost alternatives to highway lane expansion. However, there currently exists no standard metric by which agencies can assess the improvement potential from these interventions. As a result, they may fail to deploy infrastructure where it will be most promising or risk investing in infrastructure that does not meaningfully enhance performance. Our objective is to guide ITS deployment by developing a metric that quantifies the upper bound of congestion improvement from controlling speed limits on a highway. We propose controllable congestion, a metric that quantifies the maximum achievable reduction in system delay. To estimate controllable congestion, we develop a nonlinear optimization framework grounded in a reformulation of the METANET macroscopic traffic model and solved using model predictive control (MPC). Using both a synthetic scenario and highway data from the I-24 SMART Corridor in Tennessee, we find that controllable congestion is largely independent of total delay, revealing that for two days with identical travel times, controllable congestion can vary from 13\% to 80\%. We further show that the realizable share of this upper bound depends on the operational constraints imposed. On I-24, the minimum posted speed limit has little effect on controllable congestion, while frequency of speed limit update and maximum difference between adjacent gantries have large impacts. This framework allows highway operators to distinguish between congestion that is structurally unavoidable and congestion that is highly responsive to ITS, enabling more cost-effective deployment of control-based traffic infrastructure.