Optimising Parking Systems With IoT and a Multilayer Machine Learning Approach for Accurate Spot Prediction and Vehicle Detection

The Internet of Things (IoT) has revolutionised the maintenance of parking systems. By incorporating IoT technology, parking systems can assist drivers in quickly locating appropriate parking spaces, alleviating traffic congestion, and minimising emissions from vehicles meandering in search of parking. This paper proposes a machine learning multilayer model that efficiently handles data generated by the sensors; it works at two layers to accurately predict appropriate parking spaces according to the vehicle type. Based on the survey conducted by the researchers, we use different models at different layers. At the first layer, an ensemble technique predicts the available parking spot; in the second layer, a random forest technique detects the type of vehicle. The integration of these techniques makes the proposed multilayer model novel, time‐efficient, and cost‐effective. These techniques are selected because they can handle complex data patterns, use the model's different strengths, and achieve high accuracy in prediction and classification tasks. The proposed multilayer model is implemented on the sensor dataset extracted from the Harvard Dataverse. The model is implemented using Python in a Jupyter notebook, and the evaluation metrics include accuracy, recall, precision, F1 score, and ROC curve. The proposed multilayer model obtained an accuracy of 97.88%, a precision of 96.11%, a recall of 95.69%, an F1 score of 96.04%, and an AUC score of 0.89. The results prove that the proposed multilayer model achieves the highest accuracy among all existing models in predicting the parking space with appropriate vehicle detection for the most effective parking space allocation in real‐time scenarios.