2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)最新文献_第2页

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2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/aida-at48540.2020.9049164

引用次数: 0

Applications of Machine Learning Techniques to Aviation Operations: Promises and Challenges 机器学习技术在航空运营中的应用:前景与挑战

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049205

B. Sridhar

{"title":"Applications of Machine Learning Techniques to Aviation Operations: Promises and Challenges","authors":"B. Sridhar","doi":"10.1109/AIDA-AT48540.2020.9049205","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049205","url":null,"abstract":"There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large databases. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This paper describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. Currently, the application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT are the favored approach, (b) MLT perform slightly better than methods using physics-based models and (c) comparison of different MLT to the same application. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"35 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":"124877293","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: 9

Outlier Analysis of Airport Delay Distributions in US and China 美国和中国机场延误分布的离群值分析

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049208

Max Z. Li, Karthik Gopalakrishnan, Yanjun Wang, H. Balakrishnan

{"title":"Outlier Analysis of Airport Delay Distributions in US and China","authors":"Max Z. Li, Karthik Gopalakrishnan, Yanjun Wang, H. Balakrishnan","doi":"10.1109/AIDA-AT48540.2020.9049208","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049208","url":null,"abstract":"Outlier detection is a key component of several machine learning approaches. However, many existing techniques, especially for multi-dimensional signals, are not interpretable and do not explain why a specific classification was assigned to a particular data point. Another limitation is that most methods only consider the magnitude or intensity of the signal, and not its spatial distribution. We present a spectral approach to identify outliers based on the spatial distribution of a signal across the nodes of a graph without any explicit assumptions on the underlying probability distribution of the signal. By applying these techniques to airport delays, we not only identify outliers in the spatial distribution of delays, but also gain insights into the delay dynamics. Specifically, we compare spatial delay distributions in the US and China during the period 2012–17, and identify several interesting characteristics pertaining to critical airports for outlier detection. We characterize typical variabilities in the delay distributions, and the frequency of occurrence of outliers. Our results highlight the differences between the operational dynamics of the US and Chinese air transportation systems, and contribute to performance benchmarking between different airspace systems.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"25 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":"128088096","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

Real-Time Prediction of Runway Occupancy Buffers 跑道占用缓冲区的实时预测

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049165

Lu Dai, M. Hansen

引用次数: 2

Multi-Objective Gate Assignment Problem for an Airport 机场多目标登机口分配问题

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049211

Nang Laik Ma

引用次数: 0

Airspace Capacity Overload Identification Using Collision Risk Patterns 基于碰撞风险模式的空域容量过载识别

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049182

Chunyao Ma, Qing Cai, S. Alam, V. Duong

{"title":"Airspace Capacity Overload Identification Using Collision Risk Patterns","authors":"Chunyao Ma, Qing Cai, S. Alam, V. Duong","doi":"10.1109/AIDA-AT48540.2020.9049182","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049182","url":null,"abstract":"The ever increasing demand for air travel may induce en-route airspace capacity overload which endangers flight safety and elicit air traffic congestion. Knowledge of airspace capacity overload is important for air traffic flow management and flight planning to mitigate air traffic congestion without compromising airspace safety level. Since the primary task of air traffic controllers is to manage traffic flow within the constraints imposed by safety requirements, i.e., to warrant the collision risk at a low level, in this paper, we use the aircraft mid-air collision risk for a given airspace as the indicator of airspace capacity overload. With given air traffic data and airspace configurations, the collision risk distributions inside an airspace is determined through collision risk modelling. Based on the density and intensity of collision risk, the collision risk distributions are converted into heatmaps and collision risk patterns are further recognized from the heatmaps using image processing technique. Three major states of airspace workload can be identified from theses patterns: normal state, transition state and overload state. For new traffic data during a given time period, by matching its collision risk distribution to the closest collision risk pattern, we are able to identify whether the airspace is overloaded or not. The experimental study in an en-route sector of the Singapore airspace has manifested the ability of the proposed method in collision risk pattern recognition and capacity overload identification.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"71 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":"129346719","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

