本文提供了一组公开可用的微观车辆轨迹（MVT）数据集，该数据集通过无人机（UAV）在异质性、区域化的城市交通条件下采集。传统路边视频采集方法在高密度混合交通场景中常因遮挡、视场角受限及车辆运动不规则等问题而失效。基于无人机的录制方式提供了俯视视角，可有效缓解上述问题，并更充分地捕捉空间与时间动态特征。本文所述数据集利用Data from Sky（DFS）平台提取，并在前期研究中通过人工计数、区间平均速度及浮动车轨迹进行了验证。每个数据集包含以每秒30帧分辨率记录的时间戳车辆位置、速度、纵向与横向加速度以及车辆分类信息。数据采集于印度国家首都辖区六个路段中部位置，覆盖多样化的交通组成与密度水平。探索性分析揭示了若干关键行为模式，包括车道保持偏好、速度分布特征以及异质性与区域化交通环境中典型的横向机动行为。本数据集旨在为全球研究界提供资源，以支持仿真建模、安全评估及区域化交通条件下的行为研究。通过公开发布这些实证数据集，本工作为研究人员提供了独特机会，以开发、测试并验证更能准确表征复杂城市交通环境的模型。

This paper offers openly available microscopic vehicle trajectory (MVT) datasets collected using unmanned aerial vehicles (UAVs) in heterogeneous, area-based urban traffic conditions. Traditional roadside video collection often fails in dense mixed traffic due to occlusion, limited viewing angles, and irregular vehicle movements. UAV-based recording provides a top-down perspective that reduces these issues and captures rich spatial and temporal dynamics. The datasets described here were extracted using the Data from Sky (DFS) platform and validated against manual counts, space mean speeds, and probe trajectories in earlier work. Each dataset contains time-stamped vehicle positions, speeds, longitudinal and lateral accelerations, and vehicle classifications at a resolution of 30 frames per second. Data were collected at six mid-block locations in the national capital region of India, covering diverse traffic compositions and density levels. Exploratory analyses highlight key behavioural patterns, including lane-keeping preferences, speed distributions, and lateral manoeuvres typical of heterogeneous and area-based traffic settings. These datasets are intended as a resource for the global research community to support simulation modelling, safety assessment, and behavioural studies under area-based traffic conditions. By making these empirical datasets openly available, this work offers researchers a unique opportunity to develop, test, and validate models that more accurately represent complex urban traffic environments.