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NEXTOR - National Center of Excellence for Aviation Operations Research 1 of 29
Sample Decision Support ATM Pr ojects
Integration of National Airspace System Models
Modeling NAS Infrastructure Investments
J. Kobza, A.A. Trani, H.D. Sherali, H. Baik and C. Quan
Sponsor FAA Office of Program Analysis and Investment
(Dr. Randy Stevens)
NEXTOR - National Center of Excellence for Aviation Operations Research 2 of 29
Integration of National Airspace System Models
Project Objectives
• To investigate alternatives to integrate existing National Airspace System models as a suite of decision making tools
• To connect new algorithms with existing NAS airspace and airport simulation models (e.g., SIMMOD, RAMS, etc.)
Status of the Project:
• An object-oriented programming mechanism to connect to the architecture of both models with external processes is under development
• The approach will be demonstrated next using a generic ground network simulation capability to extend RAMS
NEXTOR - National Center of Excellence for Aviation Operations Research 3 of 29
Integration of Airspace/Airfield Models to External Processes
• To demonstrate the integration process RAMS and SIMMOD are being used as testcase
+ RAMS - The Reorganized Airspace Mathematical Simulator
+ Airspace operational model developed by Eurocontrol to simulate advanced airspace concepts
+ SIMMOD - the FAA airfield and airspace model
• Curiously, none of these models was designed with a plug-in architecture in mind (SIMMOD designed in the 1970s and RAMS in the 1980s)
• Both are NARIM tools
• RAMS lacks detailed ground simulation capabilities (perceived as weakness by many)
NEXTOR - National Center of Excellence for Aviation Operations Research 4 of 29
Integration Appr oach
• Develop an integration procedure to link dynamic events into RAMS and SIMMOD
• Ground simulation component is the common element for the integration procedure (RAMS has little ground logic)
RAMS SIMMODRunway Object
VPI Object-OrientedGround Model
(NodeA and B events)
Mimics Ground ATM
Runway Ops. Functions
Area of Interest
NEXTOR - National Center of Excellence for Aviation Operations Research 5 of 29
Object-Oriented Ground ATM Model
Point_Class
SP_Class
Controller_Class
Message_obj
Node_Class
Link_Class
Flight_Class
Aircraft_Class
AirpNwk_Class
AcftFoll_Class
NEXTOR - National Center of Excellence for Aviation Operations Research 6 of 29
Model F eatur es
• Continuous (or discrete-event) micro-simulation model
• The total delay due to network congestion can be analyzed
• Implements an “aircraft-following” model
+ The dynamic behavior of moving aircraft can be captured
• Communication delays and frequency congestion is incorporated
• Continuous updating the shortest path (Quasi-Dynamic)
+ More realistic results in reasonable computation time.
• Ground control model attempts to produce a continuous dynamic equilibrium
• Three types of data structures (Array, Linked-list, Mixed) depending on the data type
NEXTOR - National Center of Excellence for Aviation Operations Research 7 of 29
Departur e State Transition Diagram
No
(until initial climb rateand speed)
(with const. accel.until flying speed)
yes
No
Parked(at gate)
Gate Hold
Pushing-back
CommunicationGet
Cleance?
Communication Taxing
Rolling
AreaHolding(Holding
Area)
Waiting toTaxi
Waiting inLine
(R/W dep.Queue)
Waiting onRunway
CommunicationLiftOff
Call for push-backclearance
Call forTaxi
Clearance
Call forTake-offclearance
Get take-offClearance
Waiting toTaxi
Got Cleance?
noclearance
SendingConfirm.
Recieving(Control)Message
Waitingfor
Response
Sending(Request)Message
Communication (pilot standpoint)
NEXTOR - National Center of Excellence for Aviation Operations Research 8 of 29
Contr oller State Transition Diagram
StandbyReceivingRequestMessage
ProcessConflictSituation
Waitingfor
Confirm
SendingControl
Message
Proc.Oper.
