Engineering topics

Zelinski, Shannon

Publications and source records attributed to Zelinski, Shannon.

At least 19 records

NASA NTRS2017

Modeling and Simulating Airport Surface Operations with Gate Conflicts

The Surface Operations Simulator and Scheduler (SOSS) is a fast-time simulation platform used to develop and test future surface scheduling concepts such as NASAs Air Traffic Demonstration 2 of time-based surface metering at Charlotte Douglas International Airport (CLT). Challenges associated with CLT surface operations have driven much of SOSS development. Recently, SOSS functionality for modeling hardstand operations was developed to address gate conflicts, which occur when an arrival and departure wish to occupy the same gate at the same time. Because surface metering concepts such as ATD2 have the potential to increase gates conflicts as departure are held at their gates, it is important to study the interaction between surface metering and gate conflict management. Several approaches to managing gate conflicts with and without the use of hardstands were simulated and their effects on surface operations and scheduler performance compared.

Zelinski, Shannon↗

NASA NTRS2017

Modeling and Simulating Airport Surface Operations with Gate Conflicts

The Surface Operations Simulator and Scheduler (SOSS) is a fast-time simulation platform used to develop and test future surface scheduling concepts such as NASA's Air Traffic Demonstration 2 of time-based surface metering at Charlotte Douglass International Airport (CLT). Challenges associated with CLT surface operations have driven much of SOSS development. Recently, SOSS functionality for modeling harsdstand operations was developed to address gate conflicts, which occur when an arrival and departure wish to occupy the same gate at the same time. Because surface metering concepts such as ATD2 have the potential to increase gates conflicts as departures are held at their gates, it is important to study the interaction between surface metering and gate conflict management. Several approaches to managing gate conflicts with and without the use of hardstands were simulated and their effects on surface operations and scheduler performance compared.

Zelinski, Shannon↗

NASA NTRS2017

Assessing Tactical Scheduler Options for Time-Based Surface Metering

NASA is committed to demonstrating a concept of integrated arrival, departure, and surface operations by 2020 under the Airspace Technology Demonstration 2 (ATD2) sub-project. This will be accomplished starting with a demonstration of flight specific time-based departure metering at Charlotte Douglass International Airport (CLT). ATD2 tactical metering capability is based on NASAs Spot And Runway Departure Advisor (SARDA) which has been tested successfully in human-in-the-loop simulations of CLT. SARDA makes use of surface surveillance data and surface modeling to estimate the earliest takeoff time for each flight active on the airport surface or ready for pushback from the gate. The system then schedules each flight to its assigned runway in order of earliest takeoff time and assigns a target pushback time, displayed to ramp controllers as an advisory gate hold time. The objective of this method of departure metering is to move as much delay as possible to the gate to minimize surface congestion and engine on-time, while keeping sufficient pressure on the runway to maintain throughput. This flight specific approached enables greater flight efficiency and predictability, facilitating trajectory-based operations and surface-airspace integration, which ATD2 aims to achieve.Throughout ATD2 project formulation and system development, researchers have continuously engaged with stakeholders and future users, uncovering key system requirements for tactical metering that SARDA did not address. The SARDA scheduler is updated every 10 seconds using real-time surface surveillance data to ensure the most up-to-date information is used to predict runway usage. However, rapid updates also open the potential for fluctuating advisories, which Ramp controllers at a busy airport like CLT find unacceptable. Therefore, ATD2 tactical metering requires that all advisories freeze once flights are ready so that Ramp controllers may communicate a single hold time when responding to pilot ready calls.

Zelinski, Shannon↗

NASA NTRS2017

Assessing Tactical Scheduling Options for Time-Based Surface Metering

This paper presents a parametric analysis of the most recent tactical scheduler design for NASA’s Airspace Technology Demonstration 2 (ATD-2) sub-project, committed to demonstrating time-based surface metering at Charlotte Douglas International Airport (CLT). The tactical scheduler design is implemented in a fast-time simulation model of CLT using NASA’s Surface Operations Simulator and Scheduler. The tactical scheduler is supported by three basic functions: trajectory prediction, runway scheduling, and advisory generation. A key parameter of the advisory generation function is the taxi time delay buffer used when calculating target gate pushback times from runway schedule. Multiple simulations that varied the amount of taxi time delay buffer were analyzed to determine the effect on tactical scheduler performance. The results show an improvement in tactical scheduler performance when the buffer is made sufficiently large to release departures from their gates early enough to maintain scheduler predicted runway throughput.

