Airspace Operations Research Articles

302 Sleep, Sleepiness, and Performance Across Three In-Flight Bunk Rest Opportunities

Abstract Introduction Airline pilots are required to take a rest break in a bunk during long-haul flights in an effort to reduce sleepiness during critical phases of flight. It is unclear, however, whether each rest break affords equal opportunity for sleep. We aimed to characterize sleep, sleepiness, and performance outcomes across three in-flight rest breaks during long-haul flights. Methods Thirty-seven pilots wore actiwatches and completed sleep diaries for approximately two weeks while flying a variety of long-haul routes (n=126 flights). Self-reported in-flight bunk rest (BR) periods were used to set rest intervals and sleep was estimated within these intervals using actigraphy software (wake threshold set to medium). Pilots provided Karolinska Sleepiness Scale ratings (KSS) and performed a 5-minute psychomotor vigilance task (PVT) before landing. A linear mixed-effects model with participant included as a random effect and allowed to vary by intercept was used to assess differences between BR opportunities. Results The majority (97%, n=122) of BR periods contained sleep (as estimated by actigraphy). The mean (+/- standard deviation) sleep duration for the first, middle, and third BR opportunity was 152.8 (69.7), 149.2 (44.1), 125.2 (44.9) minutes, respectively. There was a significant effect of BR opportunity for sleep duration (F(2,54) = 3.747, p=.03) and KSS (F(2,44) = 7.869, p=.001). Bonferroni adjusted planned pairwise contrasts revealed that pilots using the third BR obtained significantly less sleep than in the first BR (p=.029). KSS ratings prior to landing were higher for the third BR compared to both the first (p=.001) and middle BR (p=.017). There were no significant differences for PVT speed or lapses (all p>05). Conclusion These results suggest that the last rest break is associated with shorter sleep, lower alertness, and no differences in performance relative to the other rest breaks. Further analysis is required to determine whether the higher KSS ratings following the third rest break are associated with sleep inertia, or whether other factors may be involved. Support (if any) NASA Airspace Operations and Safety Program, System-Wide Safety Project

313 Sleep Obtained by Cabin Crewmembers During a Long-haul Flight

Abstract Introduction Sleep loss and circadian disruption pose a significant risk in safety-sensitive occupations. In aviation, many studies have demonstrated how inflight rest locations influence alertness and performance among pilots, but few studies have evaluated cabin crew. The purpose of the present study was to evaluate sleep outcomes among cabin crewmembers sleeping in a bunk compared to a jump seat during one long-haul route. Methods Thirty-one (6 male) cabin crewmembers (age M = 30, SD = +/-13) flew the same long-haul route (outbound and return) with a flight duration of 10:41 (± 0:14) hours. Participants were randomly assigned to fly on an aircraft with a bunk or a jump seat for their sleep opportunity. Participants wore an Actiwatch (Phillips-Respironics Spectrum) throughout the entire study and completed a sleep diary at bedtime and upon waking. During flight they completed a nap diary entering the start time of the inflight sleep (if any) and the duration. Results Sixty-five flights (32 outbound and 33 return) were included in the analyses. Seventy-seven percent of the flights had a bunk and 23% had a jump seat. Crewmembers obtained M = 146.46 (± 67.20) minutes of rest out of which they slept M = 125.33 (± 64.91) minutes in the bunk. While using the jump seat, crewmembers obtained M = 169.53 (± 133.30) minutes of rest out of which they slept M = 142.92 (± 149.72) minutes. When crewmembers slept in the bunk, sleep latency was shorter (M = 13.69 ± 12.64 minutes) and efficiency was better (M = 76.16 ± 16.09 %) compared to the jump seat (sleep onset: M = 16.77 ± 13.89 minutes; sleep efficiency: M = 60.64 ± 17.42 %). Conclusion We found that cabin crewmembers slept for longer time when they used the jump seat. They fell asleep faster and their sleep efficiency was better when using the bunk compared to the jump seat. Further research is needed to understand how sleep quality and subsequent performance are influenced by sleep opportunity in a bunk compared to a jump seat. Support (if any) NASA Airspace Operations and Safety Program, System-Wide Safety Project.

