In the ever-changing landscape of smart city innovation, researchers have introduced the Residual Spatial-Temporal Graph Convolutional Neural Network (RST-GCNN), which could help users find an on-street parking space more efficiently. The work is published in the International Journal of Sensor Networks.
This new model could help change the urban driving experience and perhaps reduce congestion and pollution by enhancing the prediction of parking availability.
