Quantum Enhanced AI to Predict the Traffic Flow

This research work proposes a quantum traffic flow prediction system based on the difficulty of traffic congestion in cities. The system uses real-time traffic data, details on weather condition, and the behavioral patterns of drivers flexibly by incorporating machine learning along with quantum algorithms as an integrated approach. The data is collected through different data collection instruments namely, traffic sensors, GPS devices, and weather stations, making the data set holistic for analysis. In this model, the basic traditional algorithms of Long Short- Term Memory (LSTM) and Convolutional Neural Networks (CNN) are applied together with quantum-based optimization approaches including the Quantum Genetic Algorithm (QGA) and Learning Vector Quantization (LVQ). The QGA component is involved in the process of feature selection and modeling parameters, LVQ operates on traffic patterns classification. Together, these quantum-enhanced algorithms increase system efficiency and ensure efficient input-output work, especially when working with extensive and variable data arrays. Hence, the present research captures the flow of the model development process and establishes the evaluation of the current model’s efficiency over the conventional prediction models. Outcomes indicate considerable enhancements in predicting novelties and regulating congestion characteristic of the system’s suitability for smart city structures and urban traffic applications.