Analysis of airport ground operations based on ADS-B data 基于ADS-B数据的机场地面作业分析

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049212

M. Schultz, X. Olive, J. Rosenow, H. Fricke, S. Alam

{"title":"Analysis of airport ground operations based on ADS-B data","authors":"M. Schultz, X. Olive, J. Rosenow, H. Fricke, S. Alam","doi":"10.1109/AIDA-AT48540.2020.9049212","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049212","url":null,"abstract":"Publicly available aircraft airborne and ground movement data pave the way to new advanced analyses of complex behaviours and collaborative decision making tools for the optimisation of airport operations. Such data-driven approaches will allow cost efficient implementations, which are a key enabler for the efficient integration of small/medium sized airports into the air transportation network. We present an operational milestone concept based on Automatic Dependent Surveillance - Broadcast (ADS-B) messages emitted by approaching and departing aircraft. Since aircraft have to be equipped with a compliant transponder from 2020, airports only need cheap receivers to observe operations at the runway/taxiway system and on the apron (including parking positions). These observations will allow for a systematic monitoring (using operational milestones) and predictive analytics to provide estimated values for future system states. In this contribution, we present the core elements of an innovative framework, which may bring new insights for airport operations optimisation, with a particular focus on small and medium ones. We process here aircraft movements on the ground and around Zurich airport and present four examples of applications, which will enable prediction-based decision assistance tools for efficient airport operations.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"44 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":"132402516","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: 9

A Map-Matching Algorithm for Ground Movement Trajectory Representation using A-SMGCS Data 基于A- smgcs数据的地面运动轨迹表示匹配算法

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049181

Thanh-Nam Tran, Due-Thinh Pham, S. Alam

{"title":"A Map-Matching Algorithm for Ground Movement Trajectory Representation using A-SMGCS Data","authors":"Thanh-Nam Tran, Due-Thinh Pham, S. Alam","doi":"10.1109/AIDA-AT48540.2020.9049181","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049181","url":null,"abstract":"Increasing availability of air traffic data has opened new opportunities for better understanding of Air Traffic Management (ATM) system. At Airport-Air side, A-SMGCS (Ad-vanced Surface Movement Guidance & Control System) data may provide useful insights to improve efficiency and safety of airport operations by understanding traffic patterns, taxiway usage, ground speed profiles and any anomaly behaviour. However, A-SMGCS data comes from the fusion of several sensors such as MLAT, ADS-B and SMR. This leads to high and variable noise, missing data values, and temporal and spatial misalignment. In this study, we proposed a new and simplified representation of ground movement trajectories using a map-matching algorithm applied on A-SMGCS data. The proposed approach not only overcomes above mentioned issues of data, but also takes into consideration airport specific operational constraints. The algorithm shows a good matching results with mean percentage error of approximate 8.13%. The matching trajectories and sequences of nodes in resulting graph, supports a variety of analysis about airport operations. To show the effectiveness of proposed approach, we performed some analysis such as traffic patterns, taxi-way usages, speed profiling and anomaly detection, using one month of A-SMGCS data at Singapore Changi Airport.","PeriodicalId":106277,"journal":{"name":"2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT)","volume":"72 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":"128271608","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: 4

Community Detection of Chinese Airport Delay Correlation Network 中国机场延误相关网络的社区检测

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049192

Shuwei Chen, Yanjun Wang, Minghua Hu, Ying Zhou, D. Delahaye, Siyuan Lin

{"title":"Community Detection of Chinese Airport Delay Correlation Network","authors":"Shuwei Chen, Yanjun Wang, Minghua Hu, Ying Zhou, D. Delahaye, Siyuan Lin","doi":"10.1109/AIDA-AT48540.2020.9049192","DOIUrl":"https://doi.org/10.1109/AIDA-AT48540.2020.9049192","url":null,"abstract":"Network science has been a promising tool for characterizing and understanding complex systems. A challenging problem in network science is to uncover the community structure of the network. Community structure generally presents the partition of the nodes in the network into several groups based on various structural properties or dynamic behavior. In this paper, we analyze the community structure of Chinese airport network based on Stochastic Block Models (SBM). Different from exisiting studies, the Chinese Airport Delay Correlation Network (CADCN) is constructed with airports as nodes and the correlations between hourly delay time series of airport pairs as edges. To analyze the temporal patterns of community structures, we employ spectral clustering method and classify Chinese airports into 6 different communities. Airports within each community have closer relationships to each other on the delay propagation. A similar investigation to the traditional Chinese airport network (CAN) is carried out based on SBM as well. By comparing the results of two networks, we find that the CADCN has the advantage in revealing the implicit delay correlation than the directed flights connection between airports. Our findings have potential meanings to understand the spread of flight delays and to develop relevant management and control strategies.","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":"130252184","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: 2

Pay-Per-Flight Dynamic Pricing of UAV Operations 无人机操作的按次付费动态定价

2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) Pub Date : 2020-02-01 DOI: 10.1109/AIDA-AT48540.2020.9049171

Jaime Rubio-Hervas, Abhishek Gupta, Y. Ong, M. Reyhanoglu

引用次数: 0