对微型移动车辆（MMV）动力学进行建模在训练自动驾驶系统和构建城市交通仿真中变得日益重要。然而，主流工具依赖于运动学自行车模型（KBM）的变体或特定模式的物理模型，这些方法忽略了轮胎滑移、载荷转移以及骑行者/车辆倾斜等关键动态特性。据我们所知，目前尚无统一的基于物理的模型能够覆盖常见微型移动车辆及其轮式布局的全范围动态行为。本文提出“通用微型移动模型”（GM3），该模型基于轮胎刷子表示法，在轮胎层面进行建模，支持任意轮式配置，包括单轨/双轨及多轮平台。我们设计了一个交互式的、与模型无关的仿真框架，将车辆/布局定义与动力学解耦，以便将GM3与KBM及其他模型进行对比；该框架包含固定步长的RK4积分器、人机协同与脚本控制、实时轨迹追踪及日志记录功能，便于分析。此外，我们在斯坦福无人机数据集的deathCircle（环形交叉口）场景中，针对骑行者、滑手和手推车三类对象对GM3进行了实证验证。

Modeling the dynamics of micro-mobility vehicles (MMV) is becoming increasingly important for training autonomous vehicle systems and building urban traffic simulations. However, mainstream tools rely on variants of the Kinematic Bicycle Model (KBM) or mode-specific physics that miss tire slip, load transfer, and rider/vehicle lean. To our knowledge, no unified, physics-based model captures these dynamics across the full range of common MMVs and wheel layouts. We propose the "Generalized Micro-mobility Model" (GM3), a tire-level formulation based on the tire brush representation that supports arbitrary wheel configurations, including single/double track and multi-wheel platforms. We introduce an interactive model-agnostic simulation framework that decouples vehicle/layout specification from dynamics to compare the GM3 with the KBM and other models, consisting of fixed step RK4 integration, human-in-the-loop and scripted control, real-time trajectory traces and logging for analysis. We also empirically validate the GM3 on the Stanford Drone Dataset's deathCircle (roundabout) scene for biker, skater, and cart classes.