104 The influence of self-reported distractions on the Psychomotor Vigilance Task (PVT) in laboratory and field environments

Abstract Introduction The Psychomotor Vigilance Task (PVT) is a measure of vigilant attention that is commonly used in laboratory environments to assess the neurobehavioral impact of sleep loss and circadian misalignment. The PVT has been increasing in popularity for use in field environments; however, the potential for distraction is higher in the field compared to the lab. It is unclear how distractions experienced by individuals taking the PVT in the real world may influence reaction time metrics. We investigated the influence of self-reported distraction on PVT outcomes across laboratory and field environments. Methods We examined PVT data from five studies including short (n=36 participants, 3799 PVTs) and long-haul (n=75 participants, 3282 PVTs) airline personnel, control center personnel (n=5 participants, 96 PVTs), and healthy individuals who participated in a study involving at-home and laboratory assessments (n=12 participants, 486 and 310 PVTs). Individuals in all of the studies were asked to complete the five-minute NASA PVT at least three times daily. Participants were asked to indicate the number of distractions they experienced immediately after each PVT. Mean PVT reaction time (RT) and number of distractions were computed for each study and overall. Results Participants reported more distractions in field environments compared to the lab (short-haul=1.29 +/- 1.48, long-haul=0.66 +/- 1.07, control-center=1.20 +/- 1.37, at-home=0.86 +/- 1.36, laboratory=0.46 +/- 1.07) Across all studies, we found that PVT RT slowed as self-reported distractions increased (all studies combined: 0 distractions=PVT RT 275.7ms; 1=285.0ms; 2=304.0ms; 3=322.9ms; >4=408.6ms). These findings were similar for healthy participants completing PVTs at home (0 distractions=286.4ms; 1=309.9ms; 2=328.3ms; 3=369.8ms; >4=385.1ms) but were less consistent during in-lab assessments (0 distractions=278.7ms; 1=316.2ms; 2=396.2ms; 3=370.4ms; >4=354.4ms). These findings were similar for other PVT outcomes. Conclusion Participants reported more distractions in field environments compared to the laboratory. Our findings suggest that the number of distractions that individuals report experiencing while taking a PVT increases the reaction time registered by the device. Researchers should collect information about distractions during the PVT and should be aware that distractions may influence the recorded PVT reaction time. Support (if any) NASA Airspace Operations and Safety Program, System-Wide Safety Project

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

To improve the predictive ability in trajectory of large unmanned aerial vehicle (UAV) and the calculation performance in complicated circumstances with mixed airspace, multiple aircraft types, and joint operations, the concept of phased trajectory deviation (PTD) is introduced and a corresponding minimal interval algorithm (PTD-MI) is set up. This algorithm is capable of deriving the minimal interval between various aircraft types according to the crosswind impacts and the UAV characteristics at different flight phases and thus achieves the effective safety evaluation in airspace operation. To demonstrate the rationality and generality of the proposed algorithm, several simulation experiments are conducted. Based on the experimental results, flight procedure protection area is plotted by PTD-MI algorithm and compared with that generated by Ground-Based Augmentation System (GBAS). Results indicate that the proposed algorithm is capable of deriving a more scientific basis for airspace assignment and outperform GBAS in dealing with wide-area space problems. And, compared with GBAS, PTD-MI algorithm shows a more stable calculation performance and is easier to output the results. PTD-MI algorithm is proposed under the flight safety regulation designed by the International Civil Aviation Organization (ICAO) and designed to provide effective technical supports for the safe and normal operations of aircrafts.

