Collaborative multiview time series modeling for vehicle maintenance demand prediction
Abstract Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. However, current methods only predict maintenance demand for specific vehicle components and lack the capability to offer a comprehensive predict...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96720-1 |
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| author | Fanghua Chen Deguang Shang Gang Zhou Ke Ye Fujie Ren Guofang Wu |
| author_facet | Fanghua Chen Deguang Shang Gang Zhou Ke Ye Fujie Ren Guofang Wu |
| author_sort | Fanghua Chen |
| collection | DOAJ |
| description | Abstract Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. However, current methods only predict maintenance demand for specific vehicle components and lack the capability to offer a comprehensive prediction of all maintenance demands. Furthermore, predicting vehicle maintenance demand must incorporate the impacts of various essential maintenance projects on subsequent demands. To address these challenges, we propose an innovative method for predicting vehicle all maintenance demands based on collaborative multiview time series modeling. Leveraging the interdependencies among vehicle maintenance projects across various time periods, we employ a temporal dependency learning approach utilizing a multi-attention mechanism. To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. To capture the significant impact of key maintenance projects on future demands, we propose a module that leverages both long short-term memory networks and the attention mechanism. Experimental results on actual vehicle maintenance records confirm that the proposed model outperforms existing methods, demonstrating its efficacy and applicability in predicting vehicle maintenance demand. |
| format | Article |
| id | doaj-art-e6f6620b9dc4455c8a932a928a16cdf2 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e6f6620b9dc4455c8a932a928a16cdf22025-08-20T03:18:27ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-96720-1Collaborative multiview time series modeling for vehicle maintenance demand predictionFanghua Chen0Deguang Shang1Gang Zhou2Ke Ye3Fujie Ren4Guofang Wu5Automobile Transportation Research Center, Research Institute of Highway Ministry of TransportCollege of Mechanical and Energy Engineering, Beijing University of TechnologyAutomobile Transportation Research Center, Research Institute of Highway Ministry of TransportCollege of Mechanical and Energy Engineering, Beijing University of TechnologyCollege of Mechanical and Energy Engineering, Beijing University of TechnologyAutomobile Transportation Research Center, Research Institute of Highway Ministry of TransportAbstract Accurate prediction of vehicle maintenance demands is crucial for sustaining vehicle use, optimizing performance, and minimizing ownership costs. However, current methods only predict maintenance demand for specific vehicle components and lack the capability to offer a comprehensive prediction of all maintenance demands. Furthermore, predicting vehicle maintenance demand must incorporate the impacts of various essential maintenance projects on subsequent demands. To address these challenges, we propose an innovative method for predicting vehicle all maintenance demands based on collaborative multiview time series modeling. Leveraging the interdependencies among vehicle maintenance projects across various time periods, we employ a temporal dependency learning approach utilizing a multi-attention mechanism. To enhance the interaction between distinct time points and temporal dependencies, we developed a dependency-aware learning algorithm that effectively integrates and weighs the information and dependencies at each time step, thereby improving the model’s ability to capture the complex relationships among maintenance projects over time. To capture the significant impact of key maintenance projects on future demands, we propose a module that leverages both long short-term memory networks and the attention mechanism. Experimental results on actual vehicle maintenance records confirm that the proposed model outperforms existing methods, demonstrating its efficacy and applicability in predicting vehicle maintenance demand.https://doi.org/10.1038/s41598-025-96720-1Vehicle maintenanceDemand predictionGated recurrent unitAttention mechanismLong and short-term memory network |
| spellingShingle | Fanghua Chen Deguang Shang Gang Zhou Ke Ye Fujie Ren Guofang Wu Collaborative multiview time series modeling for vehicle maintenance demand prediction Scientific Reports Vehicle maintenance Demand prediction Gated recurrent unit Attention mechanism Long and short-term memory network |
| title | Collaborative multiview time series modeling for vehicle maintenance demand prediction |
| title_full | Collaborative multiview time series modeling for vehicle maintenance demand prediction |
| title_fullStr | Collaborative multiview time series modeling for vehicle maintenance demand prediction |
| title_full_unstemmed | Collaborative multiview time series modeling for vehicle maintenance demand prediction |
| title_short | Collaborative multiview time series modeling for vehicle maintenance demand prediction |
| title_sort | collaborative multiview time series modeling for vehicle maintenance demand prediction |
| topic | Vehicle maintenance Demand prediction Gated recurrent unit Attention mechanism Long and short-term memory network |
| url | https://doi.org/10.1038/s41598-025-96720-1 |
| work_keys_str_mv | AT fanghuachen collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction AT deguangshang collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction AT gangzhou collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction AT keye collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction AT fujieren collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction AT guofangwu collaborativemultiviewtimeseriesmodelingforvehiclemaintenancedemandprediction |