Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality
Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/508 |
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| author | Mengjie He Yujie Zhang Yi Liu Yang Shen Chao Mi |
| author_facet | Mengjie He Yujie Zhang Yi Liu Yang Shen Chao Mi |
| author_sort | Mengjie He |
| collection | DOAJ |
| description | Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency. |
| format | Article |
| id | doaj-art-b5669f1de0a747fa89f54e2177f10d3e |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-b5669f1de0a747fa89f54e2177f10d3e2025-08-20T02:08:07ZengMDPI AGAlgorithms1999-48932024-11-01171150810.3390/a17110508Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual RealityMengjie He0Yujie Zhang1Yi Liu2Yang Shen3Chao Mi4Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, ChinaInstitute of Telecommunications, ISCTE-University Institute of Lisbon, 1649-026 Lisbon, PortugalCollege of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Higher Technical, Shanghai Maritime University, Shanghai 200136, ChinaCollege of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCurrently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.https://www.mdpi.com/1999-4893/17/11/508virtual realityport securityautomated evaluation of risk behaviorsdeep Q-networkquay crane |
| spellingShingle | Mengjie He Yujie Zhang Yi Liu Yang Shen Chao Mi Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality Algorithms virtual reality port security automated evaluation of risk behaviors deep Q-network quay crane |
| title | Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality |
| title_full | Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality |
| title_fullStr | Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality |
| title_full_unstemmed | Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality |
| title_short | Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality |
| title_sort | automated evaluation method for risk behaviors of quay crane operators at ports using virtual reality |
| topic | virtual reality port security automated evaluation of risk behaviors deep Q-network quay crane |
| url | https://www.mdpi.com/1999-4893/17/11/508 |
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