An Agent-Based Framework for Performance Evaluation and Tradeoff Analysis of Highway Work Zone Policies

Amidst global efforts to mitigate climate change, managing the disruptive impact of highway work zones requires intelligent solutions. Traditional assessment methods often lack the capability to model the complex, dynamic interactions between traffic flow and temporary control policies, while struggling to balance computational efficiency with system-level dynamic assessment in large-scale networks. To address this gap, this paper designs, implements, and validates a Multiagent System based decision support framework. In this framework, individual vehicles and infrastructure elements are modeled as autonomous agents that interact to simulate emergent, system-level traffic dynamics. By modeling the system from the bottom up, this mesoscopic simulation approach enables a system-level quantification of the relative effectiveness of various control policies in reducing CO2 emissions across different work zone scenarios. Using the Hangzhou Ring Expressway as a case study, the study demonstrates the utility of the framework as a virtual testbed. The results of the framework not only reveal the non-linear sensitivity of emissions to policy parameters but, more critically, the multi-objective tradeoff analysis uncovers non-intuitive, Pareto-optimal strategies. For instance, the analysis identifies scenarios where a well-configured two-lane closure can outperform a suboptimal one-lane closure in both traffic efficiency and environmental impact. The findings confirm that the proposed framework is a powerful and extensible tool for transportation authorities to design, test, and deploy more efficient and sustainable work zone management strategies in the era of Intelligent Transportation Systems.