无序交通流以弱化或缺失的车道规则为特征，在车辆异质性显著且持续发生横向交互的条件下形成，从而挑战了传统基于车道的建模假设。本研究利用无人机采集的城市主干道高分辨率轨迹数据，对无序交通的宏观与微观特性开展实证分析。采用Edie框架的二维扩展方法量化集总交通变量，并构建二维基本图，结果表明一维公式不足以充分表征交通状态，并凸显横向再分布的持续作用。拥堵传播通过时空速度场直接估计，揭示了连贯的停走波现象，其动力学行为与常规车道化交通流相似，尽管车辆交互具有高度异质性。在微观层面，采用稳态跟驰-领航识别方法分析期望车头时距与最小横向间距、车辆尺寸分布及运动学特征，发现显著的车辆类别间异质性，可解释无序交通行为。本研究建立了连接车辆级交互与集总交通动力学的实证框架，并为无序混合交通系统的交通模型校准与验证提供了数据驱动基础。

Disordered traffic flow is characterized by weak or non-existent lane discipline in the presence of strong vehicle heterogeneity and continuous lateral interactions, challenging traditional lane-based modeling assumptions. This study presents an empirical study of macroscopic and microscopic aspects of disordered traffic using high-resolution UAV trajectory data collected on an urban arterial. A two-dimensional extension of Edie's framework is applied to quantify aggregate traffic variables and produce a two-dimensional fundamental diagram, revealing that traffic states cannot be adequately represented using one-dimensional formulations and highlighting the persistent role of lateral redistribution. The propagation of congestion is estimated directly from the spatiotemporal speed fields, demonstrating the emergence of coherent stop-and-go waves and showing a similar dynamics as conventional lane-based flow, in spite of the heterogeneous vehicle interactions. At the microscopic level, steady-state follower-leader identification is used to examine desired time gaps and minimum lateral spacing, vehicle dimension distributions, and kinematic characteristics, revealing pronounced inter-class heterogeneity that explains disordered traffic behavior. The study provides an empirical framework linking vehicle-level interactions and aggregate traffic dynamics and establishes a data-driven basis for the calibration and validation of traffic models for disordered mixed traffic systems.