Parking Availability Prediction with Long Short Term Memory Model

Publication Year: 2018 Publication Type : ConferenceProceeding


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.


    author = {Wei Shao and Yu Zhang and Bin Guo and Kyle Kai Qin and Jeffrey Chan and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Hangzhou, China, May 11-13, 2018, Revised Selected Papers},
    doi = {10.1007/978-3-030-15093-8_9},
    editor = {Shijian Li},
    pages = {124--137},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    timestamp = {Fri, 05 Jun 2020 01:00:00 +0200},
    title = {Parking Availability Prediction with Long Short Term Memory Model},
    url = {},
    volume = {11204},
    year = {2018}


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