Predicting Vacant Parking Space Availability: A Long Short-Term Memory Approach
Junkai Fan, Qian Hu, Yingying Xu, Zhenzhou Tang
Abstract
The accurate prediction of vacant parking space availability is becoming increasingly essential for assisting drivers to determine where to park in advance. It helps ease traffic pressure and reduce gas emission and pollution. This article proposes a novel multistep long short-term memory recurrent neural network (LSTM-NN) model to predict the number of the vacant parking spaces on the basis of historical parking availability information. The key parameters of the model are deeply optimized. The prediction model is fully benchmarked with five well-known machine learning models—i.e. a gated recurrent units neural network, a stacked autoencoder, a support vector regression, a back propagation neural network, and a <i>k</i>-nearest neighbor algorithm—whose key parameters are sufficiently optimized as well. Adequate experiments with practical data collected from two parking lots with various capacities and traffic flows were conducted to evaluate the models’ performances on short- and long-term predictions. Experimental results show that the proposed multistep LSTM-NN model outperforms all the benchmark models, especially in a commercial parking lot with heavy traffic flow.