Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation
The well-being of residents is a top priority for megacities, which is why urban design and sustainable development are crucial topics. Quality of Life (QoL) is used as an effective key performance index (KPI) to measure the efficiency of a city plan’s quantity and quality factors. For city dwellers...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/13/2907 |
| _version_ | 1797591811794927616 |
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| author | Thanasit Rithanasophon Kitsaphon Thitisiriwech Pittipol Kantavat Boonserm Kijsirikul Yuji Iwahori Shinji Fukui Kazuki Nakamura Yoshitsugu Hayashi |
| author_facet | Thanasit Rithanasophon Kitsaphon Thitisiriwech Pittipol Kantavat Boonserm Kijsirikul Yuji Iwahori Shinji Fukui Kazuki Nakamura Yoshitsugu Hayashi |
| author_sort | Thanasit Rithanasophon |
| collection | DOAJ |
| description | The well-being of residents is a top priority for megacities, which is why urban design and sustainable development are crucial topics. Quality of Life (QoL) is used as an effective key performance index (KPI) to measure the efficiency of a city plan’s quantity and quality factors. For city dwellers, QoL for pedestrians is also significant. The walkability concept evaluates and analyzes the QoL in a walking scene. However, the traditional questionnaire survey approach is costly, time-consuming, and limited in its evaluation area. To overcome these limitations, the paper proposes using artificial intelligence (AI) technology to evaluate walkability data collected through a questionnaire survey using virtual reality (VR) tools. The proposed method involves knowledge extraction using deep convolutional neural networks (DCNNs) for information extraction and deep learning (DL) models to infer QoL scores. Knowledge distillation (KD) is also applied to reduce the model size and improve real-time performance. The experiment results demonstrate that the proposed approach is practical and can be considered an alternative method for acquiring QoL. |
| first_indexed | 2024-03-11T01:42:42Z |
| format | Article |
| id | doaj.art-1421db71efec42029deffb505d00fc0a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:42:42Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1421db71efec42029deffb505d00fc0a2023-11-18T16:25:10ZengMDPI AGElectronics2079-92922023-07-011213290710.3390/electronics12132907Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge DistillationThanasit Rithanasophon0Kitsaphon Thitisiriwech1Pittipol Kantavat2Boonserm Kijsirikul3Yuji Iwahori4Shinji Fukui5Kazuki Nakamura6Yoshitsugu Hayashi7Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Pathumwan, Bangkok 10330, ThailandDepartment of Computer Science, Chubu University, Kasugai 487-8501, JapanFaculty of Education, Aichi University of Education, Kariya 448-8542, JapanDepartment of Civil Engineering, Meijo University, Nagoya 468-8502, JapanCenter for Sustainable Development and Global Smart City, Chubu University, Kasugai 487-8501, JapanThe well-being of residents is a top priority for megacities, which is why urban design and sustainable development are crucial topics. Quality of Life (QoL) is used as an effective key performance index (KPI) to measure the efficiency of a city plan’s quantity and quality factors. For city dwellers, QoL for pedestrians is also significant. The walkability concept evaluates and analyzes the QoL in a walking scene. However, the traditional questionnaire survey approach is costly, time-consuming, and limited in its evaluation area. To overcome these limitations, the paper proposes using artificial intelligence (AI) technology to evaluate walkability data collected through a questionnaire survey using virtual reality (VR) tools. The proposed method involves knowledge extraction using deep convolutional neural networks (DCNNs) for information extraction and deep learning (DL) models to infer QoL scores. Knowledge distillation (KD) is also applied to reduce the model size and improve real-time performance. The experiment results demonstrate that the proposed approach is practical and can be considered an alternative method for acquiring QoL.https://www.mdpi.com/2079-9292/12/13/2907quality of lifewalking scenewalkabilitysemantic segmentationobject detectiondeep convolutional neural networks |
| spellingShingle | Thanasit Rithanasophon Kitsaphon Thitisiriwech Pittipol Kantavat Boonserm Kijsirikul Yuji Iwahori Shinji Fukui Kazuki Nakamura Yoshitsugu Hayashi Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation Electronics quality of life walking scene walkability semantic segmentation object detection deep convolutional neural networks |
| title | Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation |
| title_full | Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation |
| title_fullStr | Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation |
| title_full_unstemmed | Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation |
| title_short | Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation |
| title_sort | quality of life prediction on walking scenes using deep neural networks and performance improvement using knowledge distillation |
| topic | quality of life walking scene walkability semantic segmentation object detection deep convolutional neural networks |
| url | https://www.mdpi.com/2079-9292/12/13/2907 |
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