Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time, consequently helping them avoid congestion and accidents to a certain extent. However, the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency, which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure. To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining, herein, we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data (PURP). First, to ensure prediction accuracy, PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition (LPR) data as effective characteristics. Subsequently, to utilize the recent data without retraining the model online, PURP uses the nonparametric method k-Nearest Neighbor (namely KNN) as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online. The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.