Causal Discovery of Flight Service Process Based on Event Sequence
The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the cau...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/2869521 |
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| author | Qian Luo Lin Zhang Zhiwei Xing Huan Xia Zhao-Xin Chen |
| author_facet | Qian Luo Lin Zhang Zhiwei Xing Huan Xia Zhao-Xin Chen |
| author_sort | Qian Luo |
| collection | DOAJ |
| description | The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport. |
| format | Article |
| id | doaj-art-05e3aa992cd243a59098ad2f1ae93ef0 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-05e3aa992cd243a59098ad2f1ae93ef02025-02-03T06:05:32ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/28695212869521Causal Discovery of Flight Service Process Based on Event SequenceQian Luo0Lin Zhang1Zhiwei Xing2Huan Xia3Zhao-Xin Chen4The Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, ChinaThe Second Research Institute of Civil Aviation Administration of China, Chengdu 610041, ChinaThe development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.http://dx.doi.org/10.1155/2021/2869521 |
| spellingShingle | Qian Luo Lin Zhang Zhiwei Xing Huan Xia Zhao-Xin Chen Causal Discovery of Flight Service Process Based on Event Sequence Journal of Advanced Transportation |
| title | Causal Discovery of Flight Service Process Based on Event Sequence |
| title_full | Causal Discovery of Flight Service Process Based on Event Sequence |
| title_fullStr | Causal Discovery of Flight Service Process Based on Event Sequence |
| title_full_unstemmed | Causal Discovery of Flight Service Process Based on Event Sequence |
| title_short | Causal Discovery of Flight Service Process Based on Event Sequence |
| title_sort | causal discovery of flight service process based on event sequence |
| url | http://dx.doi.org/10.1155/2021/2869521 |
| work_keys_str_mv | AT qianluo causaldiscoveryofflightserviceprocessbasedoneventsequence AT linzhang causaldiscoveryofflightserviceprocessbasedoneventsequence AT zhiweixing causaldiscoveryofflightserviceprocessbasedoneventsequence AT huanxia causaldiscoveryofflightserviceprocessbasedoneventsequence AT zhaoxinchen causaldiscoveryofflightserviceprocessbasedoneventsequence |