Const's
Standby
SendingControl
Message
Waitingfor
Confirm
ResolvingConflict
Checkingit again
SendingControl
Message
Waitingfor
Confirm
NEXTOR - National Center of Excellence for Aviation Operations Research 9 of 29
Air craft-F ollo wing Logic (second-order model)
Distance control logic
Speed control logic
Distance-speed control logic
][ 11tn
tn
ttn xxx +
∆++ −= &&&& α lt
ntn
mttn
xx
xCwhere
][
)( ,
1
1
+
∆++
−= &α
max1max1 then , If xxxx ttn
ttn &&&&&&&& => ∆+
+∆+
+
)( 11tn
tn
ttn xxkx +
∆++ −= &&&& max1max1 then , if xxxx tt
ntt
n &&&&&&&& => ∆++
∆++
distancesafety parameter,design where],)[( 11 ==−−= +∆+
+ DkDxxkx tn
tn
ttn&&
NEXTOR - National Center of Excellence for Aviation Operations Research 10 of 29
Collision Detection at Intersections
15.6(sec)
the current operation direction
20.8(sec)
30.7(sec)
Start pointof Intersection
Current positionof flight
Expected arrival time to the startpoint of the common intersction
(ETi)F
2
Legend :
First flight on this link
(Need to check
the potential collision) Conflicting flights comingtoward thecommon intersection
Second or later flights on this link(do follow the leading flight
by car-following logic)
NEXTOR - National Center of Excellence for Aviation Operations Research 11 of 29
Intersection Resolution Algorithmswhere, AG: Acceptable Gap ET: Expected arrival time to the intersection P: Priority of a certain flight
Pthis == High
For all first flights on the backward stars of this flight's next node
Yes
|ETthis - ETconf| < AG
Exist conflict flighthaving Pconf (= High)?
Yes
ETthis >= ETconf
Yes
No
Yes(then FIFO)
|ETthis - EThigh| < AG
Yes
Exist conflict flighthaving Pconf (= High)?
ETthisnew = EThigh + AG
|ETthis - ETconf| < AG
Exist conflict flighthaving Pconf (= LOW)?
No
Yes(then slow down)
No
Yes
No
Yes
Yes
No
No
No
No
Every delta seconds?
Yes
Yes
Is this the first aircraftin the Link?
