2020 Integrated Communications Navigation and Surveillance Conference (ICNS)最新文献

{"title":"Explicit Contingency Planning For Improved Human-Autonomy Teaming In Decision Support","authors":"C. Brinton, Alicia Borgman Fernandes, Curt Kaler","doi":"10.1109/ICNS50378.2020.9222923","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222923","url":null,"abstract":"As the National Airspace System (NAS) evolves into a more automated system, it will be essential that human operators can effectively team with their automated Decision Support Systems (DSSs) to manage the performance of the system. When automated systems recommend courses of action, the human operator must understand the operational recommendations with sufficient depth and clarity to evaluate their appropriateness and monitor the performance of the system. Significant shortcomings exist in the current state-of-the-art in Air Traffic Management (ATM) DSSs that cause human specialists to distrust the automation’s recommendations and information provided by the system.The focus of the research effort described herein is to identify methods, algorithms, and an overall framework in which ATM DSSs can reason about the appropriate contingency plans to consider in different operational scenarios and communicate the contingency plan to the human specialists to fulfill their information needs. This effort also studied approaches to automatically predict the effectiveness of contingency plans, so that the ATM DSS can determine when a given contingency is no longer the best option and a new ‘plan B’ should be considered.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116399062","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}

{"title":"Likelihood of Unmitigated Collision Risks for Uas in Defined Airspace Volumes","authors":"Brandon J. Daniel, Anuja Verma","doi":"10.1109/ICNS50378.2020.9222872","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222872","url":null,"abstract":"The capability to properly assess the risks associated with the integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) is critical to ensuring UAS operations are introduced in a safe and responsible manner. Key to this capability is the accurate determination of the likelihood of collision between UAS and other aircraft. Likelihood of such collisions are not well understood and, in many cases, are assessed in a subjective and qualitative manner, resulting in potentially inaccurate assumptions and evaluation of risk and/or allocation of limiting mitigations that decrease the efficiency and effectiveness of UAS operations. Quantification of the likelihood of these collision risk elements will transform the way UAS integration is evaluated and approvedThis paper focuses on a methodology to quantify and analyze the likelihood of unmitigated aircraft collisions within defined airspace volumes. This methodology, developed by The MITRE Corporation (MITRE), will enable a more objective, standardized, and comprehensive assessment of the collision risk posed by the integration of UAS into the NAS with the ability to statistically assess the differences between distributions of risk across multiple airspace volumes. Leveraging this methodology will speed the determination of airspace risk associated with integration and will specifically assist in the identification of mitigations to reduce identified risk to an acceptable level.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114524557","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}

{"title":"Predicting Conflict Free Trajectories Using Supervised Machine Learning, Initial Investigations","authors":"R. Christien, K. Zeghal, E. Hoffman","doi":"10.1109/ICNS50378.2020.9222959","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222959","url":null,"abstract":"This paper presents initial investigations on the prediction of conflict free aircraft trajectories using supervised machine learning. The motivation is to generate trajectory proposals to resolve conflicts based on current practices (imitation learning) as a way to get controller acceptability. The paper explores two distinct approaches. The first one takes a pilot point of view with a flight centred representation of the surrounding traffic, while the second one takes a controller point of view with a sector-based representation of the traffic. In addition, for the first approach, the traffic input is represented by an image going into a convolutional neural network, while in the second it is represented by a list of flights parameters going into a feed forward neural network.The case study addressed is to predict conflict free trajectories with a 5 minutes look-ahead. It relies on recorded traffic data from 2018 from a busy European en-route centre (Maastricht UAC) used to draw a 250k data set. This dataset was split in two 50% sub-sets: one with no change in vertical and/or horizontal dimension, the other with a change (change thresholds of 1000ft and 2NM determined statistically). The performance of both models is compared to a baseline to ensure a learning has been achieved. For the best model (sector based), the median deviations between the prediction and the true future locations are 0.4NM and 23ft \"with no change\", and 1.3NM and 500ft \"with change\". These results show that relevant information has been extracted and a mapping between inputs and outputs achieved. However, the prediction error remains quite significant compared to separation standards (5NM, 1000ft).Future work will investigate further both models, in particular analyze error (focus on bad performance cases patterns) and improving them, and later, adding other information (e.g. military areas, meteo).","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123899944","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}

