Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development
Parking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The prop...
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| Format: | Article |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2645.pdf |
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| author | Anchal Dahiya Pooja Mittal Yogesh Kumar Sharma Umesh Kumar Lilhore Sarita Simaiya Mohd Anul Haq Mohammed A. Aleisa Abdullah Alenizi |
| author_facet | Anchal Dahiya Pooja Mittal Yogesh Kumar Sharma Umesh Kumar Lilhore Sarita Simaiya Mohd Anul Haq Mohammed A. Aleisa Abdullah Alenizi |
| author_sort | Anchal Dahiya |
| collection | DOAJ |
| description | Parking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The proposed model works in two phases: in first phase, auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are integrated. Further, in second phase, backpropagation neural network (BPNN) is used to improve the accuracy of parking space prediction by reducing number of errors. The model utilizes the ARIMA model for handling linear values and the LSTM model for targeting non-linear values of the dataset. The Melbourne Internet of Things (IoT) based dataset, is used for implementing the proposed hybrid model. It consists of the data collected from the sensors that are employed in smart parking areas of the city. Before analysis, data was pre-processed to remove noise from the dataset and real time information collected from different sensors to predict the results accurately. The proposed hybrid model achieves the minimum mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.32, 0.48, and 0.56, respectively. Further, to verify the generalizability of the proposed hybrid model, it is also implemented on the Harvard IoT-based dataset. It achieves the minimum MSE, MAE, and RMSE values of 0.31, 0.47, and 0.56, respectively. Therefore, the proposed hybrid model outperforms both datasets by achieving minimum error, even when compared with the performance of other existing models. The proposed hybrid model can potentially improve parking space prediction, contributing to sustainable and economical smart cities and enhancing the quality of life for citizens. |
| format | Article |
| id | doaj-art-e0ff3fa3e5d94592a968e14210f08938 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e0ff3fa3e5d94592a968e14210f089382025-01-26T15:05:08ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e264510.7717/peerj-cs.2645Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city developmentAnchal Dahiya0Pooja Mittal1Yogesh Kumar Sharma2Umesh Kumar Lilhore3Sarita Simaiya4Mohd Anul Haq5Mohammed A. Aleisa6Abdullah Alenizi7Department of Computer Science & Applications, MDU, Rohtak, Haryana, IndiaDepartment of Computer Science & Applications, MDU, Rohtak, Haryana, IndiaDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Field, Vaddeswaram, Guntur, AP, IndiaDepartment of Computer Science and Engineering, Galgotias University, Greater Noida, UP, IndiaDepartment of Computer Science and Engineering, Galgotias University, Greater Noida, UP, IndiaCollege of Computer and Information Sciences, Majmaah University, Department of Computer Science, Al-Majmaah, Saudi ArabiaCollege of Computer and Information Sciences, Majmaah University, Department of Computer Science, Al-Majmaah, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi ArabiaParking space prediction is a significant aspect of smart cities. It is essential for addressing traffic congestion challenges and low parking availability in urban areas. The present research mainly focuses on proposing a novel scalable hybrid model for accurately predicting parking space. The proposed model works in two phases: in first phase, auto-regressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are integrated. Further, in second phase, backpropagation neural network (BPNN) is used to improve the accuracy of parking space prediction by reducing number of errors. The model utilizes the ARIMA model for handling linear values and the LSTM model for targeting non-linear values of the dataset. The Melbourne Internet of Things (IoT) based dataset, is used for implementing the proposed hybrid model. It consists of the data collected from the sensors that are employed in smart parking areas of the city. Before analysis, data was pre-processed to remove noise from the dataset and real time information collected from different sensors to predict the results accurately. The proposed hybrid model achieves the minimum mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) values of 0.32, 0.48, and 0.56, respectively. Further, to verify the generalizability of the proposed hybrid model, it is also implemented on the Harvard IoT-based dataset. It achieves the minimum MSE, MAE, and RMSE values of 0.31, 0.47, and 0.56, respectively. Therefore, the proposed hybrid model outperforms both datasets by achieving minimum error, even when compared with the performance of other existing models. The proposed hybrid model can potentially improve parking space prediction, contributing to sustainable and economical smart cities and enhancing the quality of life for citizens.https://peerj.com/articles/cs-2645.pdfSmart cityDeep learningARIMAParking spaceLSTMIoT |
| spellingShingle | Anchal Dahiya Pooja Mittal Yogesh Kumar Sharma Umesh Kumar Lilhore Sarita Simaiya Mohd Anul Haq Mohammed A. Aleisa Abdullah Alenizi Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development PeerJ Computer Science Smart city Deep learning ARIMA Parking space LSTM IoT |
| title | Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development |
| title_full | Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development |
| title_fullStr | Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development |
| title_full_unstemmed | Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development |
| title_short | Hybrid parking space prediction model: integrating ARIMA, Long short-term memory (LSTM), and backpropagation neural network (BPNN) for smart city development |
| title_sort | hybrid parking space prediction model integrating arima long short term memory lstm and backpropagation neural network bpnn for smart city development |
| topic | Smart city Deep learning ARIMA Parking space LSTM IoT |
| url | https://peerj.com/articles/cs-2645.pdf |
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