关键词: 交通工程, 交通流稳定性, 模型预测控制, 长隧道, 瓶颈区段, METANET宏观模型

Abstract: Under the conditions of synchronized flow when the traffic in long tunnels approaches saturation, local traffic bottleneck sections are easy to form due to the changes in driving environment and random traffic disturbances, resulting in congestion that propagates backward and turns into a wide moving jam. To address the potential random congestion problems in the long tunnels near saturation, a dynamic identification method for bottleneck sections and a stability-constrained control method based on real time detection data from characteristic cross-sections are proposed. According to the characteristics of the driving environment inside the long tunnels, the tunnel is divided into multiple sections, and the traffic flow and speed parameters of each section are detected in real time. By introducing the change in section traffic density, a macroscopic traffic flow model for dynamic traffic state prediction, bottleneck section identification, and stability control in the long tunnels is established. Near quasi-steady state points, the model is linearized, and a discrete state matrix is constructed. Based on the eigenvalue criteria, the bottleneck section is identified where the maximum eigenvalue modulus of traffic flow is closest to 1, and the corresponding critical density and critical speed parameters are extracted. A model predictive control method centered on critical density is constructed, and the traffic flow in the bottleneck section is actively regulated via upstream variable speed guidance. Taking the cross-section detection data from a specific long tunnel as an example, a dynamic identification method for section bottlenecks and a model predictive stability control strategy were developed. Simulation experiments were compared with no control and fixed-speed control strategies. The simulation results show that the model predictive stability control method can suppress flow and speed fluctuations in bottleneck sections, maintain the section density stably near the critical density within the efficient operating range, increase lane capacity utilization to 98.1%, and achieve effective intervention before traffic flow density reaches the critical state. This enables the traffic flow at bottleneck sections in long tunnel to shift from ' intervenes after the congestion' to 'predictive and proactive control.'

Key words: traffic engineering, traffic flow stability, model predictive control, long tunnels, bottleneck sections, METANET macroscopic model