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The aircraft scheduling problem consists in sequencing aircraft on airport runways and in scheduling their times of operations taking into consideration several operational constraints. It is known to be an NP-hard problem, an ongoing challenge for both researchers and air traffic controllers. The aim of this paper is to present a focused review on the most relevant techniques in the recent literature (since 2010) on the aircraft runway scheduling problem, including exact approaches such as mixed-integer programming and dynamic programming, metaheuristics, and novel approaches based on reinforcement learning. Since the benchmark instances used in the literature are easily solved by high-performance computers and current versions of solvers, we propose a new data set with challenging realistic problems constructed from real-world air traffic.
Sana Ikli; Catherine Mancel; Marcel Mongeau; Xavier Olive; Emmanuel Rachelson. The aircraft runway scheduling problem: A survey. Computers & Operations Research 2021, 132, 105336 .
AMA StyleSana Ikli, Catherine Mancel, Marcel Mongeau, Xavier Olive, Emmanuel Rachelson. The aircraft runway scheduling problem: A survey. Computers & Operations Research. 2021; 132 ():105336.
Chicago/Turabian StyleSana Ikli; Catherine Mancel; Marcel Mongeau; Xavier Olive; Emmanuel Rachelson. 2021. "The aircraft runway scheduling problem: A survey." Computers & Operations Research 132, no. : 105336.
Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.
Luis Basora; Paloma Bry; Xavier Olive; Floris Freeman. Aircraft Fleet Health Monitoring with Anomaly Detection Techniques. Aerospace 2021, 8, 103 .
AMA StyleLuis Basora, Paloma Bry, Xavier Olive, Floris Freeman. Aircraft Fleet Health Monitoring with Anomaly Detection Techniques. Aerospace. 2021; 8 (4):103.
Chicago/Turabian StyleLuis Basora; Paloma Bry; Xavier Olive; Floris Freeman. 2021. "Aircraft Fleet Health Monitoring with Anomaly Detection Techniques." Aerospace 8, no. 4: 103.
A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service providers (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smoothen daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.
Xavier Olive; Luis Basora. Detection and identification of significant events in historical aircraft trajectory data. Transportation Research Part C: Emerging Technologies 2020, 119, 102737 .
AMA StyleXavier Olive, Luis Basora. Detection and identification of significant events in historical aircraft trajectory data. Transportation Research Part C: Emerging Technologies. 2020; 119 ():102737.
Chicago/Turabian StyleXavier Olive; Luis Basora. 2020. "Detection and identification of significant events in historical aircraft trajectory data." Transportation Research Part C: Emerging Technologies 119, no. : 102737.
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.
Michael Schultz; Xavier Olive; Judith Rosenow; Hartmut Fricke; Sameer Alam. Analysis of airport ground operations based on ADS-B data. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) 2020, 1 -9.
AMA StyleMichael Schultz, Xavier Olive, Judith Rosenow, Hartmut Fricke, Sameer Alam. Analysis of airport ground operations based on ADS-B data. 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). 2020; ():1-9.
Chicago/Turabian StyleMichael Schultz; Xavier Olive; Judith Rosenow; Hartmut Fricke; Sameer Alam. 2020. "Analysis of airport ground operations based on ADS-B data." 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT) , no. : 1-9.
The large amount of aircraft trajectory data publicly available through open data sources like the OpenSky Network presents a wide range of possibilities for monitoring and post-operational analysis of air traffic performance. This contribution addresses the automatic identification of operational events associated with trajectories. This is a challenging task that can be tackled with both empirical, rule-based methods and statistical, data-driven approaches. In this paper, we first propose a taxonomy of significant events, including usual operations such as take-off, Instrument Landing System (ILS) landing and holding, as well as less usual operations like firefighting, in-flight refuelling and navigational calibration. Then, we introduce different rule-based and statistical methods for detecting a selection of these events. The goal is to compare candidate methods and to determine which of the approaches performs better in each situation.
Xavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings 2020, 59, 8 .
AMA StyleXavier Olive, Junzi Sun, Adrien Lafage, Luis Basora. Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches. Proceedings. 2020; 59 (1):8.
Chicago/Turabian StyleXavier Olive; Junzi Sun; Adrien Lafage; Luis Basora. 2020. "Detecting Events in Aircraft Trajectories: Rule-Based and Data-Driven Approaches." Proceedings 59, no. 1: 8.
Mode S surveillance allows air traffic controllers to interrogate certain information from aircraft, such as airspeeds, turn parameters, target altitudes, and meteorological conditions. However, not all aircraft have enabled the same capabilities. Before performing any specific interrogation, the surveillance radar must acquire the transponder capabilities of an aircraft. This is obtained via the common usage Ground-initiated Comm-B (GICB) capabilities report (BDS 1,7). With this report, third-party researchers can further improve the identification accuracy of different Mode S Comm-B message types, as well as study the compliance of surveillance standards. Thanks to the OpenSky network’s large-scale global coverage, a full picture of current Mode S capabilities over the world can be constructed. In this paper, using the OpenSky Impala data interface, we first sample over one month of raw BDS 1,7 messages from around the world. Around 40 million messages are obtained. We then decode and analyze the GICB capability messages. The resulting data contain Comm-B capabilities for all aircraft available to OpenSky during this month. The analyses in this paper focus on exploring statistics of GICB capabilities among all aircraft and within each aircraft type. The resulting GICB capability database is shared as an open dataset.
Junzi Sun; Huy Vû; Xavier Olive; Jacco M. Hoekstra. Mode S Transponder Comm-B Capabilities in Current Operational Aircraft. Proceedings 2020, 59, 4 .
AMA StyleJunzi Sun, Huy Vû, Xavier Olive, Jacco M. Hoekstra. Mode S Transponder Comm-B Capabilities in Current Operational Aircraft. Proceedings. 2020; 59 (1):4.
Chicago/Turabian StyleJunzi Sun; Huy Vû; Xavier Olive; Jacco M. Hoekstra. 2020. "Mode S Transponder Comm-B Capabilities in Current Operational Aircraft." Proceedings 59, no. 1: 4.
Problems tackled by researchers and data scientists in aviation and air traffic management (ATM) require manipulating large amounts of data representing trajectories, flight parameters and geographical descriptions of the airspace they fly through. The traffic library for the Python programming language defines an interface to usual processing and data analysis methods to be applied on aircraft trajectories and airspaces. This paper presents how traffic accesses different sources of data, leverages processing methods to clean, filter, clip or resample trajectories, and compares trajectory clustering methods on a sample dataset of trajectories above Switzerland.
Xavier Olive; Luis Basora. A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering. EPiC Series in Computing 2019, 67, 73 -84.
AMA StyleXavier Olive, Luis Basora. A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering. EPiC Series in Computing. 2019; 67 ():73-84.
Chicago/Turabian StyleXavier Olive; Luis Basora. 2019. "A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering." EPiC Series in Computing 67, no. : 73-84.
Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.
Luis Basora; Xavier Olive; Thomas Dubot. Recent Advances in Anomaly Detection Methods Applied to Aviation. Aerospace 2019, 6, 117 .
AMA StyleLuis Basora, Xavier Olive, Thomas Dubot. Recent Advances in Anomaly Detection Methods Applied to Aviation. Aerospace. 2019; 6 (11):117.
Chicago/Turabian StyleLuis Basora; Xavier Olive; Thomas Dubot. 2019. "Recent Advances in Anomaly Detection Methods Applied to Aviation." Aerospace 6, no. 11: 117.
Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.
Luis Basora; Xavier Olive; Thomas Dubot. Recent Advances in Anomaly Detection Methods applied to Aviation. 2019, 1 .
AMA StyleLuis Basora, Xavier Olive, Thomas Dubot. Recent Advances in Anomaly Detection Methods applied to Aviation. . 2019; ():1.
