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.

翻译：智能交通系统日益依赖来自路侧摄像头、无人机影像、激光雷达和车载传感器的异构数据，然而这些数据源缺乏统一的数据标准、模型接口和评估协议，严重影响了研究成果的可复现性、跨数据集基准测试以及跨区域迁移能力。现有轨迹数据集在坐标系、对象表征和元数据字段上遵循互不兼容的规范，迫使研究人员为每个数据集和仿真器组合构建定制化的预处理流程。为解决上述挑战，我们提出臭氧平台——一个围绕硬件层、数据层、模型层、评估层和原型层五个互连层次组织的交通研究统一平台，各层均配备标准化模式、自动化转换管道和可互操作接口。在首个版本中，数据模式将NGSIM、highD、CitySim和UTE四个轨迹数据集统一为标准格式，包含有向包围盒、运动学变量及预计算代理安全度量指标。基于CARLA的数字孪生地图和标定后的交通模型提供了集成化的基准测试环境。在人因工程研究、交通场景生成及安全关键建模等案例中，臭氧将实验设置时间减少85%，安全模型的跨城市迁移效率达91%，并将跨数据集复现性控制在3%方差范围内。源代码和数据集已公开。