基于仿真的印度机场运输系统的计算智能预测分析

IF 0.9 Q3 ENGINEERING, AEROSPACE
P. Rajarajeswari, P. Lalitha, Surya Kumari, A. S. Lakshmi, S. R. Mugunthan, Prathipati Ratna Kumar, Pranayanath Reddy Ananthula
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引用次数: 0

摘要

通常情况下,航班延误和取消对航空公司的运营和乘客满意度产生重大影响。航班延误降低了航空公司的运营绩效,对机场的准点率产生了重大影响。以前的统计模型已用于航班延误分析。本研究以印度航空业为研究对象,对国内航空公司进行了统计分析。在这篇研究论文中,我们在计算智能技术的帮助下应用机器学习模型来预测机场运输管理系统。并应用粒子群算法(PSO)和蚁群算法(ACO)等计算智能技术对延迟时间预测模型进行优化,计算最优可靠性。我们用各种方法对不同航空公司的Data Efficiency Model进行了综合分析,并使用各种机器学习模型对机场模型的预测精度进行了对比分析。在这项研究中,我们为航班延误模型的分析提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation based Predictive analysis of Indian Airport transportation system using Computational intelligence techniques
Normally, flight delays and cancellations have significant impact on airlines operations and passenger’s satisfaction. Flight delays reduce the performance of airline operations and make significant effect on airports on time performance. Previously statistical models have been used for flight delays analysis. This study was applied in Indian aviation industry and it has given statistical analysis of domestic airlines. In this research paper, we have applied Machine Learning models with the help of computational intelligence techniques for predicting airport transport management system. We have also applied computational intelligence techniques such as Particle Swarm Optimization (PSO) and Ant Colonization Optimization (ACO) to optimize the prediction model for delay period time and calculating the most optimal dependability. We have made comprehensive analysis of Data Efficiency Model for different airlines with various approaches as well as comparative analysis of accuracy for predicting airport model by using various machine learning models. In this study we have presented invaluable insights for the analysis of flight delay models.
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CiteScore
2.00
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20 weeks
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