Temporal logic learning-based anomaly detection in metroplex terminal airspace operations

The airspace system is a complex dynamical system with complicated controlled interactions between its constituent subsystems – terminal airspace, en-route airspace, and ground. Of these, air traffic management in the multi-airport (metroplex) terminal airspace is one of the most complicated subsystems to manage, especially due to the interactions between proximal airports. Analyzing anomalous behaviors in the metroplex is emerging as a key problem in understanding air traffic management complexity and safety. Although physics-based approaches have been studied in-depth for this application, newfound interest has been observed to use recorded time-series air traffic surveillance and airport operations datasets for this purpose. In this paper, we propose a machine learning-based anomaly detection algorithm that generates mathematical models to detect anomalies in metroplex operations. Several machine learning algorithms have been developed to detect anomalies using only air traffic surveillance data, but there is a significant scope of improvement by including airport operational characteristics as well, since integrating such closely-controlled metroplex operational datasets allows the developed models to effectively detect anomalies. The key contribution of this paper is in allowing anomaly detection models to recursively update so as to adapt to changes in metroplex operations. The proposed algorithm is demonstrated with real air traffic surveillance and airport operations datasets at LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for operations in the New York metroplex.

Analysis of Terminal Area Airspace Operation Status Based on Trajectory Characteristic Point Clustering

The terminal control area airspace system is one of the most complicated subsystems in the entire air traffic control system. With the continuous increase of air traffic volume, the utilization of its airspace resources has become a bottleneck restricting the sustainable development of the system. Monitor and obtain its operating status information and formulating corresponding mitigation measures are essential to improve the overall performance of the system. In order to achieve that, a new system analysis method based on clustering of trajectory characteristic point is proposed. The flight trajectories from radar position data are reconstructed at first. Followed by the method of extracting flight trajectory characteristic points including turning points, altitude transition points and speed adjustment points, all characteristic points are extracted. Then, K-means is applied for clustering according to their 3D space position, while the results are analyzed by introducing evaluation indexes based on air traffic control operation strategies. Numerical experiments show significant clustering characteristics and revealing the operation status of the terminal area airspace, which can help air traffic manager to identify defects in airspace operation and providing guidance for future airspace planning and flight procedure adjustment.

Learning Air Traffic as Images: A Deep Convolutional Neural Network for Airspace Operation Complexity Evaluation

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand‐crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end‐to‐end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand‐crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two‐dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high‐level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand‐crafted factors.

Research on Air Traffic Flow Forecast Based on ELM Non-Iterative Algorithm

In this paper, the chaotic characteristics of air traffic flow are studied, ADS-B data easily available to ground aviation users are selected as the basic data of traffic flow, and a high-dimensional prediction model of air traffic flow time series based on the non-iterative PSR-ELM algorithm is established. The prediction results of the proposed algorithm are then compared with those of the SVR algorithm, which requires iteration. Moreover, airspace operation data before and after the outbreak of the COVID-19 epidemic are selected as the experimental scene, and the prediction effects of time series with different degrees of chaos are comparatively analyzed. The experimental results reveal that the PSR-ELM algorithm achieves fast and accurate results, and, when the traffic flow state is sparse, the degree of chaos is reduced and the prediction effect is improved. The findings of this research provide a reference for air traffic flow theory.

Approach Airspace Utilization Rate Mode of Terminal Area in Blocky Distributed Hazardous Weather

Hazardous weather has a great impact on the airspace operation of the terminal area, which will lead to a reduction in the utilization of the airspace of the arrival operation. Therefore, it is necessary to evaluate the airspace utilization of the terminal area in hazardous weather. This paper analyzed the impact of blocky hazardous weather area on flight path, flight distance and flight time, and established a calculation model for the terminal area arrival airspace utilization rate in the blocky hazardous weather area based on the flow volume ratio based on the time and space constraints of the terminal area airspace operation. We selected a control terminal which was affected by the blocky hazardous weather area, then used the model to calculate the approach airspace utilization rate and compared it with the approach airspace utilization rate under normal weather conditions. The results show that the evaluation results of the established model can reflect the actual operating conditions.

