While macroscopic traffic flow models consider traffic as a fluid, microscopic traffic flow models describe the dynamics of individual vehicles. Capturing macroscopic traffic phenomena remains a challenge for microscopic models, especially in complex road sections such as on-ramps. In this paper, we propose a microscopic model for on-ramps derived from a macroscopic network flow model calibrated to real traffic data. The microscopic flow-based model requires additional assumptions regarding the acceleration and the merging behavior on the on-ramp to maintain consistency with the mean speeds, traffic flow and density predicted by the macroscopic model. To evaluate the model's performance, we conduct traffic simulations assessing speeds, accelerations, lane change positions, and risky behavior. Our results show that, although the proposed model may not fully capture all traffic phenomena of on-ramps accurately, it performs better than the Intelligent Driver Model (IDM) in most evaluated aspects. While the IDM is almost completely free of conflicts, the proposed model evokes a realistic amount and severity of conflicts and can therefore be used for safety analysis.

翻译：宏观交通流模型将交通视为流体，而微观交通流模型则描述个体车辆的动态行为。捕捉宏观交通现象对微观模型而言仍是一项挑战，尤其是在匝道等复杂路段。本文提出了一种基于真实交通数据标定的宏观网络流模型推导而来的微观匝道模型。该基于微观流量的模型需要额外假设匝道上的加速度和合流行为，以保持与宏观模型预测的平均速度、交通流量和密度的一致性。为评估模型性能，我们进行了交通仿真，对速度、加速度、换道位置及危险行为进行评估。结果表明，尽管所提模型可能无法完全精确捕捉匝道的所有交通现象，但在大多数评估指标上优于智能驾驶员模型（IDM）。IDM几乎完全避免冲突，而所提模型则能产生现实程度和严重性的冲突，因此可用于安全分析。