A Decision-Making Model Using Machine Learning for Improving Dispatching Efficiency in Chengdu Shuangliu Airport. (16th December 2020)

• Record Type: Journal Article
• Authors: Qian, Yingmiao; Chen, Shuhang; Li, Jianchang; Ren, Qinxin; Zhu, Jinfu; Yuan, Ruijia; Su, Hao
• Other Names: Uddin M. Irfan Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-12-16
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/6626937
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : 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.