QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems
The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have bee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1485 |
| _version_ | 1797623170506686464 |
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| author | Ramesh Sneka Nandhini Ramanathan Lakshmanan |
| author_facet | Ramesh Sneka Nandhini Ramanathan Lakshmanan |
| author_sort | Ramesh Sneka Nandhini |
| collection | DOAJ |
| description | The rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority. |
| first_indexed | 2024-03-11T09:24:52Z |
| format | Article |
| id | doaj.art-24c72f73e37346ef8261e66e68b4d572 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:24:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-24c72f73e37346ef8261e66e68b4d5722023-11-16T18:01:46ZengMDPI AGSensors1424-82202023-01-01233148510.3390/s23031485QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical SystemsRamesh Sneka Nandhini0Ramanathan Lakshmanan1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, IndiaThe rapid growth of industry and the economy has contributed to a tremendous increase in traffic in all urban areas. People face the problem of traffic congestion frequently in their day-to-day life. To alleviate congestion and provide traffic guidance and control, several types of research have been carried out in the past to develop suitable computational models for short- and long-term traffic. This study developed an effective multi-dimensional dataset-based model in cyber–physical systems for more accurate traffic-volume prediction. The integration of quantum convolutional neural network and Bayesian optimization (QCNN_BaOpt) constituted the proposed model in this study. Furthermore, optimal tuning of hyperparameters was carried out using Bayesian optimization. The constructed model was evaluated using the US accident dataset records available in Kaggle, which comprise 1.5 million records. The dataset consists of 47 attributes describing spatial and temporal behavior, accidents, and weather characteristics. The efficiency of the proposed model was evaluated by calculating various metrics. The performance of the proposed model was assessed as having an accuracy of 99.3%. Furthermore, the proposed model was compared against the existing state-of-the-art models to demonstrate its superiority.https://www.mdpi.com/1424-8220/23/3/1485cyber–physical system (CPS)Bayesian optimization hyper tuningquantum convolutional neural network (QCNN)traffic volume prediction |
| spellingShingle | Ramesh Sneka Nandhini Ramanathan Lakshmanan QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems Sensors cyber–physical system (CPS) Bayesian optimization hyper tuning quantum convolutional neural network (QCNN) traffic volume prediction |
| title | QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems |
| title_full | QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems |
| title_fullStr | QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems |
| title_full_unstemmed | QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems |
| title_short | QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems |
| title_sort | qcnn baopt multi dimensional data based traffic volume prediction in cyber physical systems |
| topic | cyber–physical system (CPS) Bayesian optimization hyper tuning quantum convolutional neural network (QCNN) traffic volume prediction |
| url | https://www.mdpi.com/1424-8220/23/3/1485 |
| work_keys_str_mv | AT rameshsnekanandhini qcnnbaoptmultidimensionaldatabasedtrafficvolumepredictionincyberphysicalsystems AT ramanathanlakshmanan qcnnbaoptmultidimensionaldatabasedtrafficvolumepredictionincyberphysicalsystems |