I-METANET: Macroscopic Freeway Model for Real-Time Incident Impact Estimation and Traffic Management

Macroscopic traffic flow models, notably METANET, have been instrumental in the real-time estimation and optimization of control strategies for recurrent traffic congestions owing to their computational efficiency and robust performance. Their embedded formulations, however, are not best tailored to capture the traffic dynamics during roadway lane blockage owing to incidents where rapid and surging queue waves often propagate to upstream segments at time-varying but much faster speeds than under recurrent traffic congestion. Therefore, this study introduces I-METANET for use in incident traffic modeling and management, grounded mainly in METANET’s core mechanism but with the following enhancements: (1) reflecting incident-induced merging behaviors and their significant but attenuate impacts on the speed propagation over upstream segments; (2) incorporating the concurrent impacts of the approaching upstream traffic flows and the downstream incident-incurred queue waves on a subject segment’s evolving speed; and (3) integrating the compound effects of ramp-flows’ weaving maneuvers and the presence of incident queues on an interchange segment’s traffic conditions. The proposed I-METANET, calibrated and evaluated by the field data from Interstate 4 (I-4) in Florida, has demonstrated its expected improvements over METANET under incident scenarios, based on the produced time-varying speeds and flow rates over each roadway segment during incident clearance periods. An extensive sensitivity analysis with respect to key parameters in I-METANET has also been conducted, and evaluation results have confirmed the robustness of I-METANET for simulating freeway traffic dynamics during incident-incurred traffic scenarios.