Purpose

Artificial intelligence (AI)-based systems can save the lives of many people by assessing the safety of flight paths. Unfortunately, the world witnessed a horrible event in January 2020 with the case of flight 752 of Ukrainian International Airlines from Tehran to Kiev and it has prompted us to ask how AI can prevent such events by warning to flight path planners. This paper aims to propose a framework for assessing the safety of flight paths from a shooting of an airplane by air defense systems installed on the path. Unlike the existing studies, this study takes a new look at pre-flight risk assessment by using textual information in social and news networks. To this end, the authors use existing information retrieval techniques to identify high flight risk areas by examining the news articles, comments, posts, tweets, etc., in social media and then estimate the probability of targeting a passenger aircraft by the air defense systems probably installed on high-risk areas with the help of a statistical model. This estimation can then be used by fight planners to avoid such events.

Design/methodology/approach

To design a framework for estimating the probability of a fatal shooting of an airplane by air defense systems installed on its flight path, the authors have used the idea of information retrieval in conjunction with statistical methods. The authors have extracted some significant variables in the shooting of flights and proposed an AI-based framework to estimate the probability of a fatal shooting of an airplane during its flight and sketched a case study for using machine learning approaches to assist with flight path planning. As a case study, the authors covered flight 752 to explain the usefulness of the proposed framework in this context.

Findings

Unlike the existing methods, this study investigates flight path safety assessment from the social media and crowdsourcing perspective. In this study, the authors proposed an AI-based framework to avoid aviation hazards by estimating the probability of a shooting of an airplane by air defense systems installed on its flight path. Moreover, this study was designed to show how estimating the safety of flight paths by using AI-based methods can help flight planners to avoid such events and gain further insights into the use of AI-based systems in pre-flight risk assessment.

Originality/value

The idea behind the proposed method is original and as the authors’ best knowledge, there is no similar framework using social media for flight path safety assessment.

中文翻译：

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使用社交媒体进行飞行路径安全评估

目的

基于人工智能 (AI) 的系统可以通过评估飞行路径的安全性来挽救许多人的生命。不幸的是，世界在 2020 年 1 月见证了乌克兰国际航空公司从德黑兰飞往基辅的 752 航班的可怕事件，这促使我们询问人工智能如何通过向飞行路线规划者发出警告来防止此类事件发生。本文旨在提出一个框架，用于评估安装在路径上的防空系统射击飞机时飞行路径的安全性。与现有研究不同，本研究通过使用社交和新闻网络中的文本信息对飞行前风险评估进行了新的审视。为此，作者使用现有的信息检索技术通过检查新闻文章、评论、帖子、推文等来识别高风险区域，在社交媒体中，然后借助统计模型估计可能安装在高风险地区的防空系统瞄准客机的概率。然后战斗计划人员可以使用这种估计来避免此类事件。

设计/方法/方法

为了设计一个框架来估计安装在其飞行路径上的防空系统对飞机进行致命射击的概率，作者将信息检索的想法与统计方法结合使用。作者提取了飞行射击中的一些重要变量，并提出了一个基于 AI 的框架来估计飞机在飞行过程中发生致命射击的概率，并绘制了一个使用机器学习方法辅助飞行路径规划的案例研究。作为案例研究，作者介绍了 752 航班，以解释提议的框架在这​​种情况下的用处。

发现

与现有方法不同，本研究从社交媒体和众包的角度研究飞行路径安全评估。在这项研究中，作者提出了一个基于人工智能的框架，通过估计安装在飞机飞行路径上的防空系统射击飞机的概率来避免航空危险。此外，本研究旨在展示如何使用基于人工智能的方法评估飞行路径的安全性，如何帮助飞行规划者避免此类事件，并进一步了解在飞行前风险评估中使用基于人工智能的系统。

原创性/价值

所提出的方法背后的想法是原创的，并且作为作者的最佳知识，没有使用社交媒体进行飞行路径安全评估的类似框架。