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Integrated Demand Management (IDM) Annotated Bibliography page header
NASA has developed the Integrated Demand Management (IDM) concept over a five-year long project lifespan. During that time period, IDM researchers, both at NASA and outside organizations funded by NASA, published approximately forty conference papers and other technical publications associated with the IDM concept. In the following sections, these publications are cited and annotated for their content and their relevance to the IDM concept development.

Rather than organizing the reference list alphabetically, the citations are organized by topic and logical sequence of the work. References are distinguished as either research conducted as direct support for the IDM concept development, or outside research with broader perspectives that could benefit the IDM concept in general. Each reference is identified by "N" for research conducted and published by NASA’s in-house researchers, or "O" for research conducted by outside organizations funded by the IDM project.

Integrated Demand Management Concept Development and Evaluation header
The IDM concept originated from the desire to effectively integrate, or coordinate, two main traffic flow management capabilities: Traffic Flow Management System (TFMS) and Time-Based Flow Management (TBFM). IDM addressed imbalances between traffic demand and capacity through the coordinated use of the more strategic TFMS and its new Collaborative Trajectory Options Program (CTOP) capability, and the more tactical TBFM with its new capabilities that enable Extended Metering to feed the existing Arrival Metering.

The following section describes research regarding the IDM concept development. The concept was initially developed for clear weather, high traffic density scenarios to Newark airport. It was subsequently expanded for convective weather scenarios where the flights needed to be rerouted around the weather cells and into different meter fixes for the Newark and LaGuardia airports. These papers describe the concept development and evaluation through a series of human-in-the-loop (HITL) simulations. Additional benefits and feasibility analyses were conducted using a fast-time simulation capability.

Clear weather concept, benefits, and feasibility

N1) Smith, N. M., Brasil, C. B., Lee, P. U., Buckley, N., Gabriel, C., Mohlenbrink, C., Omar, F., Parke, B., Speridakos, C., Yoo, H. Integrated Demand Management: Coordinating Strategic and Tactical Flow Scheduling Operations. AIAA ATIO 2016.

- View N1 Summary/Relevance (Click to Expand/Collapse)

N2) Yoo, H. Brasil, B, Buckley, N., Mohlenbrink, C., Speridakos, C., Parke, B., Hodell, G., Lee, P. U. and Smith N. Integrated Demand Management: Minimizing Unanticipated Excessive Departure Delay while Ensuring Fairness from a Traffic Management Initiative. AIAA ATIO 2017.

N3) Mohlenbrink, C., Parke, B., Yoo, H. Y., Brasil, C., Buckley, N., Speridakos, C., Muro, F., Hodell, G., Lee, P. U. and Smith, N. M. Evaluation of Integrated Demand Management looking into Strategic & Tactical Flow Management, ATM 2017.

- View N2/N3 Summary/Relevance (Click to Expand/Collapse)

N4) Evans, T. and Lee, P. Analyzing Double Delays at Newark Liberty International Airport. AIAA Aviation 2016

- View N4 Summary/Relevance (Click to Expand/Collapse)

N5) Arneson, H., Evans, T., Li, J., and Wei, M. Automated Simulation in Support of Integrated Demand Management. AIAA Royal Aeronautical Society Flight Simulation, 2017.

N6) Arneson, H., Evans, A., Kulkarni, D., Lee, P. Li, J. and Wei, M. Using an Automated Air Traffic Simulation Capability for a Parametric Study in Traffic Flow Management. Aviation 2018.

- View N5/N6 Summary/Relevance (Click to Expand/Collapse)

Convective weather concept, benefits and feasibility

The following three papers describe the IDM concept development for convective weather scenarios, in which the flights were rerouted around weather cells and into different meter fixes for Newark and LaGuardia airports. These papers describe the concept development and evaluation in a series of human-in-the-loop (HITL) simulations.

N7) Smith, N. Integrated Demand Management - Stakeholder Engagement: Demonstrating Benefits of Submitting Multiple Trajectory Options, ARMD/AOSP Technical Seminar, 2018.

N8) Yoo, H. Brasil, C., Buckley, N., Hodell, G., Kalush, S., Lee, P. U. and Smith N. M. Impact of Different Trajectory Option Set Participation Levels within an Air Traffic Management Collaborative Trajectory Option Program. AIAA ATIO 2018.

N9) Hodell, G., Smith, N., Brasil, C. Yoo, H., Buckley, N., Kalush, S., Gabriel, C., and Lee. P. U. Demonstrating Early Adopter Benefits of Submitting Multiple Trajectory Options for Airlines. AIAA AVIATION Forum and Exposition, 2020.

- View N7/N8/N9 Summary/Relevance (Click to Expand/Collapse)

N10) Yoo, H., Evans, A., Kulkarni, D., Lee, P., Li, J., Wei, M. and Wang, Y. Benefit Assessment of the Integrated Demand Management Concept for Multiple New York Metroplex Airports. SciTech 2020.