NEXTOR - National Center of Excellence for Aviation Operations Research 12 of 29
Sample Air port Netw orks
5000ft
M21
M1
C1-32
E1-15
F1-11
L1-10
G1-16
K1-19
H1-18
M2-6
M13-20
B1-22
M7-12
5000ft
ORDDCA
NEXTOR - National Center of Excellence for Aviation Operations Research 13 of 29
Sample Data Files
File 1: Node Data
1. NodeId
2. NodeType (Gate/Taxiway/Runway)
3. TT
4. Restriction
File 2: Link Data
1. FromNode
2. ToNode
3. LinkType
4. LinkId
5. Restriction
6. Direction
File 3: Aircraft Model
1. Model_Id
2. Wingspan
3. Length_f
4. MaxAccel
5. Etc.
File 4: Flight Plan
1. Flt No.
2. Acft_Type
3. Flt_Type
4. S_Time: Start time
5. O_Node: Origin Node
6. D_Node: Destination node
7. etc
File 5: MinPath Matrix
from\to 0 1 2 3 4 5
0 0 1 1 3 3 3
1 -9999 0 2 -9999 4 4
2 -9999 -9999 0 -9999 -9999 5
3 -9999 -9999 -9999 0 4 4
4 -9999 -9999 -9999 -9999 0 5
5 -9999 -9999 -9999 -9999 -9999 0
NEXTOR - National Center of Excellence for Aviation Operations Research 14 of 29
Rele vance of the Integration
• RAMS is the subject of three integration processes to enhance its capabilities as a decision support tool (our viewpoint)
+ RAMS-OPGEN
+ RAMS-Weather
+ RAMS-Ground_Sim
• While moderate gains in airspace capacity might be possible in NAS under various future Concept of Operations the airport capacity limits of the system remain a critical ATM issue
NEXTOR - National Center of Excellence for Aviation Operations Research 15 of 29
Modeling N AS Infrastructur e In vestments
Research Objectives:
• Develop a modeling framework to estimate NAS investments, their effects in NAS operational metrics (delay, capacity, etc.) and macroeconomic impacts
Approach:
• A macroscopic modeling framework to assess the development of NAS has been postulated using Systems Dynamics
• Backbone of the modeling framework has been developed
• Implementation of the framework using State-Flow1 is in progress (proof-of-concept)
1.State-Flow is a simulation environment developed by the Mathworks in Natick, MA.
NEXTOR - National Center of Excellence for Aviation Operations Research 16 of 29
Investment Models Cycle (NARIM)
• A conglomerate of models (belonging to three specific domains - per Odoni et al., 1996):
+ Operational models (RAMS, SIMMOD, ACM, etc.)
+ Architectural models (NASSIM - National Airspace Systems Simulation)
+ Investment analysis modeling
Airline Schedule
Demand Function
NAS Resource Simulator
LOS = f (Safety, Delay,etc.)
Cost/BenefitAnalysisSocio-Economic Model
Optimization
OperationsConcept of
Critical F eedback
NEXTOR - National Center of Excellence for Aviation Operations Research 17 of 29
Complementary Approach to NARIM
• We are not trying to create a new NARIM
• The framework being investigated could be used (if successful) as an integration approach that binds NARIM models together
• The difficulty with the current approach in NARIM is that each case scenario needs to be studied independently and models run individually
• The Systems Dynamics approach proposed uses a response curve approach coupled with a dynamic model to ‘encapsulate’ agent behavior researched elsewhere (i.e., ATM Group # 1)
• The approach tries to answer questions without running each model every time
NEXTOR - National Center of Excellence for Aviation Operations Research 18 of 29
Systems Dynamics Variables (STELLA II TM Repr esentation)
• Level variables - represent accumulation of resources (also called state variables of the system)
• Rate variables - represent the rates of change of level variables
• Auxiliary variables - other variables used to estimate rate variables
Air Transport Demand
ATD Rate of Change
Population
GNP
Level of Service
Multiplier
ATS Capacity
FeedbackLoop
NEXTOR - National Center of Excellence for Aviation Operations Research 19 of 29
Issues in Modeling NAS Agents
• Level of aggregation
+ NAS agents can be modeled individually or using some level of aggregation
+ The level of aggregation in modeling is very critical
• Who should be considered?
+ FAA (infrastructure facilities such as centers, sectors, CNS assets; staff,etc.)
+ Airlines (fleet assets, gaming strategy)
+ Users (the basis for air transport demand)
• Modeling agent dynamics
+ Part of NEXTOR’s challenge is to research how agents respond to short and long term system changes
+ Agent dynamics are being studied concurrently
NEXTOR - National Center of Excellence for Aviation Operations Research 20 of 29
Modeling Framework Structure
Air Transportation System Model
Economic Model
Air Transportation System Metrics
Safety Capacity Delay Load Factor
ATC Airports Airlines
Investment Strategy Population, Labor, GNPyields Demand
When?, How much?What?, Where?
