Parking lot occupancy prediction using long short-term memory and statistical methods
Abstract
In crowded city centers, drivers looking for available parking space generate extra traffic and in addition, the resulting excessive exhaust gases cause air pollution. Therefore, directing the drivers to a parking spot in an intelligent way is an important task for smart city applications. This task requires the prediction of occupancy states of parking lots which involves appropriate processing of the historical parking data. In this work, Long-Short Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) methods were applied to parking data collected from curbside parking spots of Adana, Turkey for predicting the parking lot occupancy rates of future values. The experiments were performed for making predictions with different prediction horizons that are 1 minute, 5 minutes, and 15 minutes. The performances of the methods were compared by calculating root mean squared error (RMSE) and mean absolute error (MAE) values. The experiments were performed on data from five different days. According to the results, when the prediction horizon is set to 1 minute, LSTM achieved RMSE and MAE values of 0.98 and 0.72, respectively. For the same prediction horizon, ARIMA achieved RMSE and MAE values of 0.62 and 0.35, respectively. On the other hand, LSTM achieved smaller error values for larger prediction horizons. In conclusion, it was shown that LSTM is more suitable for larger prediction horizons, however, ARIMA is better at predicting near-future values.
Keywords
ARIMA, Deep Learning, LSTM, Parking Occupancy, Smart Parking, Time Series Prediction
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