| Title |
Deep Learning-Based Mobile Application Design for Smart Parking |
| ID_Doc |
42781 |
| Authors |
Canli, H; Toklu, S |
| Title |
Deep Learning-Based Mobile Application Design for Smart Parking |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/ACCESS.2021.3074887 |
| Abstract |
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. |
| Author Keywords |
Deep learning; Automobiles; Support vector machines; Vehicles; Smart cities; Space vehicles; Internet of Things; Smart city; deep learning; LSTM; support vector machine; random forest; ARIMA |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000645071600001 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09410536.pdf
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