Effective parking management is essential for reducing congestion and enhancing urban mobility in smart cities. However, accurately forecasting parking space availability remains challenging due to seasonal and temporal variability. This study proposes a cost-effective forecasting framework based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, applied to vehicle inflow and outflow data extracted from camera-based detection systems. By capturing periodic patterns through seasonal differencing and autoregressive terms, SARIMA achieves high forecasting accuracy, evidenced by a Mean Absolute Error (MAE) of 6.38 and Root Mean Square Error (RMSE) of 7.24 across a 24 -hour horizon. The model outperforms neural network and regression baselines, particularly during peak periods. The approach is deployed via a real-time dashboard integrated with ASM Terni's infrastructure, demonstrating its scalability and practical utility. Future extensions include hybrid SARIMA-deep learning models for enhanced generalization.
SARIMA-Based Forecasting for Camera-Driven Smart Parking Systems / Ghoreishi, Mohammad; Jabari, Mostafa; Bragatto, Tommaso; Santori, Francesca; Cresta, Massimo. - (2025), pp. 1-6. (Intervento presentato al convegno 2025 IEEE International Conference on Environment and Electrical Engineering and 2025 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) tenutosi a Crete; Greece) [10.1109/eeeic/icpseurope64998.2025.11169165].
SARIMA-Based Forecasting for Camera-Driven Smart Parking Systems
Ghoreishi, Mohammad
Primo
;Jabari, Mostafa
;Bragatto, Tommaso
;Cresta, Massimo
2025
Abstract
Effective parking management is essential for reducing congestion and enhancing urban mobility in smart cities. However, accurately forecasting parking space availability remains challenging due to seasonal and temporal variability. This study proposes a cost-effective forecasting framework based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, applied to vehicle inflow and outflow data extracted from camera-based detection systems. By capturing periodic patterns through seasonal differencing and autoregressive terms, SARIMA achieves high forecasting accuracy, evidenced by a Mean Absolute Error (MAE) of 6.38 and Root Mean Square Error (RMSE) of 7.24 across a 24 -hour horizon. The model outperforms neural network and regression baselines, particularly during peak periods. The approach is deployed via a real-time dashboard integrated with ASM Terni's infrastructure, demonstrating its scalability and practical utility. Future extensions include hybrid SARIMA-deep learning models for enhanced generalization.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