Chicago/Turabian StyleLuis Basora; Xavier Olive; Thomas Dubot. 2019. "Recent Advances in Anomaly Detection Methods applied to Aviation." , no. : 1.
Collision avoidance is one of the most crucial applications with regards to the safety of the global airspace. The introduction of mandatory airborne collision avoidance systems has significantly reduced the likelihood of mid-air collisions despite the increase in air traffic density. In this paper, we analyze 250 billion aircraft transponder messages received from 126,700 aircraft by the OpenSky Network over a two-week period. We use this data to quantify equipage and usage aspects of Traffic Alert and Collision Avoidance System (TCAS) as it is working in the real world. We furthermore provide an overview of the methods used by OpenSky to collect, decode and store this data for use by other researchers and aviation authorities. We observe that around 89.5% of the ADS-B-equipped aircraft have an operational TCAS. We further analyze the concrete usage of TCAS by examining several case studies where a loss of separation between aircraft has happened.
Matthias Schafer; Xavier Olive; Martin Strohmeier; Matthew Smith; Ivan Martinovic; Vincent Lenders. OpenSky Report 2019: Analysing TCAS in the Real World using Big Data. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) 2019, 1 -9.
AMA StyleMatthias Schafer, Xavier Olive, Martin Strohmeier, Matthew Smith, Ivan Martinovic, Vincent Lenders. OpenSky Report 2019: Analysing TCAS in the Real World using Big Data. 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC). 2019; ():1-9.
Chicago/Turabian StyleMatthias Schafer; Xavier Olive; Martin Strohmeier; Matthew Smith; Ivan Martinovic; Vincent Lenders. 2019. "OpenSky Report 2019: Analysing TCAS in the Real World using Big Data." 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC) , no. : 1-9.
Xavier Olive. traffic, a toolbox for processing and analysing air traffic data. Journal of Open Source Software 2019, 4, 1 .
AMA StyleXavier Olive. traffic, a toolbox for processing and analysing air traffic data. Journal of Open Source Software. 2019; 4 (39):1.
Chicago/Turabian StyleXavier Olive. 2019. "traffic, a toolbox for processing and analysing air traffic data." Journal of Open Source Software 4, no. 39: 1.
A way to assess rare aircraft incidents (e.g., runway excursion) is to identify contributing factors (e.g., late braking, long landing, inappropriate flare, unstable approach) and to build a dependency tree (e.g., long landing may be the result of an unstable approach not followed by a go around) that describes the causality between these factors. Probabilities are then fed into such models in order to evaluate the assessed risk. When estimating such probabilities, many sources can be of interest. Airlines have access to the comprehensive flight data records of their fleet; manufacturers push to collect data for the aircraft they build; air traffic control log radar tracks. Albeit not as complete as other flight data records, Mode S data is very attractive, esp. for academics, as the data is open, may be published without obfuscation and offers reproducible results to the community. Mode S also provides an indiscriminate source of information (not limited to an airline or to an aircraft type) that is of great help for putting in context flights matching unusual patterns. We propose to discuss the advantages and limitations of an analysis based only on Mode S data with a case study around the runway excursion risk assessment.
Xavier Olive; Pierre Bieber. Quantitative Assessments of Runway Excursion Precursors using Mode S data. 2019, 1 .
AMA StyleXavier Olive, Pierre Bieber. Quantitative Assessments of Runway Excursion Precursors using Mode S data. . 2019; ():1.
Chicago/Turabian StyleXavier Olive; Pierre Bieber. 2019. "Quantitative Assessments of Runway Excursion Precursors using Mode S data." , no. : 1.
We present a new approach to separate air traffic trajectories in an area constrained by operational procedures. This technique is applied on a set of real trajectories in Toulouse terminal manoeuvring area (TMA). The resulting clusters foster good understanding of the structure of traffic and of how controllers schedule landings at Toulouse–Blagnac airport; on the other hand, a group of peculiar trajectories emerge with useful information calling for further analysis and paving the way for a probabilistic approach to risk assessment in air traffic safety.
