Urban Parking Demand Forecasting Using xLSTM-Informer Model - Jimei Ma; Zhong Wang (IEEE Access)
With the rapid advancement of urbanization, traffic congestion and parking shortages have emerged as critical challenges for modern cities. Accurate parking demand forecasting plays a pivotal role in supporting intelligent traffic management, resource allocation, and the efficient operation of parking facilities. While deep learning techniques have notably enhanced prediction capabilities, current studies often fail to effectively combine the strengths of diverse model architectures. In this study, we propose a hybrid forecasting model—xLSTM-Informer—which integrates the Extended Long Short-Term Memory Network (xLSTM) and the Informer architecture. To validate the model’s generalizability and scalability, we extend our analysis beyond a single commercial parking lot and include four additional sites representing diverse urban land-use types—office, residential, hospital, and transit hub areas—across different spatial scales. Extensive experiments conducted on real-world hourly parking datasets demonstrate that the proposed xLSTM-Informer model consistently outperforms baseline models. Notably, it achieves a mean absolute percentage error (MAPE) of 3.93% for vehicle entry and 8.66% for vehicle exit prediction, along with strong performance in R2, RMSE, and MAE metrics. These results highlight the model’s robustness, high accuracy, and potential for practical deployment in city-scale parking demand forecasting.