AI-Based Prediction Models for Urban Parking Availability
A Case Study of Valencia
DOI:
https://doi.org/10.62161/sauc.v11.5990Keywords:
Urban parking prediction, Smart City, Artificial Intelligence, Machine Learning, Recurrent neural networksAbstract
Efficient parking access is crucial for urban mobility in smart cities. This study presents a pilot system predicting public parking occupancy in Valencia, Spain, using municipal sensor data. We developed and compared recurrent neural network architectures (RNN, LSTM, GRU), achieving accurate forecasts with performance variations across locations and times. Explainable AI methods provided model interpretability and insights into variable influence. Results indicate that baseline recurrent models yield low MAEs, while Bayesian hyperparameter optimisation offers only marginal gains, highlighting the practicality of straightforward recurrent approaches for urban parking prediction.
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