Short-time prediction of parking demand based on LSTM neural network model

Lijie Chen; Tao Wang; Sixuan Li; Jiahao Zhang

doi:10.1117/12.2686377

10 August 2023 Short-time prediction of parking demand based on LSTM neural network model

Lijie Chen, Tao Wang, Sixuan Li, Jiahao Zhang

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275933 (2023) https://doi.org/10.1117/12.2686377
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

In order to study the problem of short term prediction of parking demand in the region, long short-term memory (LSTM) neural network model has been used to predict the corresponding parking demand at subsequent time points based on the historical parking demand changes. Using the historical order data of on-street parking in Guilin, the data were organized into time series of 15-minute periods, and processed by noise reduction using wavelet threshold denoising method to train and test the model. The experimental results show that the prediction accuracy of the LSTM model is higher (MSE=11.588, RMSE=3.404, MAE=2.079, R2=0.945) compared with the traditional back propagation (BP) and wavelet neural network (WNN) neural network algorithm, and the prediction results are more similar to the real results. It can be seen that the use of LSTM recurrent neural network is effective and feasible for short time forecasting of berth demand in the region.

Citation Download Citation

Lijie Chen, Tao Wang, Sixuan Li, and Jiahao Zhang "Short-time prediction of parking demand based on LSTM neural network model", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275933 (10 August 2023); https://doi.org/10.1117/12.2686377

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