- View N10 Summary/Relevance (Click to Expand/Collapse)

Impact of TBFM Settings

The IDM concept originated from the desire to integrate, or coordinate, TFMS/CTOP and TBFM. A large amount of research was devoted to understanding and enhancing CTOP capabilities to be used effectively in this concept. However, the project also devoted significant efforts to tune TBFM to effectively handle traffic demand that was pre-conditioned by CTOP. The effort mostly focused on adapting the latest TBFM capabilities, including Extended Metering (XM), rather than developing new capabilities or conducting controlled studies. As a result, fewer publications for TBFM related research were written. However, the following paper represents one controlled TBFM study that was conducted.

N11) Parke, B., Mohlenbrink, C., Brasil, C., Speridakos, C., Yoo, H. Y., Omar, F., Buckley, N., Gabriel, C., Lee, P. U. and Smith, N. M. Reducing Departure Delays for Adjacent Center Airports using Time Based Flow Management Scheduler: Checkbox ON or OFF?. DASC, 2016.

- View N11 Summary/Relevance (Click to Expand/Collapse)

Impact of RTA Capability

During the first couple of years of the IDM project, the RTA capability was an utilized to control scheduled times into CTOP FCAs for en route aircraft. Using RTAs to better control traffic demand into CTOP FCAs once aircraft were airborne made sense and had the support of airline stakeholders. However, HITL results in N2 and N3 suggested that the RTA capability did not seem to add benefits to the overall throughput and delay metrics. Following paper examines why RTA may not have been beneficial in the HITL experiments.

N12) Yoo, H. Mohlenbrink, C. Brasil, B, Buckley, N., Globus, A. Smith N. and Lee, P. Required Time of Arrival as a Control Mechanism to Mitigate Uncertainty in Arrival Traffic Demand Management. DASC 2016.

- View N12 Summary/Relevance (Click to Expand/Collapse)

CTOP capability enhancements and use cases header
The papers referenced so far have described the overall IDM concept development and evaluation process. The following sections describe papers that explored ways to enhance and expand CTOP capabilities by using various algorithms and decision support tools to automatically calculate and generate TOS, Relative Trajectory Costs (RTCs), and other functions.

Trajectory Option Set Generation

One of the key functions in CTOP is the ability for airline operators to submit alternate preferred trajectories for a given flight using the Trajectory Option Set (TOS) mechanism. However, the workload for the airline dispatchers to generate alternate trajectories that are acceptable to the air traffic controllers (ATCs) may be too prohibitive to allow routine usage of CTOP. Therefore, we have researched ways to automatically generate TOS routes using algorithms and historical data.

N13) Arneson, H., Bombelli, A. and Segarra-Torne, A. and Tse, E. Analysis of convective weather impact on pre-departure routing of flights from Fort Worth Center to New York Center. AIAA Aviation 2017.

O1) Hall W., and Hunter G., Trajectory Optimization and the Clearable Route Network, AIAA Aviation Forum. 2018.

- View N13 Summary/Relevance (Click to Expand/Collapse)

N14) Evans, T. and Lee, P. Predicting the Operational Acceptance of Route Advisories. AIAA Aviation 2017. N15) Evans, T., Lee, P. Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options. ATM Seminar, 2019.

- View N14/N15 Summary/Relevance (Click to Expand/Collapse)

Relative Trajectory Cost

One of the key attributes of CTOP is the application of TOS, in which participating air carriers can specify their preferences for rerouting options. Preferences are stated via Relative Trajectory Costs (RTCs). For a given flight, the air carrier submits one RTC value for each alternate route in the TOS. The CTOP resource allocation algorithm uses the RTC values to infer which route the air carrier would like the flight to take, given the amount of ground delay it would receive on each of the routes.

N16) Kulkarni, D. Analysis of Impact of RTC Errors on CTOP Performance. NASA/TM-2018-219943. 2018.

- View N16 Summary/Relevance (Click to Expand/Collapse)

O2) Hoffman, R., Hackney, B., Kicinger, R., Wei, P. and Zhu, G. Ball, M. Computational Methods for Flight Routing Costs in Collaborative Trajectory Options Programs. AIAA Aviation Forum. 2018.

O3) Tereshchenko, I., Hanson, M. Hoffman, R., and Hackney, B. Relative Trajectory Cost Prediction for Trajectory Option Set Generation in CTOP Simulations. AIAA Aviation Forum. 2018.

O4) Tereshchenko, I. and Hanson, M. Relative Trajectory Cost Estimation for CTOP Applications Using Multivariate Nonparametric Finite Mixture Logit. ICRAT 2018.