0175 Light Improves Alertness and Mood During the Sleep Inertia Period Following Slow Wave Sleep

Abstract Introduction Waking from sleep, especially slow wave sleep (SWS), is associated with reduced alertness known as sleep inertia. Light improves alertness during sleep deprivation and circadian misalignment. In this study, we assessed the efficacy of light to improve alertness and mood immediately after waking from SWS. Methods Twelve participants kept a sleep schedule of 8.5 h for 5 nights and 5 h for one night prior to the overnight laboratory visit (confirmed by actigraphy). Participants went to bed at their scheduled habitual bedtime in the laboratory and were monitored by standard polysomnography. After at least 5 min of SWS, participants were awoken and exposed to either red ambient light (control) or blue-enriched bright light (light) for 1 h. During this time, participants completed a subjective scale of alertness (Karolinska Sleepiness Scale, KSS) and visual analogue scales (VAS) of mood at 2 min, 17 min, 32 min, and 47 min after waking. Following this sleep inertia measurement period, all lights were turned off and participants were allowed to return to sleep. They were then awoken again from their subsequent SWS period and exposed to the opposite condition (control or light). A linear mixed-effects model with fixed effects of condition, time, and condition*time and a random effect of participant was used to determine the impact of light across the testing period. An average of baseline responses (pre-sleep) was included as a covariate. Results Compared to the control condition, participants exposed to blue-enriched bright light reported feeling more alert (KSS: F1,77=4.955, p=.029; VASalert: F1,77=8.226, p=.005), more cheerful (VAScheerful: F1,77=8.615, p=.004), less depressed (VASdepressed: F1,77=4.649, p=.034), and less lethargic (VASlethargic: F1,77=5.652, p=.020). Conclusion Exposure to blue-enriched bright light immediately after waking from SWS may help to improve subjective alertness and mood. Future analyses will explore whether these findings extend to effects on cognitive performance. Support Naval Postgraduate School Grant. NASA Airspace Operations and Safety Program, System-Wide Safety Project.

0078 Influence of Light on Brain Activity Upon Waking From Slow Wave Sleep

Abstract Introduction Waking from sleep is associated with reduced alertness due to sleep inertia. Light acutely improves alertness during sleep deprivation. In this study we assessed the influence of light on brain activity and connectivity after waking from slow wave sleep (SWS). Methods Twelve participants kept an actigraphy-confirmed stable sleep schedule with 8.5 hours for five nights and five hours for one night prior to an overnight laboratory visit. Participants completed two three-minute Karolinska Drowsiness Tests (KDT) before going to bed at their habitual bedtime. They were monitored continuously using high-density EEG (32-channel; Brain Products GmbH). Participants were woken twice and exposed to red light (0.01 melanopic-lux; control) or blue-enriched light (63.62 melanopic-lux) for one hour, in a randomized order, following at least five minutes of SWS. EEG artifact were removed algorithmically and the spectral composition of each electrode (i.e., fast fourier transform, FFT) and effective connectivity (i.e., partial directed coherence, PDC) between each electrode were estimated. A graphical analysis was conducted to extract features relevant to the facilitation of efficient communication between electrodes. All data were averaged within frequency bins of interest that correspond to delta (1-3Hz), theta (4-7Hz), alpha (8-12Hz), and beta (13-25Hz) bands and expressed relative to the pre-sleep baseline. Results Compared to the pre-sleep baseline, participants exposed to blue-enriched light experienced reduced theta and alpha activity; however, these results were not significantly different from the control. In contrast, the communication of frontal electrodes significantly increased across all frequency bands compared to the control, and this effect was most prominent in the alpha (t(11)=3.80, p=.005) and beta bands (t(11)=3.92, p=.004). Conclusion Exposure to blue-enriched light immediately after waking from SWS may accelerate the process of waking and help to improve alertness by facilitating communication between brain regions. Future analyses will explore the temporal persistence and granularity of the communicative properties associated with this response. Support Naval Postgraduate School Grant. NASA Airspace Operations and Safety Program, System-Wide Safety Project.

Scalable Multi-Agent Computational Guidance with Separation Assurance for Autonomous Urban Air Mobility