{"title":"Agent-Based Simulation of Metropolitan Area Evacuation by Unmanned Air Mobility","authors":"Jonathan West, L. Sherry","doi":"10.1109/ICNS50378.2020.9222890","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222890","url":null,"abstract":"Researchers have proposed a portfolio of autonomous transportation systems for metropolitan areas including Urban Air Mobility (UAM) systems. Urban Air Mobility systems consist of low occupant battery operated helicopters, similar to drones. In a future state, when Urban Air Mobility is a ubiquitous transportation option, urban planners will need to understand the potential role of the Urban Air Mobility system for an efficient evacuation of a metropolitan area. An agent-based model is used to assess the evacuation efficiency as throughput and time to complete. The agent-based model includes autonomous Urban Air Mobility systems operating in an urban environment on routes defined by existing city streets and originating at a central location that may be on the ground or on the top of a building. In the event of an evacuation, the routing of each Urban Air Mobility unit is determined by a central air traffic flow management system to maximize the evacuation throughput. Standard deviation of time-to-complete is computing to understand where the model shows convergence. The implications of the results and limitations of the model are discussed.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134114859","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}

{"title":"Exploring A Time-Based Management Fleet Prioritization Service","authors":"Amanda Matthews, Marcus Smith, A. Staley, S. Stalnaker","doi":"10.1109/ICNS50378.2020.9222980","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222980","url":null,"abstract":"When National Airspace System (NAS) flight demand (e.g., Flight Operator operations) exceeds capacity (e.g., airport-, weather-, airspace-related) at a NAS resource the result is delay. To ensure an efficient NAS, the Federal Aviation Administration (FAA) uses various Time-Based Management (TBM) capabilities to balance capacity and demand across NAS resources. These capabilities assign the resulting delay across the resource flight demand. For example, the FAA uses the Time-Based Flow Management (TBFM) system to manage the balance between demand and capacity at arrival airports and departure fixes/flows by assigning delays across airborne and ground-based flights. TBFM is not creating delay, rather assigning delay that exists within the NAS to balance traffic demand with available capacity. TBFM uses controlled departure times to assign this delay on an as-requested approach. There is a desire among flight operators to provide priority inputs that can be accounted for by TBFM in order to minimize delay assigned to those flights that are most important to them in meeting their business objectives. Fleet Prioritization concepts, which intend to address this desire to better accommodate flight operator preferences as part of TBM, is considered consistent with achieving increased Operational Flexibility, one of four stated objectives in the FAA’s Vision for Trajectory-Based Operations (TBO).The MITRE Corporation in collaboration with the Federal Aviation Administration (FAA) is analyzing and exploring how a TBM Fleet Prioritization Service can be incorporated as part of future TBFM system capabilities. The Flight Operators’ needs for Fleet Prioritization and a concept for this was investigated, including concept elements that would be needed. Process-, procedural-, and automation-based methods were identified to achieve Fleet Prioritization goals. The scope of shortfalls related to TBFM and broader TBM operations were explored and data analysis performed to determine to what extent operational and/or business considerations necessitate the prioritization of flights to reallocate assigned delay in TBM operations. TBM assigned departure delays can vary widely for a flight; however, current operational TBM practices provide only a limited planning horizon for potential prioritization activities. The prioritization of flights requires sufficient planning time to ensure that the flight operators can meet business needs. Further data analysis highlighted that the advantages of applying increased scheduling lead-time as a means to minimize delay are location-dependent and not consistent NAS-wide. Therefore, any envisioned Fleet Prioritization service will require location-specific considerations. This paper will describe how the FAA should make both near-term and longer-term improvements to exchange data with flight operators, mature the concept, and utilize existing capabilities to improve flight operator preference accommodation.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131548449","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}

