Uncertainty-Aware License Plate Recognition for Data-Driven Urban Traffic Analytics

Urban transportation analytics increasingly rely on automated sensing infrastructures to generate actionable insights for planning and management. However, Automatic Vehicle Number Plate Recognition (ANPR) systems suffer performance degradation when applied to embossed license plates in developing-country environments, where illumination variability, bilingual scripts, and infrastructure idiosyncrasies introduce significant noise. This study proposes an uncertainty-aware, analytics-driven framework that integrates deep learning–based detection, bilingual optical character recognition, and structured urban intelligence modeling for end-to-end traffic analytics. We curate a bespoke dataset of 5,000 embossed Nepali license plate images captured across heterogeneous environmental conditions (daylight, dusk, low light, rain, and occlusion). Vehicle detection is performed using YOLOv8, and bilingual OCR is implemented via PaddleOCR with script-aware confidence calibration. Recognized events are aggregated to form structured event logs supporting statistical traffic density estimation and short-term forecasting using autoregressive integrated moving average (ARIMA) models with quantified uncertainty. Our empirical evaluation shows 98.2% detection accuracy under daytime conditions and 91.4% overall recognition accuracy, with statistically significant improvements over baseline configurations (t = 3.87, p = 0.0023). Forecasting results achieve MAE = 14.3 vehicles/hour and RMSE = 21.8 vehicles/hour, reducing forecasting error by 39.4% relative to a naïve persistence baseline, while prediction intervals capture 93% of observed values at 95% confidence. We further demonstrate that propagating recognition confidence into density estimation reduces aggregation bias under adverse conditions. The proposed framework extends conventional ANPR pipelines by embedding recognition uncertainty into downstream analytics, thereby providing a scalable data-driven foundation for intelligent transportation systems in resource-constrained urban environments.