Mesoscopic traffic flow models combines the merits of both macroscopic and microscopic models by capturing individual vehicle behavior in great detail and remaining the computational efficiency. At the time of this study, the mesoscopic model proposed by Eissfeldt (2004) is used in Simulation of Urban MObility (SUMO). The movement of vehicles is governed by dynamic headways between edges. However, the model does not fully comply with the principle of the Lighthill-Whitham-Richards (LWR) model. Several problems are identified, including the incomplete consideration of queue dynamics and the limited implementation of backward traveling spaces. Two case study scenarios demonstrate that the problems lead to unrealistic onset and recovery pattern of congestion. The magnitude of congestion is generally underestimated with this model. To address these drawbacks, a proper mesoscopic discrete-time implementation of link transmission model, which follows the LWR principle, is proposed. By explicitly incorporating backward traveling spaces to capture queue spillback phenomena, the proposed model provides a more precise representation of congestion dynamics. The link density outputs are consistent with the kinematic wave theory and the microscopic traffic simulation in SUMO, thus verifying its theoretical accuracy.

翻译：介观交通流模型结合了宏观和微观模型的优点，既能详细刻画个体车辆行为，又保持了计算效率。在本研究进行时，SUMO（城市交通仿真软件）采用了Eissfeldt（2004）提出的介观模型。车辆的运动由路段间的动态车头时距控制。然而，该模型并未完全遵循Lighthill-Whitham-Richards（LWR）模型原理。我们识别出若干问题，包括对排队动力学的考虑不完整以及后向传播空间实现有限。两个案例场景表明，这些问题会导致拥堵形成与消散模式不切实际。该模型通常会低估拥堵程度。针对这些缺陷，我们提出了一种遵循LWR原理的链路传输模型的合理介观离散时间实现。通过显式纳入后向传播空间以捕捉排队溢出现象，所提模型能更精确地描述拥堵动力学。其路段密度输出与运动波理论及SUMO微观交通仿真结果一致，从而验证了其理论准确性。