A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport
Due to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6626937 |
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| author | Yingmiao Qian Shuhang Chen Jianchang Li Qinxin Ren Jinfu Zhu Ruijia Yuan Hao Su |
| author_facet | Yingmiao Qian Shuhang Chen Jianchang Li Qinxin Ren Jinfu Zhu Ruijia Yuan Hao Su |
| author_sort | Yingmiao Qian |
| collection | DOAJ |
| description | Due to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of optimizing the allocation of taxi resource, this paper is based on the cost-benefit analysis method to determine the factors that affect the taxi driver’s decision-making. The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. A conclusion has been drawn that the allocation of airport taxi resource should be arranged closely related to drivers’ revenue and the layout of airport line. |
| format | Article |
| id | doaj-art-72438d1388c1449eae278f1ce0ef007b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-72438d1388c1449eae278f1ce0ef007b2025-02-03T00:58:52ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66269376626937A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu AirportYingmiao Qian0Shuhang Chen1Jianchang Li2Qinxin Ren3Jinfu Zhu4Ruijia Yuan5Hao Su6School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, ChinaDesign School, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, ChinaSchool of Science, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, ChinaSchool of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, ChinaDesign School, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, ChinaSchool of Civil Engineering, Central South University, Changsha 410075, ChinaSchool of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, ChinaDue to the increasing number of people traveling by air, the passenger flow at the airport is increasing, and the problem of passenger drop-off and pickup has a huge impact on urban traffic. The difficulty of taking a taxi at the airport is still a hot issue in the society. Aiming at the problem of optimizing the allocation of taxi resource, this paper is based on the cost-benefit analysis method to determine the factors that affect the taxi driver’s decision-making. The mathematical methods such as function equation, BP neural network algorithm, and queuing theory were used to establish a complete decision-making model for taxi drivers and an optimization model of dispatching efficiency at the airport. A conclusion has been drawn that the allocation of airport taxi resource should be arranged closely related to drivers’ revenue and the layout of airport line.http://dx.doi.org/10.1155/2020/6626937 |
| spellingShingle | Yingmiao Qian Shuhang Chen Jianchang Li Qinxin Ren Jinfu Zhu Ruijia Yuan Hao Su A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport Complexity |
| title | A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport |
| title_full | A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport |
| title_fullStr | A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport |
| title_full_unstemmed | A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport |
| title_short | A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport |
| title_sort | decision making model using machine learning for improving dispatching efficiency in chengdu shuangliu airport |
| url | http://dx.doi.org/10.1155/2020/6626937 |
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