智能交通系统（ITS）日益依赖来自路侧摄像头、无人机影像、激光雷达（LiDAR）及车载传感器等异构数据源的数据，然而这些数据源之间缺乏统一的数据标准、模型接口与评估协议，严重制约了研究成果的可复现性、跨数据集基准测试能力以及跨区域迁移能力。现有轨迹数据集在坐标系、目标表征方式和元数据字段等方面采用互不兼容的约定，迫使研究人员为每个数据集与仿真器组合单独构建定制化预处理流程。为应对上述挑战，我们提出 Ozone——一个围绕五个相互关联层级（硬件层、数据层、模型层、评估层与原型层）构建的交通研究统一平台，各层级均配备标准化模式、自动化转换流水线及可互操作接口。在首版发布中，数据模式将四个轨迹数据集（NGSIM、highD、CitySim 和 UTE）统一为规范格式，包含朝向边界框（oriented bounding boxes）、运动学变量及预计算的代理安全指标（surrogate safety measures）。基于 CARLA 的数字孪生地图与标定后的交通模型共同构成集成式基准测试环境。在人因研究、交通场景生成与安全关键建模等案例研究中，Ozone 将实验搭建时间减少 85%，安全模型的跨城市迁移效率达 91%，跨数据集可复现性提升至方差控制在 3% 以内。源代码与数据集均已公开。

Intelligent Transportation Systems increasingly depend on heterogeneous data from roadside cameras, UAV imagery, LiDAR, and in-vehicle sensors, yet the lack of unified data standards, model interfaces, and evaluation protocols across these sources hampers reproducibility, cross-dataset benchmarking, and cross-region transferability of research findings. Existing trajectory datasets follow incompatible conventions for coordinate systems, object representations, and metadata fields, forcing researchers to build custom preprocessing pipelines for each dataset and simulator combination. To address these challenges, we propose Ozone, a unified platform for transportation research organized around five interconnected layers -- Hardware, Data, Model, Evaluation, and Prototype -- each with standardized schemas, automated conversion pipelines, and interoperable interfaces. In the first release, the data schema unifies four trajectory datasets -- NGSIM, highD, CitySim, and UTE -- into a canonical format with oriented bounding boxes, kinematic variables, and pre-computed surrogate safety measures. Digital-twin maps in CARLA and calibrated traffic models provide integrated benchmarking environments. Case studies in human-factor research, traffic scene generation, and safety-critical modeling demonstrate that Ozone reduces experiment setup time by 85%, achieves 91% cross-city transfer efficiency for safety models, and improves cross-dataset reproducibility to within 3% variance. The source code and datasets are publicly available.