This dataset provides continuous license plate recognition captures from fixed roadside cameras along a major U.S. metropolitan corridor, representing real vehicles in transit. Each detection delivers a vehicle profile including plate text and issuing state, vehicle type, make, model, color, per-field AI confidence scores, GPS-anchored camera location, and timestamped raw image frames. Built for computer vision and traffic analytics, the dataset supports model training, validation, vehicle composition studies, temporal flow and congestion analysis, and verifiable audit trails using imagery. Spatially enabled for mapping, lane-level and site-level analysis, and regional traffic composition, the collection reflects interstate diversity rather than a single-state population and includes quality indicators to filter by precision.

Key attributes include:

• Vehicle-level detections capturing plate region and plate text
• Rich vehicle profile: type, make, model, and color
• Per-field AI confidence scores for plate reads, make, and color
• GPS-anchored camera locations, lane context, and timestamps
• Raw image frames per record for verification and model training

Transportation agencies, CV teams, and logistics planners can use this dataset to improve traffic management, validate recognition models, and analyze cross-regional vehicle composition. Confidence metrics enable tailored quality filters for diverse operational and research needs.