Predicting vacant parking space availability: A DWT-Bi-LSTM model

Abstract

Accurate and efficient prediction of vacant parking space availability, despite its great significance, is no easy a task. How to address the noise in the original data and how to improve the efficiency and accuracy of prediction are among the thorny problems many existing prediction methods are confronted with. To overcome these problems, this paper proposes a DWT-Bi-LSTM model for parking space availability prediction based on historical parking data. This model combines wavelet transform (WT) and bidirectional long short-term memory (Bi-LSTM). Firstly, a multi-scale decomposition of the time series is performed using WT, and the detailed series at each scale are de-noised using the threshold method. Next, a LSTM model based on Bi-LSTM neural network is established to learn from the historical denoised data and the predicted time series are further trained to effectively avoid large prediction error. Finally, the prediction accuracy is further improved by taking advantage of the capacity of the forward and backward LSTM of capturing time-series and long-range dependence. The effectiveness of the proposed model is verified using the real-world data of a parking lot in Chongqing, China and the experimental results show that compared with other prediction methods, the proposed model demonstrates higher prediction accuracy and faster training speed under the same conditions.