{"title":"Integrating ARIMA and Bidirectional LSTM to Predict ETA in Multi-Airport Systems","authors":"Lechen Wang, Xuechun Li, Jianfeng Mao","doi":"10.1109/ICNS50378.2020.9222874","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222874","url":null,"abstract":"Traffic states prediction in air transportation systems is a challenging problem and has not been fully explored because it is subject to many more highly correlated factors and a more complicated traffic management scheme compared to urban transportation systems. It becomes a more formidable task when facing a multi-airport system (MAS), in which several major airports are closely located and tightly coupled with each other through limited terminal airspace. In this work, we propose a novel method using a time series model and recurrent neural network to make the estimated time of arrival (ETA) for a flight to an MAS, which can be potentially utilized for flight delay prediction and congestion analysis. The experiment utilizes two months of 4D trajectories data from Beijing Capital International Airport (PEK) to Shenzhen Bao’an International airport (ZGSZ). The entire prediction work is decomposed into two sub-problems, en-route travel time prediction which is from flight origin to the entering gate of MAS, defined as the location is 300km from the airport in MAS, and terminal maneuvering area (TMA) travel time prediction which is from the entrance to flight’s destination. The auto-regressive integrated moving average (ARIMA), a time series prediction model, is used to predict travel time in en-route under given the flight departure time. Bidirectional long short term memory (LSTM), a recurrent neural network, is developed to forecast travel time in the arrival approach by utilizing spatio-temporal features. To design the input features, we use density-based spatial clustering (DBSCAN) with the help of the Voronoi diagram to extract spatial information from every historical flight trajectory of aircraft operated in an MAS, then select the observation time window to capture the temporal information for each flight. The Multivariate Stacked Fully connected-Bidirectional LSTM (MSFCB-LSTM) model is constructed to make shortterm forecasting using spatio-temporal features we designed when the flight’s entering MAS time is given. For TMA travel time prediction, a case study of Guangdong-Hong Kong-Macao Greater Bay Area (GHM-GBA), a typical MAS which contains five major airports closely located within 120km, is carried out using actual historical 4D trajectory data. Finally, Using two months 4D trajectories data, PEK to ZGSZ, the result exhibits the best accuracy, a measurement we define for prediction, of the longterm prediction of ETA given departure time is 92%, and mean absolute error (MAE) is 6.09 minutes.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131560519","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}

{"title":"Operational Evaluation of Digital Taxi Instruction","authors":"Nouri Ghazavi, Scott Masarky, Joe Monahan, Mike Copp, Shawn Sanchez, Denise David, Tritana Supamusdisukul","doi":"10.1109/ICNS50378.2020.9222996","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222996","url":null,"abstract":"Currently, the airport surface is one of the most difficult areas for a flight crew to navigate, especially at large complex airports. Taxi instructions are communicated through Ultra High Frequency / Very High Frequency (UHF/VHF) radio communications from the air traffic controller to the flight deck [1]. Frequency congestion at major airports increases difficulty conveying taxi instructions. The challenges of effective communication for ground controllers and pilots due to a single method of communication to many aircraft are clearly present in the current state. Flight crews may experience limitations to visibility and signage, or have a lack of reference to surface destinations, further complicating surface navigation. The combination of lengthy detailed taxi instructions, issuing instructions multiple times, radio frequency congestion, and unfamiliarity with the airport can result in a complex environment for the flight crew.The Federal Aviation Administration (FAA) is interested in improving clarity and delivery of taxi instructions through automation in the tower and the flight deck, focusing on Part 121 aircraft at larger airports. Current research interests will focus on developing capabilities and procedures to digitize taxi instructions on a Ground Control (GC) application and deliver the taxi instructions to the flight deck’s Electronic Flight Bag (EFB). Development of digital taxi instruction concepts and infrastructure should leverage existing National Airspace System (NAS) systems and procedures and identify gaps for further exploration. Digital taxi instructions may improve instruction clarity with minimal voice exchanges and clarifications from the GC before a common understanding is reached. Also, the flight deck will have less \"head down\" time processing taxi instructions, increasing surface situational awareness.This paper will provide initial research on the use of connected aircraft to support digital taxi instructions. The initial scope and future potential capabilities will be discussed. Identification of the functional hierarchy to realize digital taxi instruction capabilities will be reviewed. The concept has identified data elements and message sets that could be integrated into the digital taxi applications via System Wide Information Management (SWIM). Current exchange models like Flight Information Exchange Model (FIXM) should be considered for handling the message sets. Lastly, initial benefits of digital taxi instruction have been identified.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"315 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124469572","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}

