A Linear Model Predictive Control‐Based Ramp Metering Strategy for Traffic Flow Analysis in Continuous Multi‐Bottleneck Highway Segments

Multi‐bottleneck highway segments present significant challenges in traffic management due to the propagation of congestion waves between closely spaced bottlenecks. This study proposes a linear model predictive control (MPC)‐based ramp metering strategy designed specifically for continuous multi‐bottleneck corridors. The approach incorporates a macroscopic linear traffic flow model that discretizes the roadway into interconnected cells, allowing real‐time prediction of traffic states on both mainline and ramp segments. The control problem is formulated as a constrained quadratic programming task aimed at minimizing vehicle accumulation and enhancing overall throughput. A key innovation of this strategy lies in its predictive, multi‐input, multi‐output architecture, which enables proactive, corridor‐wide coordination of ramp inflows based on anticipated traffic conditions and inter‐bottleneck interactions. To ensure real‐time computational feasibility, a linearized cell transmission model is used and efficiently solved via the CPLEX optimizer. Simulation experiments demonstrate the effectiveness of the proposed method, with reductions in total travel time of 23.7%, 11.6% and 2.4% and corresponding reductions in total delay of 74.6%, 54.1% and 8.9%, compared to no‐control, PI‐ALINEA and regional MPC ramp metering strategies, respectively. These results highlight the strategy's superiority in improving system‐wide traffic efficiency under complex congestion scenarios.