Electric vertical takeoff and landing vehicles are becoming promising for on-demand air transportation in urban air mobility (UAM). However, successfully bringing such vehicles and airspace operations to fruition will require introducing orders of magnitude more aircraft to a given airspace volume. Although there are existing solutions for communication technology, onboard computing capability, and sensor technology, the computation guidance algorithm to enable safe, efficient, and scalable flight operations for dense self-organizing air traffic still remains an open question. In this paper, a message-based decentralized computational guidance algorithm is proposed and analyzed for multiple cooperative aircraft by formulating this problem using multi-agent Markov decision process and solving it by Monte Carlo tree search algorithm. A novel coordination strategy is introduced by using the logit level- model in behavioral game theory. To achieve higher scalability, the airspace sector concept is introduced into the UAM environment by dividing the airspace into sectors, so that each aircraft only needs to coordinate with aircraft in the same sector. At each decision step, all of the aircraft will run the proposed computational guidance algorithm onboard, which can guide all the aircraft to their respective destinations while avoiding potential conflicts among them. For validation and demonstration, a free-flight airspace simulator that incorporates environment uncertainty is built in an OpenAI Gym environment. Numerical experiment results over several case studies, including the roundabout test problem, show that the proposed computational guidance algorithm has promising performance even with the high-density air traffic case.

해외도입 FTB 항공기의 국내 운용을 위한 제도 고찰

해외도입 FTB 항공기의 국내 운용을 위한 제도 고찰

A Mindset-Based Evolution of Unmanned Aircraft System (UAS) Acceptance Into the National Airspace System (NAS)

The main goal of this research is to evaluate the Air Traffic Controller (ATCo) workload considering the Unmanned Aircraft System (UAS) integration into the National Airspace System (NAS) through fast-time simulations, and including futuristic scenarios in which the ATCo is familiar with manned and unmanned aircraft, i.e., different ATCo mindsets are considered. As these professionals play an essential role in optimizing the airspace operation, maintaining their workload at an acceptable level is essential. However, the integration of new technologies, such as UAS, may present an impact on safety levels from the workload perspective. In this context, the Technology Maturity Level (TML), which is a systematic metric/measurement system that supports assessments of the familiarity of a particular aircraft with ATCos, is proposed. The experiments showed that the integration of UAS into the NAS should be conducted gradually.

A method for estimating flight paths missing data

Air-traffic optimization is an essential part of airspace operation reengineering, as the number of flights and the usage of routes increase in the world. The NextGen and SESAR projects are important initiatives that allow for more scalability and safety in air-traffic. One element of these projects is the Automatic Dependent Surveillance-Broadcast (ADS-B) system which allows airplanes to share their position and speed. The ADS-B antennas’ coverage is somewhat limited in less economically developed and oceanic areas, resulting in a lack of flight path data. This paper proposes a method based on artificial neural networks (ANN), interpolation and average computation to fill flight path data partially tracked by ADS-B antennas. While other methods are focused on one or two dimensions of the flight path, this work is focused on infilling the 4-dimensions present in the ADS-B data (latitude, longitude, altitude and ground speed). This work is useful in analyzing performance of historical flights related to limited coverage areas or in predicting flight path in air-traffic management systems (ATMs). The comparison between the real and estimated trajectories in a set of 517 flights has shown accuracy superior to 92% for the metrics distance flown, estimated burned fuel, and trajectory correlation.

Computational Platform for Analyzing the Safety of the National Airspace System

Safety analysis of complex systems can be carried out using computational models of the subsystems and their interactions. A modeling framework for analyzing the safety of the national airspace operations is described. The framework enables investigators to collaboratively formulate models of the subsystems of the National Airspace System and insert off-nominal operating conditions to assess their impact on the overall system safety. The software is designed to allow popular computational frameworks such as Python and Java to interact with it in a seamless manner. Interfaces to one of the popular commercial computational package are also provided. The software incorporates detailed aspects of the national airspace system infrastructure such as airport taxi ways and runways, approach-departure procedures, jet routes, air traffic control Sector and Center boundaries. It also includes simplified human performance models of controllers, pilots and ground vehicle operators. Performance data for over 400 different aircraft types, derived from a well-known database have also been incorporated. Airspace flight operations data is obtained from the Federal Aviation Administration data feed, and the weather data is derived from the National Oceanic and Atmospheric Administration web site. Two example applications of the software are given.