{"title":"Tree-Based Airspace Capacity Estimation","authors":"Kai Zhang, Yongxin Liu, Jian Wang, Houbing Song, Dahai Liu","doi":"10.1109/ICNS50378.2020.9222986","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222986","url":null,"abstract":"Accurate estimation of airspace capacity is essential to a safe, efficient and predictable air transportation system. Conventional approaches focus on controller workload using airspace complexity measurements that only consider operational conditions of controllers. However, such model-driven methods don’t completely demonstrate airspace capacity in the real world because of lack of consideration for other critical factors such as weather. To address this challenge, we propose a new airspace capacity estimation model based on decision tree ensembles. Our model combines multi-source data to quantify the maximum transportation capacity of en route sector under different circumstances.This paper makes the following contributions: (a) we present an interpretable data-driven model that estimates the capacities of the National Airspace System (NAS), and highlight factor importance for airspace capacities; (b) the airspace capacity estimated by our proposed model is dynamically adjusted based on the real-time environment that has the potential to be a guide for temporary flight path changes or air traffic selections for an emergency landing; and (c) we promote the role of machine learning-based methods in future ATM and airspace optimization.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114611619","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}

{"title":"An Effective Hybrid Algorithm for Real-Time Optimizing Locations of Urban Mobile Stations for Luggage Check-in Service","authors":"Hang Zhou, Xiao-Bing Hu","doi":"10.1109/ICNS50378.2020.9222883","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9222883","url":null,"abstract":"In order to overcome the demerits of traditional city air terminals, a new service mode based on urban mobile stations for providing the urban luggage check-in service is proposed in this study. The station locations are dynamically allocated based on the real-time passenger distribution. Three aspects including the average distance from passengers to urban mobile stations, the maximum tolerable distance, and the maximum service capacity are considered. An effective hybrid algorithm is developed, in which the ripple-spreading algorithm is applied for solving many-to-many path optimization problems and an adaptive genetic algorithm is developed for locating stations. In a case study of Tianjin, China, the proposed method is applied to allocate the urban mobile stations. The service performance of the new mode is compared with that of the traditional city air terminals mode to show the advantages.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"11 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816281","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}

{"title":"On-Demand Mobility Cargo Demand Estimation in Northern California Region","authors":"Mihir Rimjha, Sayantan Tarafdar, N. Hinze, A. Trani, H. Swingle, Jerry C. Smith, T. Marien, S. Dollyhigh","doi":"10.1109/ICNS50378.2020.9223015","DOIUrl":"https://doi.org/10.1109/ICNS50378.2020.9223015","url":null,"abstract":"The objective of this paper is to study the potential market for electric Vertical-Takeoff-and-Landing (eVTOL) aircraft in the cargo delivery role in an urban network. Specifically, we study the potential small-package cargo operations network in the Northern California region. Recent developments in the cargo shipping industry have opened opportunities for faster modes of small-package delivery in intra-city markets. With increasing congestion of ground transportation modes and limited catchment areas, there is a potential for small-package, high-value cargo delivery using proposed eVTOL aircraft.The On-Demand Mobility (ODM) concept for cargo transportation could improve the speed and efficiency of the delivery of small packages to communities. The concept could expand the delivery services offered by traditional ground transportation modes. The concept, however, needs to offer compelling speed advantages at a reasonable cost. The objective of this study is to estimate the potential demand for ODM cargo operations in the Northern California area encompassing 17 counties. Annual cargo flows in the study area are estimated using the Transearch, Freight Analysis Framework 4, and Bureau of Transportation Statistics T-100 International datasets. A parametric analysis of market share presents the results of this study.The study presents a first-order impact analysis of ODM cargo operations on passenger ODM operations. A significant challenge in this study is the lack of specific level of detail of the shipment cost of the various databases used. Generally, private cargo companies do disclose detailed records of shipments to the public.","PeriodicalId":424869,"journal":{"name":"2020 Integrated Communications Navigation and Surveillance Conference (ICNS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123127261","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}