Zelinski, Shannon↗

NASA NTRS2016

Route Optimization for Offloading Congested Meter Fixes

The Optimized Route Capability (ORC) concept proposed by the FAA facilitates traffic managers to identify and resolve arrival flight delays caused by bottlenecks formed at arrival meter fixes when there exists imbalance between arrival fixes and runways. ORC makes use of the prediction capability of existing automation tools, monitors the traffic delays based on these predictions, and searches the best reroutes upstream of the meter fixes based on the predictions and estimated arrival schedules when delays are over a predefined threshold. Initial implementation and evaluation of the ORC concept considered only reroutes available at the time arrival congestion was first predicted. This work extends previous work by introducing an additional dimension in reroute options such that ORC can find the best time to reroute and overcome the 'firstcome- first-reroute' phenomenon. To deal with the enlarged reroute solution space, a genetic algorithm was developed to solve this problem. Experiments were conducted using the same traffic scenario used in previous work, when an arrival rush was created for one of the four arrival meter fixes at George Bush Intercontinental Houston Airport. Results showed the new approach further improved delay savings. The suggested route changes from the new approach were on average 30 minutes later than those using other approaches, and fewer numbers of reroutes were required. Fewer numbers of reroutes reduce operational complexity and later reroutes help decision makers deal with uncertain situations.

Optimized Route Capability↗

NASA NTRS2016

Departure Queue Prediction for Strategic and Tactical Surface Scheduler Integration

A departure metering concept to be demonstrated at Charlotte Douglas International Airport (CLT) will integrate strategic and tactical surface scheduling components to enable the respective collaborative decision making and improved efficiency benefits these two methods of scheduling provide. This study analyzes the effect of tactical scheduling on strategic scheduler predictability. Strategic queue predictions and target gate pushback times to achieve a desired queue length are compared between fast time simulations of CLT surface operations with and without tactical scheduling. The use of variable departure rates as a strategic scheduler input was shown to substantially improve queue predictions over static departure rates. With target queue length calibration, the strategic scheduler can be tuned to produce average delays within one minute of the tactical scheduler. However, root mean square differences between strategic and tactical delays were between 12 and 15 minutes due to the different methods the strategic and tactical schedulers use to predict takeoff times and generate gate pushback clearances. This demonstrates how difficult it is for the strategic scheduler to predict tactical scheduler assigned gate delays on an individual flight basis as the tactical scheduler adjusts departure sequence to accommodate arrival interactions. Strategic/tactical scheduler compatibility may be improved by providing more arrival information to the strategic scheduler and stabilizing tactical scheduler changes to runway sequence in response to arrivals.

departure scheduling↗

NASA NTRS2016

Comparison of Different Control Schemes for Strategic Departure Metering

Airports and their terminal airspaces are key choke points in the air transportation system causing major delays and adding to pollution. A solution aimed at mitigating these chokepoints integrates the scheduling of runway operations, flight release from the gates and ramp into the airport movement area, and merging with other traffic competing for downstream airspace points. Within this integrated concept, we present a simulation-based analysis of the departure metering process, which delays the release of flights into the airport movement area while balancing two competing objectives: (1) maintaining large enough queues at the airport resources to maximize throughput and (2) absorbing excess delays at the gates or in ramp areas to save on fuel consumption, emissions, noise, and passenger discomfort. Three metering strategies are compared which respectively attempt to control the number of flights that (1) left the gate but did not take off, (2) left the ramp but did not take off, and (3) spent their unimpeded transit time to the runway but did not take off. It was observed that under deterministic and demand uncertainty conditions, the first strategy performed better than the other two strategies in terms of maintaining the runway throughput while transferring a significant average delay of two minutes to the gate. On the other hand, under uncertainties of flight transit time and runway service rate, all the strategies struggled to delay flights at the gate without a significant impact on the runway throughput.

air traffic management↗

NASA NTRS2016

Optimized Route Capability (ORC) Intelligent Offloading of Congested Arrival Routes

