Using ADS-B big data pattern analysis to improve the quality of multivariate 4D trajectory optimization strategies

利用ADS-B大数据模式分析提高多元4D轨迹优化策略的质量

• 批准号: 410540389
• 负责人: Professor Dr.-Ing. Hartmut Fricke
• 金额: --
• 依托单位: Institut für Luftfahrt und Logistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/410540389?language=en
• 关键词: Using; ADS; big; data; pattern

A highly efficient and safe air transport system relies on individually and system-wide optimized aircraft trajectories, where optimization functions typically focus on fuel, flight time minimization, transport quality, and environmental impact as major market drivers, or a combination thereof. Given these multiple criteria and the large-scale of the air transportation system, the trajectory building process for modern, efficient air transportation is highly computationally expensive, as its optimization relies on accurate aerodynamic and engine characteristics to be reflected in aircraft performance models. Moreover, environmental uncertainty sources should be statistically taken into consideration in the trajectory building process searching for individually optimized altitude, path and time conditions.This project aims to identify the benefits of using big data analysis and data driven methods for efficient trajectory and network design. So far used trajectory optimization tools are mostly analytically driven, model/search-based, and largely neglect historical trajectories as background. The rise of performance mass data gathering and storage capabilities, data mining over historical flight data is a novel candidate for robust and fast optimization. Given ICAO’s mandate on aircraft ADS-B (Automatic Dependent Surveillance - Broadcast) equipment installation by 2020, an aircraft tracking surveillance technology, increasing numbers of commercial aircraft are now being equipped with this capability. Throughout this project, we aim to better understand the suitability of applying big data-driven techniques for predicting single (local optimum) but also multiple (global optimum) aircraft trajectories. The biggest challenge in this approach is how to process these large quantities of data, more than 200,000 messages per second over a period of more than one year. Based on our initial joint research on compressed storage and indexing techniques, we intend to first search for efficient coupling of both partners’ strengths for single flight aircraft trajectory augmentation based on historic ADS-B track data in large entities and aircraft performance models for specific aircraft-engine combinations, then extending the optimization goal towards flow oriented objective functions reflecting particularly on small and large airline networks. Validation of the claimed model will be performed along significantly different styles and behavior in the Chinese and European airspace structure and market strategies guiding both airline network structures differently. Based on our research outcome, it will be possible to turn historical knowledge into scenario-based recommendations, which yield unprecedented accuracy and efficiency for the data-driven, goal-oriented design and optimization of aircraft trajectories.

高效和安全的航空运输系统依赖于单独和全系统优化的飞机轨迹，其中优化功能通常集中在燃料、飞行时间最小化、运输质量和环境影响作为主要市场驱动因素或其组合。考虑到这些多个标准和航空运输系统的大规模，用于现代高效航空运输的轨迹构建过程在计算上是非常昂贵的，因为其优化依赖于要在飞机性能模型中反映的准确的空气动力学和发动机特性。此外，在轨迹构建过程中，应在统计学上考虑环境不确定性因素，以寻求单独优化的高度、路径和时间条件。本项目旨在确定使用大数据分析和数据驱动方法进行高效轨迹和网络设计的好处。到目前为止，使用的轨迹优化工具大多是分析驱动的，基于模型/搜索的，并且在很大程度上忽略了历史轨迹作为背景。随着海量数据收集和存储能力的提高，对历史飞行数据的数据挖掘是一种鲁棒和快速优化的新候选。鉴于国际民航组织的任务是在2020年前安装飞机ADS-B（广播式自动相关监视）设备，这是一种飞机跟踪监视技术，越来越多的商用飞机现在正在配备这种能力。在整个项目中，我们的目标是更好地了解应用大数据驱动技术预测单个（局部最优）和多个（全局最优）飞机轨迹的适用性。这种方法面临的最大挑战是如何处理这些大量的数据，在一年多的时间里每秒处理20多万条消息。基于我们对压缩存储和索引技术的初步联合研究，我们打算首先根据大型实体中的历史ADS-B跟踪数据和特定飞机发动机组合的飞机性能模型，然后将优化目标扩展到特别反映小型和大型航线网络的面向流量的目标函数。将沿着中国和欧洲空域结构的显著不同风格和行为以及指导两个航空公司网络结构的不同市场战略对所要求的模型进行验证。基于我们的研究成果，将有可能将历史知识转化为基于数据的建议，从而为数据驱动的、以目标为导向的设计和飞机轨迹优化提供前所未有的准确性和效率。