2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)最新文献

Door-to-Door Air Travel Time Analysis in the United States using Uber Data 使用Uber数据的美国门到门航空旅行时间分析

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049179

P. Monmousseau, D. Delahaye, A. Marzuoli, E. Feron

引用次数: 1

A Distributed Metaheuristic Approach for Complexity Reduction in Air Traffic for Strategic 4D Trajectory Optimization 空中交通策略四维轨迹优化复杂性降低的分布式元启发式方法

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049200

Paveen Juntama, S. Chaimatanan, S. Alam, D. Delahaye

{"title":"A Distributed Metaheuristic Approach for Complexity Reduction in Air Traffic for Strategic 4D Trajectory Optimization","authors":"Paveen Juntama, S. Chaimatanan, S. Alam, D. Delahaye","doi":"10.1109/AIDA-AT48540.2020.9049200","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049200","url":null,"abstract":"This paper presents a new challenge on the strategic 4D trajectory optimization problem with the evaluation of air traffic complexity by using the geometric-based intrinsic complexity measure called König metric. The demonstration of König metric shows the potential that the algorithm can capture the disorganized the disorganized traffic which represents the difficulty of maintaining situational awareness as expected by the air traffic controller. We reformulate the optimization problem with two trajectory separation approaches including delaying flight departure time and allocating the new flight level subject to limited delay time of departure, limited changes of flight levels and fuel consumption constraints. We propose our solution to solve daily traffic demands in the regional French airspace. The resolution process uses the distributed metaheuristic algorithm to optimize aircraft trajectories in 4D environment with the objective of finding the optimal air traffic complexity. The experimental results shows the reduction of maximum complexity more than 95 % with average delay of 2.69 minutes. The optimized trajectories can save fuel more than 80000 kg. The proposed algorithm not only reduces the air traffic complexity but also maintain its distribution in traffic. The research results represent further steps towards taking other trajectory separations methods and aircraft trajectory uncertainties into account, developing our approach at the continental scale as well as adapting it in the pre-tactical and tactical planning phase.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"16 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113934552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Predicting Passenger Flow at Charles De Gaulle Airport Security Checkpoints 预测戴高乐机场安全检查站的客流

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049190

P. Monmousseau, Gabriel Jarry, Florian Bertosio, D. Delahaye, M. Houalla

引用次数: 2

Real-time Unstable Approach Detection Using Sparse Variational Gaussian Process 基于稀疏变分高斯过程的实时不稳定方法检测

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049174

N. P. Singh, S. Goh, S. Alam

{"title":"Real-time Unstable Approach Detection Using Sparse Variational Gaussian Process","authors":"N. P. Singh, S. Goh, S. Alam","doi":"10.1109/AIDA-AT48540.2020.9049174","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049174","url":null,"abstract":"Worldwide, Air Navigation Service Providers (ANSP) are striving to exceed the desired safety levels. The Terminal Manoeuvre Area (TMA) is one of the most safety-critical areas in ATM as it encompasses the most critical phase of flight, i.e., departure and landing. An aircraft, during the final approach phase, is required to remain in a stable configuration and prevent any undesired state such as an unstable approach, which may subsequently lead to incidents/accidents such as Go-Around, Runway Excursions, etc. In this paper, we propose a data-driven framework to model the aircraft 4D trajectories in the final approach phase by adopting sparse variational Gaussian process (SVGP) model. The model is trained to learn the aircraft landing dynamics from Advanced Surface Movement Guidance and Control System (A-SMGCS) data, during the final approach phase. We experimentally demonstrate that SVGP provides an interpretable probabilistic bound of aircraft parameters that can quantify deviation and perform real-time anomaly detection. The findings of this work can increase situational awareness of the air traffic controller and has implications for the design of a new approach procedure in complex runway configurations such as parallel approach.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Approach and Landing Aircraft On-Board Parameters Estimation with LSTM Networks 基于LSTM网络的进近降落机机载参数估计

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049199

Gabriel Jarry, D. Delahaye, E. Feron

引用次数: 6

Identifying Interesting Moments in Controllers Work Video via Dimensionality Reduction 通过降维识别控制器工作视频中的有趣时刻

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049170

Kristofer Krus, Tatiana Polishchuk, V. Polishchuk

引用次数: 1

Ant Colony Systems for Optimizing Sequences of Airspace Partitions 空域划分序列优化的蚁群系统

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049206

D. Gianazza, N. Durand

引用次数: 1

A Traffic Method for Unmanned Aircraft Systems on a Virtual Closed Circuit 基于虚拟闭环的无人机系统交通方法

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049166

Gregoire Ky, S. Alam, V. Duong

引用次数: 2

Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections 基于学习滑行道交叉口时空利用的动态热点预测

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049186

Hasnain Ali, Raphael Delair, D. Pham, S. Alam, M. Schultz

{"title":"Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections","authors":"Hasnain Ali, Raphael Delair, D. Pham, S. Alam, M. Schultz","doi":"10.1109/AIDA-AT48540.2020.9049186","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049186","url":null,"abstract":"Airports across the world are expanding by building multiple ground control towers and resorting to complex taxiway and runway system, in response to growing air traffic. Current outcome- based ground safety management at the airside may impede our potential to learn from and adapt to evolving air traffic scenarios, owing to the sparsity of accidents when compared with number of daily airside operations. To augment airside ground safety at Singapore Changi airport, in this study, we predict dynamic hot spots- areas where multiple aircraft may come in close vicinity on taxiways, as pre-cursor events to airside conflicts. We use airside infrastructure and A-SMGCS operations data of Changi airport to model aircraft arrival at different taxiway intersections both in temporal and spatial dimensions. The statistically learnt spatial-temporal model is then used to compute conflict probability at identified intersections, in order to evaluate conflict coefficients or hotness values of hot spots. These hot spots are then visually displayed on the aerodrome diagram for heightened attention of ground ATCOs. In the Subjective opinion of Ground Movement Air Traffic Controller, highlighted Hot Spots make sense and leads to better understanding of taxiway movements and increased situational awareness. Future research shall incorporate detailed human-in-the-loop validation of the dynamic hot spot model by ATCOs in 360 degree tower simulator.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134193863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Predictive Joint Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction 提高飞机爬升预测的质量和速度剖面预测联合分布

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049196

Richard Alligier

{"title":"Predictive Joint Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction","authors":"Richard Alligier","doi":"10.1109/AIDA-AT48540.2020.9049196","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049196","url":null,"abstract":"Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. Focusing on the climb phase, we predict some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This speed intent is parameterized by three values (cas1, cas2, $M$). These missing parameters might be useful to predict the future trajectory of a climbing aircraft. In this work, an ensemble of neural networks uses the observed past trajectory of the considered aircraft as input and predicts a Gaussian Mixture Model (GMM) modeling the joint distribution of (mass, cas1, cas2, $M$). Ideally, this predicted distribution will be close to a conditional distribution: the distribution of possible (mass, cas1, cas2, $M$) values given the observed past trajectory of the considered aircraft. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The obtained data set contains millions of climbing segments from all over the world. Using this data, we show that using the proposed predictive model instead of a regression model brings almost as much information as using a regression model instead of a simple mean. The data set and the machine learning code are publicly available.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129049369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1