The Optimized Route Capability (ORC) concept is designed to enable intelligent offloading of congested arrival routes. When ORC predicts arrival route congestion as projected excess arrival meter fix delay, automation offers decision support to traffic managers by identifying candidate flights to strategically reroute to alternate meter fixes and alleviate the congestion. This concept was applied to a model of arrival operations into Houston International Airport. An arrival rush from the Northeast was simulated in fast-time to analyze ORC algorithm behavior. The results demonstrate how strategically rerouting a few flights to alternate meter fixes not only has the potential to manage meter fix delay (and possibly the need for traffic management initiatives applied upstream), but may also increase airport capacity utilization and reduce total flight delay.

Zelinski, Shannon↗

NASA NTRS2015

Optimizing Integrated Arrival, Departure and Surface Operations Under Uncertainty

In airports and surrounding terminal airspaces, the integration of arrival, departure and surface scheduling and routing have the potential to improve the operations efficiency. Recent research had developed mixed-integer-linear programming algorithm-based scheduler for integrated arrival and departure operations in the presence of uncertainty. This paper extends to the surface previous research performed by the authors to integrate taxiway and runway operations. The developed algorithm is capable of computing optimal aircraft schedules and routings that reflects the integration of air and ground operations. A preliminary study case is conducted for a set of thirteen aircraft evolving in a model of the Los Angeles International airport and surrounding terminal areas. Using historical data, a representative traffic scenario is constructed and probabilistic distributions of pushback delay and arrival gate delay are obtained. To assess the benefits of optimization, a First- Come-First-Serve algorithm approach comparison is realized. Evaluation results demonstrate that the optimization can help identifying runway sequencing and schedule that reduce gate waiting time without increasing average taxi times.

Bosson, Christabelle↗

NASA NTRS2015

A Stochastic Scheduler for Integrated Arrival, Departure and Surface Operations in Los Angeles

In terminal airspace, integrating arrivals, departures, and surface operations with competing resources provides the potential of improving operational efficiency by removing barriers between different operations. This work develops a centralized stochastic scheduler for operations in a terminal area including airborne and surface operations using Non-dominated sorting genetic algorithm and Monte Carlo simulations. The scheduler handles completing resources between different flows, such as runway allocations, runway crossing, departure fixes, and other interaction way points between arrivals and departures. Meanwhile, the scheduler also takes time-varied uncertainties into account when optimizing schedules. The scheduler is run sequentially to identify the best and robust schedule for the next planning window. Resulting schedules decide the routes, speed or delays, and runway assignments with separation constraints at mergingdiverging waypoints in the air and crossing and separations on runways. The Los Angels terminal area was used as an example. The implementation of this stochastic scheduler for integrated arrival, departure and surface operations is completed. And several preliminary runs are finished for over 1,200 flights in LAX in a typical day. Sensitivity studies on various planning window sizes are presented, which shows that trade-off exits between planning window size and achievable minimum delay. Preliminary results on runway usage are also presented in this abstract. Because arrivals on the outer runways have to be followed by crossings on the inner runways, algorithmic runway allocation prefers inner runways for arrivals and outer runways for departures. More results will be presented in the final paper. And current terminal arrival and departure procedures based on first-come-first-serve procedure will also be set up and used as a baseline for comparison.

integrated scheduler↗

NASA NTRS2014

Optimizing Integrated Terminal Airspace Operations Under Uncertainty

In the terminal airspace, integrated departures and arrivals have the potential to increase operations efficiency. Recent research has developed geneticalgorithm- based schedulers for integrated arrival and departure operations under uncertainty. This paper presents an alternate method using a machine jobshop scheduling formulation to model the integrated airspace operations. A multistage stochastic programming approach is chosen to formulate the problem and candidate solutions are obtained by solving sample average approximation problems with finite sample size. Because approximate solutions are computed, the proposed algorithm incorporates the computation of statistical bounds to estimate the optimality of the candidate solutions. A proof-ofconcept study is conducted on a baseline implementation of a simple problem considering a fleet mix of 14 aircraft evolving in a model of the Los Angeles terminal airspace. A more thorough statistical analysis is also performed to evaluate the impact of the number of scenarios considered in the sampled problem. To handle extensive sampling computations, a multithreading technique is introduced.

