Short-term Prediction of Parking Demand for Parking Delicacy Management

Abstract:

Parking guidance system (GPS) is an effective way to alleviate traffic congestion, but as a key technology for releasing vacant parking spaces, the short-time accurate prediction of parking demand has not been effectively solved. Parking demand data were grouped based on the linear stability of the time-varying characteristic curves and the significant variability of the amplitudes among the working days. GRU (gated recurrent unit) Model was introduced to the accurate short-term prediction of parking demand. The model with a simpler logic gate control structure could memorize the time series data. Study results show that compared with the traditional neural network and ARIMA (autoregressive integrated moving average) Model, the proposed GRU model offers a satisfactory prediction accuracy.