Traffic microsimulation is a crucial tool that uses microscopic traffic models, such as car-following and lane-change models, to simulate the trajectories of individual agents. This digital platform allows for the assessment of the impact of emerging technologies on transportation system performance. While these microscopic models are based on mathematical structures, their parameters must be fitted to real-world data through a process called model calibration. Despite extensive studies on calibration, the focus has predominantly been on fitting microscopic data, such as trajectories, rather than evaluating how well the models reproduce macroscopic traffic patterns, such as congestion, bottlenecks, and traffic waves. In this work, we address this gap by calibrating microscopic traffic flow models using macroscopic (aggregated) data, which is more readily accessible. We designed a SUMO-in-the-loop calibration framework with the goal of replicating observed macroscopic traffic features. To assess calibration accuracy, we developed a set of performance measures that evaluate the models' ability to replicate traffic states across the entire spatiotemporal domain and other qualitative characteristics of traffic flow. The calibration method was applied to both a synthetic scenario and a real-world scenario on a segment of Interstate 24, to demonstrate its effectiveness in reproducing observed traffic patterns.

翻译：交通微观仿真是一种关键工具，它利用微观交通模型（如跟驰模型和换道模型）来模拟个体智能体的运动轨迹。该数字平台可用于评估新兴技术对交通系统性能的影响。虽然这些微观模型基于数学结构，但其参数必须通过模型标定过程与真实世界数据进行拟合。尽管已有大量关于标定的研究，但焦点主要集中于拟合微观数据（如轨迹），而非评估模型再现宏观交通模式（如拥堵、瓶颈和交通波）的能力。本研究通过使用更易获取的宏观（聚合）数据标定微观交通流模型，以弥补这一空白。我们设计了一个SUMO在环标定框架，其目标是复现观测到的宏观交通特征。为评估标定精度，我们开发了一套性能指标，用于评估模型在整个时空域内复现交通状态的能力以及交通流的其他定性特征。该标定方法被应用于合成场景和州际24号公路某路段的真实场景，以证明其在复现观测交通模式方面的有效性。