air traffic optimization↗

NASA NTRS2014

Precision Arrival Scheduling for Tactical Reconfiguration

This research adapts the concept of precision arrival scheduling to accommodate reconfiguration operations between two sets of fixed arrival routes modeled for Chicago O'Hare International Airport (ORD). Integrated fixed path routing from en-route to runways was modeled for ORD's top two peak traffic configurations, as well as transition routing between the two configurations. A first-come-first-served multi-point scheduler was adapted to the reconfiguration problem by prioritizing the rescheduling of aircraft within the terminal airspace at the time of reconfiguration notification. Arrival rescheduling was then tested for a range of arrival rates and reconfiguration notification lead-times in fasttime simulation. Reconfigurations with lead-times as short as 10 minutes at a nominal static configuration arrival rate (~25 arrivals per quarter hour) could be accommodated with little impact to throughput. However, as lead-time shortened below 25 minutes, individual aircraft efficiency quickly degraded due to extra flight time at lower altitude and speed. In general, first-come-first-served arrival scheduling on fixed routing for this sample reconfiguration problem is promising if at least 10-15 minutes lead-time is given before the reconfiguration is in effect.

airport reconfiguration↗

NASA NTRS2014

Integrated Arrival and Departure Schedule Optimization Under Uncertainty

In terminal airspace, integrating arrivals and departures with shared waypoints provides the potential of improving operational efficiency by allowing direct routes when possible. Incorporating stochastic evaluation as a post-analysis process of deterministic optimization, and imposing a safety buffer in deterministic optimization, are two ways to learn and alleviate the impact of uncertainty and to avoid unexpected outcomes. This work presents a third and direct way to take uncertainty into consideration during the optimization. The impact of uncertainty was incorporated into cost evaluations when searching for the optimal solutions. The controller intervention count was computed using a heuristic model and served as another stochastic cost besides total delay. Costs under uncertainty were evaluated using Monte Carlo simulations. The Pareto fronts that contain a set of solutions were identified and the trade-off between delays and controller intervention count was shown. Solutions that shared similar delays but had different intervention counts were investigated. The results showed that optimization under uncertainty could identify compromise solutions on Pareto fonts, which is better than deterministic optimization with extra safety buffers. It helps decision-makers reduce controller intervention while achieving low delays.

stochastic optimization↗

NASA NTRS2011

Comparing Methods for Dynamic Airspace Configuration

This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most.

Zelinski, Shannon↗

NASA NTRS2011

Comparing Methods for Dynamic Airspace Configuration

Zelinski, Shannon↗

NASA NTRS2011

Defining Dynamic Route Structure

This poster describes a method for defining route structure from flight tracks. Dynamically generated route structures could be useful in guiding dynamic airspace configuration and helping controllers retain situational awareness under dynamically changing traffic conditions. Individual merge and diverge intersections between pairs of flights are identified, clustered, and grouped into nodes of a route structure network. Links are placed between nodes to represent major traffic flows. A parametric analysis determined the algorithm input parameters producing route structures of current day flight plans that are closest to todays airway structure. These parameters are then used to define and analyze the dynamic route structure over the course of a day for current day flight paths. Route structures are also compared between current day flight paths and more user preferred paths such as great circle and weather avoidance routing.

Zelinski, Shannon↗

NASA NTRS2010

Effect of Dynamic Sector Boundary Changes on Air Traffic Controllers

The effect of dynamic sector boundary changes on air traffic controller workload was investigated with data from a human-in-the-loop simulation. Multiple boundary changes were made during simulated operations, and controller rating of workload was recorded. Analysis of these data showed an increase of 16.9% in controller workload due to boundary changes. This increased workload was correlated with the number of aircraft handoffs and change in sector volume. There was also a 12.7% increase in average workload due to the changed sector design after boundary changes. This increase was correlated to traffic flow crossing points getting closer to sector boundaries and an increase in the number of flights with short dwell time in a sector. This study has identified some of the factors that affect controller workload when sector boundaries are changed, but more research is needed to better understand their relationships.

Jung, Jaewoo↗

NASA NTRS2010

Operational Dynamic Configuration Analysis

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.

Lai, Chok Fung↗