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    Deep Learning-Based Mobile Application Design for Smart Parking
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Canli, H.; Toklu, S.
    In the era of Internet of Things (IoT) and smart city ecosystems, there is a need for innovative smart parking systems for more sustainable cities. With the increasing number of vehicles in the cities every year, it takes more time to find parking spaces. The solution methods developed are no longer sufficient. The time that passes while waiting for a parking space in traffic carries with it problems such as energy, environmental pollution and stress. In this study, a deep learning and cloud-based new mobile smart parking application was developed to minimize the problem of searching for parking spaces. Within the application, a service has been developed based on deep learning with Long short-term memory (LSTM) to predict the parking space. Here, dynamic access is provided to the LSTM-based model previously created through the mobile device of the user, and the process of displaying the occupancy rates of the parks at the desired place is accomplished on the mobile device by entering the relevant parameters. By this means, both energy and time savings have been achieved. With the real-time car parking data collected in the city of Istanbul in Turkey, high accuracy results were obtained. In order to demonstrate the effectiveness of the model proposed, it was compared with the Support Vector Machine, Random Forest and ARIMA methods. The results have confirmed the high accuracy and reliability that was promised.
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    Design and Implementation of a Prediction Approach Using Big Data and Deep Learning Techniques for Parking Occupancy
    (Springer Heidelberg, 2021) Canli, H.; Toklu, S.
    With the developing world, cities have begun to become smarter. Smart parking systems, with the ever-increasing number of vehicles, are among the important matters in smart cities. The reason for this is that the search for parking spaces that are already insufficient, brings along a serious cost, air pollution and stress issues. In this study, a new approach that attempts to forecast the parking lot occupancy rate in the short- and medium-term with its deep learning-based Gated Recurrent Units (GRU) model was proposed. Initially, data belonging to 607 carparks located in the city of Istanbul in Turkey, and weather data have been collected, and a multivariate time series data set has been created. In the second stage, to forecast the parking places that would be available in the short- and medium-term, the GRU model was used in the system proposed. To show the effectiveness of the model, the results obtained through the 27 different models were compared by means of the Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), which were some other sequence models. According to the experimental results made on the weather data obtained from ISPARK dataset and AKOM, the our proposed GRU model achieves 99.11% accuracy gave the best results with 0.90 MAE, 2.35 MSE and 1.53 RMSE metric values. Experimental results obtained with various hyperparameters clearly demonstrate the success of the GRU deep learning model in prediction parking occupancy rates.