Research Article
Evaluation of Super Large Airport Cooperative Operation State Based on Decision Tree Support Vector Machine
@INPROCEEDINGS{10.4108/eai.2-12-2022.2327958, author={Menglu Li and Ruicong Gao and Gan Chai}, title={Evaluation of Super Large Airport Cooperative Operation State Based on Decision Tree Support Vector Machine}, proceedings={Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqing, China}, publisher={EAI}, proceedings_a={ICICA}, year={2023}, month={3}, keywords={super large airport cooperative operation evaluation index system decision tree support vector machine}, doi={10.4108/eai.2-12-2022.2327958} }- Menglu Li
Ruicong Gao
Gan Chai
Year: 2023
Evaluation of Super Large Airport Cooperative Operation State Based on Decision Tree Support Vector Machine
ICICA
EAI
DOI: 10.4108/eai.2-12-2022.2327958
Abstract
In order to accurately evaluate the real-time cooperative operation state of super large airports, the evaluation index system of cooperative operation is designed from four aspects: external distribution efficiency, internal transfer efficiency, key traffic corridor state and information interaction efficiency. Based on the established index system, a decision tree SVM cooperative operation state evaluation model is established, and the model parameters are optimized using Bayesian optimization method and 5-fold cross validation method. A case study of Beijing Capital International Airport is carried out. The results show that the established evaluation model has high accuracy and good classification performance, and can solve the problem of cooperative operation state evaluation of high-dimensional and nonlinear large-scale airport systems. The proposed evaluation method reduces the impact of subjective factors on the reliability of the evaluation results, and is conducive to the objective evaluation of the real-time cooperative operation of airports and the optimal scheduling of transport capacity resources.