Minimizing airline and passenger delay cost in airport surface and terminal airspace operations

This research studies the effectiveness of incorporating airline and passenger delay cost (APDC) into an integrated airport surface and terminal airspace (ASTA) traffic management system. Most air traffic management systems typically schedule aircraft with an assumption that all flights want to be operated along the minimum fuel trajectory. However, airlines and passengers may have other preferences that can significantly influence flight schedules. Therefore, the objective of this research is to investigate the effect of incorporating APDC to ASTA scheduling, while ensuring safety. A mixed integer nonlinear programming model (MINLP-APDC) and a swap separation violating aircraft heuristic model (SSVA-APDC) are developed to minimize the cost of delays for airlines and passengers. The proposed approaches are compared to the first-come, first-serve heuristic and two integrated scheduling algorithms for ASTA operations: 1) minimizing runway makespan (MINLP-RM); and 2) minimizing flight delays (MINLP-FD). The experimental results show that the proposed approaches save at least 1.2% APDC compared to other approaches. The proposed approaches can also achieve at least 3.0% fewer flight delays than the MINLP-RM model without increasing either runway or schedule makespan. Compared to MINLP-FD, the MINLP-APDC model increases flight delays by on average 3.7% while the SSVA-APDC model achieves on average 15.1% more flight delays. Although the MINLP-APDC model outperforms the SSVA-APDC heuristic in terms of APDC and flight delays, it requires more than 30 min of computational time. Meanwhile, the SSVA-APDC heuristics requires only a few seconds to provide a feasible flight schedule, which makes it more practical.

Optimization of terminal airspace operation with environmental considerations

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.

A knowledge-transfer-based learning framework for airspace operation complexity evaluation

A sector is a component airspace whose operation is allocated to an air traffic controller. The operation complexity of a sector plays a critical role in the current Air Traffic Management system, e.g. it determines the workload volume of air traffic controllers and serves as a reliable index for airspace configuration and traffic flow management. Therefore, accurately evaluating the sector operation complexity is a problem of paramount importance in both practice and research. Due to numerous interacting factors, traditional methods based on only one single complexity indicator fail to accurately reflect the true complexity, especially when these factors are nonlinearly correlated. In light of these, the attempt to use machine learning models to mine the complex factor-complexity relationship has prevailed recently. The performance of these models however relies heavily on sufficient samples. The high cost of collecting ample data often results in a small training set, adversely impacting on the performance that these machine learning models can achieve. To overcome this problem, this paper for the first time proposes a new sector operation complexity evaluation framework based on knowledge transfer specifically for small-training-sample environment. The proposed framework is able to effectively mine knowledge hidden within the samples of the target sector, i.e. the sector undergoes evaluation, as well as other sectors, i.e. non-target sectors. Moreover, the framework can properly handle the integration between the knowledge derived from different sectors. Extensive experiments on real data of 6 sectors in China illustrate that our proposed framework can achieve promising performance on complexity evaluation when only a small training set of the target sector is available.

Visualizing aviation impacts: Modeling current and future flight trajectories with publicly available flight data

As the demand for air travel continues to grow both nationally and internationally, airports seek airfield expansions and new infrastructure to accommodate new aircraft movements. However, airport expansion initiatives – runway extensions and/or new runway construction – directly impact terminal airspace operations. A change in flight paths near the airport affects the population living proximate to the airport, increasing noise levels and exposure to pollutants emitted by low-altitude departures and arrivals. While airspace procedures and their impacts are well-understood by aviation stakeholders, they are often convoluted and technical in nature. To this end, we propose a methodology to visualize terminal airspace trajectories leveraging publicly available data and commonly-used software. The trajectories are extracted via polynomial regression and hyperbolic tangent interpolation is applied to reflect accurate final approach maneuvers. Final visualizations are rendered in ArcGIS, a geographic information system used by many Metropolitan Planning Organizations. These trajectory visualization modules can also be combined with external models that provide noise contour projections and emission level estimations. We present a case study of Chicago O’Hare International Airport (ORD) due to the contentious O’Hare Modernization Program. The resultant interactive and dynamic three-dimensional trajectory airspace maps for ORD rendered via ArcGIS are easy to understand and customize, allowing MPOs to package them into downloadable online modules. MPOs armed with these modules will bridge the critical gap between the technical knowledge presented by authorities touting airport expansions and the residents who are impacted by the expansions.