Ozone: A Unified Platform for Transportation Research

2026-04-12

AI Summary

This study addresses the challenges in intelligent transportation research stemming from the absence of unified standards for multi-source heterogeneous data, inconsistent model interfaces, and non-standardized evaluation protocols, which collectively hinder reproducibility, cross-dataset benchmarking, and cross-regional transferability. To overcome these limitations, the authors propose a unified five-layer platform encompassing hardware, data, models, evaluation, and prototyping. The work introduces an object-oriented trajectory representation—incorporating oriented bounding boxes and kinematic variables—that enables, for the first time, a standardized format across diverse trajectory datasets. The platform integrates CARLA-based digital twin maps, calibrated traffic models, automated data conversion pipelines, and standardized evaluation protocols. Experimental results demonstrate an 85% reduction in experimental setup time, 91% efficiency in cross-city safety model transfer, and cross-dataset reproduction variance below 3%.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Intelligent Transportation Systems
• heterogeneous data
• unified data standards
• trajectory datasets
• cross-dataset reproducibility

Innovation

• unified platform
• trajectory standardization
• interoperable interfaces
• digital twin
• cross-dataset reproducibility