Leveraging Microscopic Simulation to Enhance the Design of Highway Active Traffic Management Strategies

Advancements in Autonomous Vehicles (AVs) technology necessitate Active Traffic Management (ATM) strategies that incorporate fine-grained microscopic traffic features, accounting for AVs' distinct driving behaviors, decision-making patterns, and the specific road geometry design required for increasing AV penetration. While microscopic traffic simulation models are well-suited for capturing these microscopic features, their high computational demands and non-analytic nature have confined their use to ATM strategies optimization. To bridge this gap, this study employs a Simulation-Based Optimization (SBO) paradigm to integrate microscopic models into the design of ATM strategies for highways. We introduce an SBO framework that combines a Cell Transmission Model (CTM)-based meta model with the Adaptive Hyperbox Algorithm (AHA), where the CTM-based meta model approximates microscopic traffic conditions, and the AHA explores high-dimensional, discrete decision spaces to determine optimal ATM strategies. Our experiments demonstrate that the proposed SBO frame-work substantially outperforms other general-purpose SBO methods. This work advances ATM optimization by facilitating the integration of microscopic simulation models into practical optimization frameworks, ensuring the consideration of evolving traffic characteristics with the increasing presence of AVs.