Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties
The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Tra...
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MDPI AG
2025-05-01
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| Series: | Aerospace |
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| author | Lingzhong Meng Minggong Wu Xiangxi Wen Zhichong Zhou Qingguo Tian |
| author_facet | Lingzhong Meng Minggong Wu Xiangxi Wen Zhichong Zhou Qingguo Tian |
| author_sort | Lingzhong Meng |
| collection | DOAJ |
| description | The vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future. |
| format | Article |
| id | doaj-art-05ab9130c4d04559aa78f01a7d8e8cbd |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-05ab9130c4d04559aa78f01a7d8e8cbd2025-08-20T01:57:00ZengMDPI AGAerospace2226-43102025-05-0112541310.3390/aerospace12050413Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal UncertaintiesLingzhong Meng0Minggong Wu1Xiangxi Wen2Zhichong Zhou3Qingguo Tian4Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, ChinaAir Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, ChinaUnit 91422 of the PLA, Yantai 265200, ChinaThe vigorous development of urban air mobility (UAM) is reshaping the urban travel landscape, but it also poses severe challenges to the safe and efficient operation of dense and complex airspace. Potential conflicts between flight plans have become a core bottleneck restricting its development. Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. Meanwhile, existing global optimization strategies often face issues of inefficiency and overly broad adjustment scopes when dealing with large-scale plan conflicts. To address these challenges, this study proposes an innovative flight plan conflict management framework. First, by introducing a probabilistic model of flight time errors, a new conflict detection mechanism based on confidence intervals is constructed, significantly enhancing the ability to foresee non-obvious conflict risks. Furthermore, based on complex network theory, the framework accurately identifies a small number of “critical flight plans” that play a core role in the conflict network, revealing their key impact on chain reactions of conflicts. On this basis, a phased optimization strategy is adopted, prioritizing the adjustment of spatiotemporal parameters (departure time and speed) for these critical plans to systematically resolve most conflicts. Subsequently, only fine-tuning the speeds of non-critical plans is required to address remaining local conflicts, thereby minimizing interference with the overall operational order. Simulation results demonstrate that this framework not only significantly improves the comprehensiveness of conflict detection but also effectively reduces the total number of conflicts. Additionally, the proposed phased artificial lemming algorithm (ALA) outperforms traditional optimization algorithms in terms of solution quality. This work provides an important theoretical foundation and a practically valuable solution for developing robust and efficient UAM dynamic scheduling systems, holding promise to support the safe and orderly operation of large-scale urban air traffic in the future.https://www.mdpi.com/2226-4310/12/5/413urban air mobilityflight plan conflict managementprobabilistic conflict detectioncritical plan identificationphased optimization strategy |
| spellingShingle | Lingzhong Meng Minggong Wu Xiangxi Wen Zhichong Zhou Qingguo Tian Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties Aerospace urban air mobility flight plan conflict management probabilistic conflict detection critical plan identification phased optimization strategy |
| title | Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties |
| title_full | Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties |
| title_fullStr | Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties |
| title_full_unstemmed | Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties |
| title_short | Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties |
| title_sort | optimization of flight scheduling in urban air mobility considering spatiotemporal uncertainties |
| topic | urban air mobility flight plan conflict management probabilistic conflict detection critical plan identification phased optimization strategy |
| url | https://www.mdpi.com/2226-4310/12/5/413 |
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