Xavier Olive; Jérôme Morio. Trajectory clustering of air traffic flows around airports. Aerospace Science and Technology 2018, 84, 776 -781.
AMA StyleXavier Olive, Jérôme Morio. Trajectory clustering of air traffic flows around airports. Aerospace Science and Technology. 2018; 84 ():776-781.
Chicago/Turabian StyleXavier Olive; Jérôme Morio. 2018. "Trajectory clustering of air traffic flows around airports." Aerospace Science and Technology 84, no. : 776-781.
The increasing number of space debris in Low-Earth Orbit (LEO) raises the question of future Active Debris Removal (ADR) operations. Typical ADR scenarios rely on an Orbital Transfer Vehicle (OTV) using one of the two following disposal strategies: the first one consists in attaching a deorbiting kit, such as a solid rocket booster, to the debris after rendezvous; with the second one, the OTV captures the debris and moves it to a low-perigee disposal orbit. For multiple-target ADR scenarios, the design of such a mission is very complex, as it involves two optimization levels: one for the space debris sequence, and a second one for the “elementary” orbit transfer strategy from a released debris to the next one in the sequence. This problem can be seen as a Time-Dependant Traveling Salesman Problem (TDTSP) with two objective functions to minimize: the total mission duration and the total propellant consumption. In order to efficiently solve this problem, ONERA has designed, under CNES contract, TOPAS (Tool for Optimal Planning of ADR Sequence), a tool that implements a Branch & Bound method developed in previous work together with a dedicated algorithm for optimizing the “elementary” orbit transfer. A single run of this tool yields an estimation of the Pareto front of the problem, which exhibits the trade-off between mission duration and propellant consumption. We first detail our solution to cope with the combinatorial explosion of complex ADR scenarios with 10 debris. The key point of this approach is to define the orbit transfer strategy through a small set of parameters, allowing an acceptable compromise between the quality of the optimum solution and the calculation cost. Then we present optimization results obtained for various 10 debris removal scenarios involving a 15-ton OTV, using either the deorbiting kit or the disposal orbit strategy. We show that the advantage of one strategy upon the other depends on the propellant margin, the maximum duration allowed for the mission and the orbit inclination domain. For high inclination orbits near 98 deg, the disposal orbit strategy is more appropriate for short duration missions, while the deorbiting kit strategy ensures a better propellant margin. Conversely, for lower inclination orbits near 65 deg, the deorbiting kit strategy appears to be the only possible with a 10 debris set. We eventually explain the consistency of these results with regards to astrodynamics.
Nicolas Bérend; Xavier Olive. Bi-objective optimization of a multiple-target active debris removal mission. Acta Astronautica 2016, 122, 324 -335.
AMA StyleNicolas Bérend, Xavier Olive. Bi-objective optimization of a multiple-target active debris removal mission. Acta Astronautica. 2016; 122 ():324-335.
Chicago/Turabian StyleNicolas Bérend; Xavier Olive. 2016. "Bi-objective optimization of a multiple-target active debris removal mission." Acta Astronautica 122, no. : 324-335.
Xavier Olive; Hiroshi Nakashima. Efficient Representation of Constraints and Propagation of Variable-Value Symmetries in Distributed Constraint Reasoning. Journal of Information Processing 2011, 19, 201 -210.
AMA StyleXavier Olive, Hiroshi Nakashima. Efficient Representation of Constraints and Propagation of Variable-Value Symmetries in Distributed Constraint Reasoning. Journal of Information Processing. 2011; 19 ():201-210.
Chicago/Turabian StyleXavier Olive; Hiroshi Nakashima. 2011. "Efficient Representation of Constraints and Propagation of Variable-Value Symmetries in Distributed Constraint Reasoning." Journal of Information Processing 19, no. : 201-210.