NEXTOR - National Center of Excellence for Aviation Operations Research 21 of 29
Example of Airline Asset Modeling
• The following example illustrates in a very simplified way how an airline might add or retire fleet assets
Aircraft Fleet
New Aircraft Retired Aircraft
Load Factor
Airline Market Share
Seat CapacityTotal Capacity
Fleet Age
AT Demand
Retiring Acft NormalDesired Fleet Age
In practice the aircraft fleet level variable could be replacedby n levels representing various aircraft age groups
From EconomicModel
NEXTOR - National Center of Excellence for Aviation Operations Research 22 of 29
Example of FAA Resources ModelingStaffing Level
New staff
Retirements
Traffic Demand
Frac to Sur
Comm Tech multiplier
FAA Unit Level Capacity
Communication Assets
Comm Proc Comm Ret
Frac to Staff
Surveillance Assets
Sur Proc Sur Ret
FAA UL Budget
Frac to Comm
Sur Tech multiplier
Level of Service
From EconomicModel
NEXTOR - National Center of Excellence for Aviation Operations Research 23 of 29
US Socio-Economic Development Model (SEM)
• The model includes the following relevant variables from the following sectors:
+ Industrial
+ Infrastructure (includes transportation)
+ Social
+ Population
• Predicts socio-economic activity over time
• Predicts demand functions (regional, city pair, national, etc.)
• Ties in with air transport model (described in the following pages)
• An adaptation of ACIM can be made to link traditional economic parameters (GNP) and the technical model
NEXTOR - National Center of Excellence for Aviation Operations Research 24 of 29
US Air Transportation System Development Model (ATSDM)
• A technology model to predict air transport specific variables consists of the following sectors:
+ Air Traffic Control
+ Airlines
+ Airport
• Tracks airport infrastructure, air fleet, and ATC assets over time
• Contains the dynamics of agents evolve as system state variables change
• Uses the results of SEM (economic model)
NEXTOR - National Center of Excellence for Aviation Operations Research 25 of 29
Why is Feedback Modeling Important?
• Traditional NAS metric predictions are based on a point performance model (base and horizon years)
• Agents (FAA, airlines, users, and the economy itself) exhibit various degrees of adaptive behavior that are impossible to capture with such a model
• Feedback occurs naturally as these agents adapt over time and thus a dynamic model offers an interesting alternative (as long as we know how this adaptation behavior occurs)
• A dynamic model is capable of responding to external inputs as the model evolves (gaming is possible with such a model)
• Life cycle analysis is a secondary benefit of a dynamic model without gross assumptions in evolution
NEXTOR - National Center of Excellence for Aviation Operations Research 26 of 29
Model with Explicit Capacity Formulation
AT Demand
ROC ATD
Revenue
Investment to AT
Frac of Rev to Investment
AT Infrastructure Const
Capacity of AT System
ROC Capacity Unit Cost of Capacity
AT Sys Attractiveness
Demand Capacity Ratio
PopulationROC Population
Captivity Factor
Jobs
System Delays
Region Attractiveness
Taxes
Cost
Third OrderFeedback System
NEXTOR - National Center of Excellence for Aviation Operations Research 27 of 29
Scenario Analysis
• Scenarios of interest to air transportation service providers, users and FAA can be constructed and analyzed
• The proposed modeling framework can be used to understand important investment strategies for NAS and how these affect NAS metrics
+ Level of ATC automation
+ Level of airline investments and return
+ Free flight impacts on NAS metrics
+ Levels of investment needed
• Perhaps most important is the question when to invest to achieve any benefit on investment (the classical ROI airline and FAA question)
NEXTOR - National Center of Excellence for Aviation Operations Research 28 of 29
Expected Results of the Model
• Notional patterns for various NAS capital investment policies
Year
Nominal
(0.80) Nominal
(1.2) Nominal
Nominal
(0.80) Nominal
(1.2) Nominal
NEXTOR - National Center of Excellence for Aviation Operations Research 29 of 29
Closing Remarks
• Systems Dynamics is just one method to understand ties between variables of social, economic and technological order in complex dynamic systems like NAS
• A properly developed and calibrated model of NAS behavior could serve as a living laboratory where FAA can examine investment policies before implementation
• The approach presented here is a first order modeling effort that complements the our understanding of the NARIM umbrella of models
• While it can be argued that establishing causality between technology and economic variables is difficult it is important to realize that many concurrent (or previous studies and models) can be useful to sort out these details