Traffic data quality and secure computation are critical for intelligent transportation systems (ITS). To address low-quality traffic data, we propose STAP, a spatio-temporal correlation model using an improved Random Forest for anomaly detection and XGBoost for data estimation, effectively enhancing data quality as validated by experiments on real-world data from Changsha, China. For secure outsourcing computing in vehicular fog environments, we introduce SE-VFC, which combines lightweight BLS and group signatures for anonymous batch authentication and privacy protection of fog vehicles, ensuring correctness and traceability of computations while maintaining low overhead. Compared to traditional anomaly detection methods, STAP improves average accuracy by 5%, while SE-VFC proves effective and practical in vehicular fog computing, offering low communication and computation overhead.