Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations
Abstract Intelligent assistants often struggle with the complexity of spatiotemporal models used for understanding objects and environments. The construction and usage of such models demand significant computational resources. This article introduces a novel multilevel spatiotemporal model and a com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42535-x |
| _version_ | 1797452628797423616 |
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| author | Man Tianxing Alexander Vodyaho Nataly Zhukova Alexey Subbotin Yulia Shichkina |
| author_facet | Man Tianxing Alexander Vodyaho Nataly Zhukova Alexey Subbotin Yulia Shichkina |
| author_sort | Man Tianxing |
| collection | DOAJ |
| description | Abstract Intelligent assistants often struggle with the complexity of spatiotemporal models used for understanding objects and environments. The construction and usage of such models demand significant computational resources. This article introduces a novel multilevel spatiotemporal model and a computationally efficient construction method. To facilitate model construction on different levels, we employ a meta-mining technique. Furthermore, the proposed model is specifically designed to excel in foggy environments. As a practical application, we develop an intelligent assistant focused on enhancing subway passenger safety. We present case examples involving jammed objects, such as shoes, in escalator combs. Our results demonstrate the effectiveness of the proposed model and method. Specifically, the accuracy of breakdown detection has improved by 10% compared to existing information systems used in subways. Moreover, the time required to build a spatiotemporal model is reduced by 2.3 times, further highlighting the efficiency of our approach. Our research offers a promising solution for intelligent assistants dealing with complex spatiotemporal modeling, with practical applications in ensuring subway passenger safety. |
| first_indexed | 2024-03-09T15:11:25Z |
| format | Article |
| id | doaj.art-1212c423fe2940b18d31bfe5d348dabc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:11:25Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1212c423fe2940b18d31bfe5d348dabc2023-11-26T13:18:36ZengNature PortfolioScientific Reports2045-23222023-09-0113111610.1038/s41598-023-42535-xUrban intelligent assistant on the example of the escalator passenger safety management at the subway stationsMan Tianxing0Alexander Vodyaho1Nataly Zhukova2Alexey Subbotin3Yulia Shichkina4School of Artificial Intelligence, Jilin UniversitySaint-Petersburg State Electrotechnical University “LETI”Laboratory of Big Data Technologies in Socio-Cyberphysical Systems, Saint-Petersburg Federal Research Centre of the Russian Academy of SciencesSaint-Petersburg State Electrotechnical University “LETI”Saint-Petersburg State Electrotechnical University “LETI”Abstract Intelligent assistants often struggle with the complexity of spatiotemporal models used for understanding objects and environments. The construction and usage of such models demand significant computational resources. This article introduces a novel multilevel spatiotemporal model and a computationally efficient construction method. To facilitate model construction on different levels, we employ a meta-mining technique. Furthermore, the proposed model is specifically designed to excel in foggy environments. As a practical application, we develop an intelligent assistant focused on enhancing subway passenger safety. We present case examples involving jammed objects, such as shoes, in escalator combs. Our results demonstrate the effectiveness of the proposed model and method. Specifically, the accuracy of breakdown detection has improved by 10% compared to existing information systems used in subways. Moreover, the time required to build a spatiotemporal model is reduced by 2.3 times, further highlighting the efficiency of our approach. Our research offers a promising solution for intelligent assistants dealing with complex spatiotemporal modeling, with practical applications in ensuring subway passenger safety.https://doi.org/10.1038/s41598-023-42535-x |
| spellingShingle | Man Tianxing Alexander Vodyaho Nataly Zhukova Alexey Subbotin Yulia Shichkina Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations Scientific Reports |
| title | Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| title_full | Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| title_fullStr | Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| title_full_unstemmed | Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| title_short | Urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| title_sort | urban intelligent assistant on the example of the escalator passenger safety management at the subway stations |
| url | https://doi.org/10.1038/s41598-023-42535-x |
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