SAW: A Hybrid Prediction Model for Parking Occupancy under the Environment of Lacking Real-time Data

It is popular to develop the city-wide parking guidance system(PGS) in China nowadays, in order to alleviate the parking difficulties arising in large cities. Prediction on parking occupancy is the essential intelligent technology to help vehicles find the proper parking lot efficiently in PGS. And the known prediction methods have to be powered by real-time data, without which would cause significant inaccuracies. In the early stage of PGS deployment, however, it is very hard to collect the real-time data from the parking lots all over the city, considering the financial and time cost. So how could PGS try to keep the prediction on parking occupancy working well under the environment lacking real-time data? In this paper, we propose a method named SAW(non-stationary Stochastic And Wavelet neural network), to predict the parking occupancy at the given time, based on digging the history data. In the simulation, we compare our model with the largest Lyapunov exponents method and traditional wavelet neural network(WNN) by experimental tests using the same data, and the comparative analysis shows that the proposed model can effectively improve the long-term forecasting accuracy and achieve satisfactory results based on the smaller computational complexity.