Abstract
Traffic congestion causes heavily energy consumption, carbon dioxide emission and air pollution in cities, which is usually created by cars searching on-street parking spaces. Drivers are likely to move slowly and waste time on the road for an available on-street parking space if parking slot availability information is not revealed in advanced. Therefore, it is necessary for city councils to provide a car parking availability prediction service which could inform car drivers vacant parking slots before they start the journey. In this paper, we propose a novel framework based on recurrent network and use the long short-term memory (LSTM) model to predict parking multi-steps ahead. The core idea of this framework is that both the occupancy rate of on-street parking in a specific region and car leaving probability are exploited as prediction performance metric. A large real parking dataset is used to evaluate the proposed approach with extensive comparative experiments. Experimental results shows the proposed model outperform the state-of-art model.
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Shao, W., Zhang, Y., Guo, B., Qin, K., Chan, J., Salim, F.D. (2019). Parking Availability Prediction with Long Short Term Memory Model. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_9
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DOI: https://doi.org/10.1007/978-3-030-15093-8_9
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