Abstract
Problems with parking have resulted in traffic congestion, social phobia, and smog, as well as an inefficient allocation of resources as a result of the city’s growing population. The importance of computer-aided methods and existing methods for parking prediction are analyzed. On this basis, the existing deep learning and mobile deep learning methods were investigated in depth and found that the existing methods are dependent on the cloud server and also have issues related to accuracy and response time. MDLpark, a novel mobile deep learning architecture-based approach for parking occupancy prediction is proposed. The method is based on Temporal Convolutional Network (TCN), which uses a one-dimensional fully convolutional network architecture through TCN, which utilizes its convolution and dilation to make it dynamically adaptive to the prediction window and prediction response. We restructured the residual block of TCN by replacing the weight normalization with batch normalization, which is more stable than weight normalization. The proposed MDLpark prediction method is implemented based on Keras and TensorFlow, and a mobile deep learning parking application is designed. Experimental results show that compared with other models (TCN, LSTM, GRU and MLP), MDLpark achieves higher prediction accuracy with 97.6% accuracy (97.3%, 84.3%, 95.6%, and 95.7% for TCN, LSTM, GRU, and MLP, respectively). At the same time, through the developed model, the traveler’s time to find a parking space is reduced, and the function of direction to these parking lots is provided.
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Acknowledgment
This research was supported by the National Natural Science Foundation of China (Nos. 62172336 and 62032018).
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Rahman, M.T., Zhang, Y., Arani, S.A., Shao, W. (2022). MDLpark: Available Parking Prediction for Smart Parking Through Mobile Deep Learning. In: Ma, H., Wang, X., Cheng, L., Cui, L., Liu, L., Zeng, A. (eds) Wireless Sensor Networks. CWSN 2022. Communications in Computer and Information Science, vol 1715. Springer, Singapore. https://doi.org/10.1007/978-981-19-8350-4_15
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