Spatiotemporal Deep-Learning Networks for Shared-Parking Demand Prediction
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 6
Abstract
One fundamental issue in managing a shared-parking system is predicting shared-parking demand. Such predictions are very challenging because predicting shared-parking demand usually involves nonlinearities and complex spatiotemporal dependencies. In this paper, we propose a deep learning–based network comprising of three modeling components—CNN-Module, Conv-LSTM-Module, and LSTM-Module—to predict the shared-parking inflow and outflow in each region of a shared-parking system. First, the CNN-Module utilized convolution neural networks to determine local spatial dependencies. Second, the Conv-LSTM-Module leveraged the Conv-LSTM neural network to capture similarities of shared-parking demand across different regions. Finally, the LSTM-Module was applied to model temporal features by using the Long Short-Term Memory (LSTM) network. Moreover, we also divided the input into three components (recent, daily, and weekly) to extract the periodically shifted relations. The model was evaluated using a real-world shared-parking data set in Chengdu, China. Experiments showed that our model outperforms six other well-known baseline methods within an acceptable time frame. Extensive additional experiments and evaluations were conducted to investigate the sensitivity of our model.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
This work is supported by the Science & Technology Project of Sichuan Province (No. 2020YJ0255), Scientific Research Project of China Railway Eryuan Engineering Group CO. LTD KYY2019027(19-20) and China Scholarship Council awards. Thanks for the data provided by ChengduTutu Le Technology co. LTD.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 6 • June 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 12, 2020
Accepted: Dec 30, 2020
Published online: Mar 25, 2021
Published in print: Jun 1, 2021
Discussion open until: Aug 25, 2021
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