Decision Support Tools

Although CTOP is a powerful tool that can significantly enhance traffic flow management in the NAS, the complexity of managing multiple FCAs within a single CTOP, as well as managing multiple interacting CTOPs, provides a significant challenge that is difficult to manage without the help of automation and new decision support tools (DSTs). In the following set of papers, NASA researchers, as well as Mosaic-ATM and Metron with funding and technical support from NASA, developed multiple prototype DSTs and algorithms to support CTOP-based decision making by traffic flow managers. Some tools were designed as enhancements to CTOP to improve FCA management, while others were stand-alone analytic and modeling tools to assess predicted demand, capacity, TOS, FCAs, and other CTOP-related parameters.

N17) Hodell, G., Yoo, H. Brasil, C., Buckley, N., Gabriel, C., Kalush, S., Lee, P. U. and Smith N. M. Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program. DASC 2018.

- View N17 Summary/Relevance (Click to Expand/Collapse)

O5) Kaler C., Hall W., Brinton C., Fernandes A., and Hunter G., Collaborative Trajectory Options Program within the NAS Flow Advisory Manager. AIAA Aviation Forum, 2018.

O6) Hall W., Capozzi B., Hunter G., M. Klopfenstein, and Klein S.,  Development and Analysis of Decision Support for the Collaborative Trajectory Options Program (CTOP). AIAA Aviation Forum. 2018.

O7) Final Report on Decision Support Tools Mosaic-ATM NRA: Collaborative Trajectory Options Program: Modeling, Decision Support, Optimization and Simulation. NRA 2019.

O8) Final Report Mosaic-ATM NRA: Collaborative Trajectory Options Program: Modeling, Decision Support, Optimization and Simulation. NRA 2019.

O9) Final Report on Decision Support Capabilities Metron NRA: Collaborative Trajectory Options Program: Modeling, Decision Support, Optimization and Simulation. NRA 2019

CTOP Use Cases

O10) Smith, P., Evans, M., Spencer, A., Hoffman, R., Myers, T., Kicinger, R., Hackney, B. “Integrated Application of the Collaborative Trajectory Options Program” DASC 2019.

Demand modeling and capacity rate settings header
A challenge to effective strategic demand / capacity management is the ability to better model the predictive demand and capacity. MIT-LL, Metron, and Mosaic-ATM are organizations that have worked on state-of-the-art predictions for weather impacted capacity and traffic demand. They have used their expertise to explore ways to apply the best predictive capabilities in the field to IDM.

N18) Wang, Y., Prediction of Weather Impacts on Airport Arrival Meter Fix Capacity. SAE Int. J. Adv. & Curr. Prac. in Mobility 1(2):343-351, 2019.

N19) Kulkarni, D. Models of Maximum Flows in Airspace Sectors in the Presence of Multiple Constraints. DASC 2017

- View N18/N19 Summary/Relevance (Click to Expand/Collapse)

O11) Tereshchenko, I. and Hanson, M. Causal Demand Modelling for Applications in En Route Air Traffic Management. ATM Seminar 2019.

O12) Jones, J. C., DeLaura, R., Pawlak, M., Underhill, N. and Troxel. S. Predicting & quantifying risk in airport capacity profile selection for air traffic management. In the 14th USA/Europe Air Traffic Management Research and Development Seminar (ATM2017), Seattle, WA.

O13) Jones, J. C. and DeLaura, R. Methods for Planning Airport Acceptance Rates. In 17th AIAA Aviation Technology, Integration, and Operations Conference, Denver, CO, 2017.

O14) Jones, J. C., DeLaura, R., and Glina, Y. Learning Airspace Flow Rates through Fast-time Simulation. In 18th AIAA Aviation Technology, Integration, and Operations Conference, Atlanta, GA, 2018.

O15) Jones, J. and Glina, Y. Estimating Flow Rates in Convective Weather: A Simulation-Based Approach. In 13th USA/Europe Air Traffic Management Research and Development Seminar., Vienna, Austria, 2019.

O16) Hoffman, R., Hackney, B., Wei, P. and Zhu, G. Enhanced Stochastic Optimization Model (ESOM) for Setting Flow Rates in a Collaborative Trajectory Options Program (CTOP).  AIAA Aviation Forum. 2018

O17) Zhu, G., Wei, P., Hoffman, R., Hackney, B. Aggregate Multi-commodity Stochastic Models for Collaborative Trajectory Options Program (CTOP). ICRAT 2018.

O18) Zhu, G., Wei, P., Hoffman, R., Hackney, B. Centralized Disaggregate Stochastic Allocation Models for Collaborative Trajectory Options Program (CTOP). DASC 2018.

O19) Zhu, G., Wei, P., Hoffman, R., Hackney, B. Saturation Technique for Optimizing Planned Acceptance Rates in Traffic Management Initiatives. IEEE Intelligent Transportation Systems 2018.

O20) Zhu, G., Wei, P., Hoffman, R., Hackney, B. Risk-hedged Multistage Stochastic Programming Model for Setting Flow Rates in Collaborative Trajectory Options Programs (CTOP). AIAA Science and Technology Forum and Exposition 2019.
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Last Updated: May 6, 2021