Adaptive few-shot tiny neural systems for real-time traffic intensity prediction in smart cities

Abstract

The rapid evolution of urban mobility and smart city demands an intelligent transportation system which can make real-time decisions using lightweight and adaptive AI models. This research introduces a novel application of tiny machine learning which will combine the features of Few-shot learning algorithm and it will classify the traffic intensity levels on regional traffic data. By converting the traffic volume into three dynamic classes (Low/ Medium/ High), a compact neural network model is trained on episodic few-shot tasks that can mimic real-world low-data learning conditions. The proposed work supports open set classification which is more suitable for detecting unknown traffic behavior analysis by considering the previous day traffic level and how the future traffic intensity level can be predicted effectively. The accuracy of the proposed method is compared with the existing methods which lie with the baseline CNN (90 %) and SVM (89 %). But the average episode accuracy achieved through the proposed model is 95.2 % which makes this model promising for low-power edge deployment in intelligent transportation system.