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Parking slot occupancy prediction using LSTM

  • S.I. : Low Resource Machine Learning Algorithms (LR-MLA)
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Abstract

Nowadays due to the advancement of the smart parking solutions, the drivers are eager to know that how much vacancy will be there in a particular parking place in specific day or in a particular hour of a day. An intelligent decision support system can help to provide advance information to the drivers about the occupancy percentage of a parking area. This information should be available to any type of vehicle in an indoor parking area. In this way, parking spaces could be found more quickly during traffic searches. Parking occupancy percentage forecasting is an optimum problem. Even though many researchers have tried to solve these problems previously using various methods of deep learning, there are still some shortcomings when it comes to estimating parking space occupancy levels. As such, in this paper, parking occupancy percentage is forecasted for an indoor parking system for any type of vehicle using a modified long short-term memory model (LSTM) as a CNN-LSTM hybrid model, from which parking occupancy percentage can be predicted using the prior parking information during specific dates and hours. With the use of the Internet of Things network, cloud server, and sensors used in various smart parking places, we generate real-time information to estimate the hybrid CNN-LSTM model and the results have been discussed in detail. Comparative analysis has been done with the existing time series a model including other LSTM models, and it is found that our proposed CNN-LSTM-based models provide better results in terms of different performance measurement parameters.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the repository.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, School of Technology, Assam University, Silchar, Assam, 788011, India

    Rohit Kumar Kasera & Tapodhir Acharjee

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  1. Rohit Kumar Kasera
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Kasera, R.K., Acharjee, T. Parking slot occupancy prediction using LSTM. Innovations Syst Softw Eng (2022). https://doi.org/10.1007/s11334-022-00481-3

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