Evaluation of Prediction Methods for Parking Occupancy Rate

Abstract:

This paper analyzed temporary characteristics of parking occupancy rate (POR) at three different types of parking lots, i.e., shopping mall, office building and stadium, and evaluated the applicability of autoregressive integrated moving average method(ARIMA), Kalman filter and BP neural networks on the prediction of POR, based on parking lot detection data at Wujiaochang District, Shanghai. The results show that ARIMA and BP neural networks can achieve higher prediction accuracies as compared with the Kalman filter method, and the BP neural networks performs best for the shortterm prediction of shopping mall and office building. The prediction accuracy of the three methods decreases as the forecasting time step increases. Different prediction accuracies exist for different types of parking lots, and the prediction accuracy for weekdays is higher than that for weekends. And the model has good adaptability. This paper can provide reference for the selection of prediction methods for different types of parking lots.

Key words:parking occupancy rate prediction; autoregressive integrated moving average model(ARIMA); Kalman filter; back propagation neural networks