Research on the frequency prediction of barrier-free resources in airport terminal based on ARIMA model

Qingyang Yu, Ning He, Weijia Ye, Lifei Pang
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Abstract

At present, the airport is constantly promoting the construction of barrier-free environment to assist special passengers with hearing, visual or mobility problems. More and more barrier-free service resources such as new technologies, new products and new facilities are deployed in the terminal. In order to better improve the utilization rate and deployment rationality of barrier-free service resources and enhance the barrier-free service guarantee capability of civil aviation airports, it is necessary to evaluate the current use of barrier-free service resources and predict the future use frequency. In this paper, using the mathematical model with high prediction accuracy, a kind of usage prediction method suitable for many kinds of barrier-free service resources is verified. In order to better illustrate the content of the prediction method of this article, the accessibility service resource research object is marked as ‘Facility A’. Based on the randomness and time relevance of the use of ‘Facility A’ by passengers, using python to generate random frequency time series data on the usage frequency of ’Facility A’ in the past 60 months. The serial data is as the original data for method of study in this paper, regarded as real data as well. After the analysis and processing of the original data, to build a mathematical prediction model of usage frequency based on ARIMA model, to calculate the usage prediction data based on the real data, and analyzing the deviation between the real data and the predicted data using with the mathematical theory, so as to verify the feasibility of the prediction method in this paper.
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基于ARIMA模型的机场航站楼无障碍资源频率预测研究
目前,机场正在不断推进无障碍环境的建设,以帮助有听觉、视觉或行动障碍的特殊旅客。越来越多的新技术、新产品、新设施等无障碍服务资源部署在终端。为了更好地提高无障碍服务资源的利用率和部署合理性,增强民航机场无障碍服务保障能力,有必要对当前无障碍服务资源的使用情况进行评估,并对未来的使用频率进行预测。本文利用预测精度较高的数学模型,验证了一种适用于多种无障碍服务资源的使用量预测方法。为了更好地说明本文预测方法的内容,将无障碍服务资源研究对象标记为“设施A”。基于乘客使用“A设施”的随机性和时间相关性,使用python生成过去60个月“A设施”使用频率的随机频率时间序列数据。序列数据作为本文研究方法的原始数据,也视为真实数据。在对原始数据进行分析处理后,基于ARIMA模型建立使用频率的数学预测模型,根据实际数据计算使用频率预测数据,并利用数学理论分析实际数据与预测数据之间的偏差,从而验证本文预测方法的可行性。
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