Assessing the Usability of Existing ITS Data for Autonomous-Driving AI: A Task-Specific Framework for Sustainable Data Reuse

This study assesses whether existing intelligent transportation system (ITS) data can be reused for autonomous-driving artificial intelligence (AI) based on task-specific information requirements and the recoverability of missing information. A task-specific framework was applied to five heterogeneous ITS data assets—roadside closed-circuit television (CCTV), camera-based and radar-based automatic incident detection system (AIDS) data, radar-based smart intersection system (SIS) data, and vehicle detection system (VDS) performance-evaluation data—across four data-use tasks: 2D road-user detection training, detection/measurement output validation, event/structured traffic-state retrieval, and road-referenced trajectory reconstruction. For each asset–task pair, mandatory information requirements were identified and classified as explicitly available, directly derivable or linkable, recoverable through feasible transformation, not reliably recoverable, or unconfirmed; these information-level states were then translated into task-specific usability. The results showed that usability varied substantially by task. Camera-based AIDS provided the broadest direct usability, whereas roadside CCTV showed greater reuse potential through transformation. Radar-based AIDS, radar-based SIS, and VDS performance-evaluation data were directly usable for validation and structured traffic-state retrieval, while visual-training and trajectory-reconstruction usability was more limited. Performance-evaluation records showed particular value for validation because they preserved target outputs, validated reference information, and their correspondence. These findings demonstrate that the reuse value of existing ITS data depends on the information actually retained and its recoverability rather than on sensing modality alone. By identifying reusable data and selective future-retention needs, the framework supports more resource-efficient management of digital ITS assets and links task-specific data reuse to sustainable transportation.