CAPACITY LIMITS IN THE NAS How well do we understand the dynamics ? R. John Hansman, Director Massachusetts Institute of Technology International Center for Air Transportation rjhans@mit.edu 617-253-2271

Issue The US Air Transportation system is approaching a critical saturation threshold where nominal interruptions (e.g. weather) result in a nonlinear amplification of delay US and Regional Economies highly dependant on Air Transportation Business travel (stimulated by info technology) Air Freight Personal travel System is highly complex and interdependent Need better understanding of system dynamics and real constraints to guide and justify efforts to upgrade NAS Current efforts will not provide capacity to meet demand Impact of upcoming capacity crisis is not well understood

Background ATM is a human centered contract process for the allocation of airspace and airport surface resources. Current NAS has evolved over 60 years The system has significant local adaptations resulting in nonhomogeneity Airspace design Local procedures Letters of agreement Noise restrictions Site specific training (FPL = 3 years) Major operational changes were event driven, enabled by technical capability Positive radar control - Grand Canyon 1956 TCAS - Los Cerritos 1982

ATM System Current Functional Structure Aircraft State Aircraft Guidance and Navigation AC State Sensor Sector Traffic Control Vectors Clearances Planning National Flow Approved Flight Plans Handoffs Desired Loads Clearance Requests Other Aircraft States Weather Schedule Filed Negotiate Schedule of Capacities < 5 min 5 min 5-20 min hrs - day Facility hrs Execution - Tactical Level Planning - Strategic Level Airline CFMU TMU D-side R-side Pilot Planned Rates Measurement Real State Plan/Intent AOC Efficiency Throughput Increasing Criticality Level Safety Source: A. Haraldsdottir Boeing

Critical Issues 1 Capacity Limits (Schedule vs. Infrastructure) Airports Airspace Controllers Environmental Limits Noise (relates to Airport) Emissions (local, Ozone, NOX, CO2) Understanding of Current System Complexity Dynamics , non-linearity's Labor Issues Safety vs Capacity How to improve current high levels Separation Standards example

Critical Issues 2 Role of Information Technology Decision Aids vs Information Sharing (eg CDM) Centralized vs Distributed Control Impact of Structure Vulnerability/Robustness Issues Political Issues How does air transportation impact economic development Balance between local and regional interests Impact of Labor on system Transition issues

Schedule Factors Peak Demand/Capacity issue driven by airline Hub and Spoke scheduling behavior Peak demand often exceeds airport IFR capacity (VFR/IFR Limits) Depend on bank spreading and lulls to recover Hub and Spoke amplifies delay Hub and spoke is an efficient network Supports weak demand markets Schedules driven by competitive/market factors Operations respond to marketing Trend to more frequent services, smaller aircraft Ratchet behavior Impact of regional jets Ultimately, airlines will schedule rationally To delay tolerance of the market (delay homeostasis) Limited federal or local mechanisms to regulate schedule

Variable Capacity Effects 1995 Delays vs Operations

Capacity Example (50 Flights/hr)

Capacity Example (40 Flights/hr)

Capacity Example (30 Flights/hr)

Completely Connected Network = 2(N-1) Flights Hub and Spoke Network Completely Connected Network = 2(N-1) Flights (eg., 50 Airports, 98 Flights)

Fully Connected Network Completely Connected Network = N(N-1) (eg., 50 Airports, 2450 Flights)

Infrastructure Airports (Concrete) Aging Infrastructure Arrival/Departure Capacity Set by Runways/ Wake Vortex Separations Marginal Increase in Peak Capacity Available at Existing High Demand Airports (less than 40%) New Airports/Runways Politically Difficult Community opposition Gates and land-side limits Load shedding to underutilized airports occurring MHT, PVD, BWI Impact on Terminal Areas Aging Infrastructure Modernization challenge Communications, Navigation, Surveillance, Information, Software Reliability impact Labor issues

ACARS Constraint Identification

The Airport / Terminal Area System

Interactive Queuing Model BOS 22L,22R,27 Configuration

BOS Queuing Model 27/22L-22R Configuration

BOS Queuing Model 4L/4R-9 Configuration

Runway Configuration Capacity Envelops

Downstream Restrictions Ground Stops

Downstream Restrictions Local TRW

Gate Dynamics Low Predictability of Departure Demand based on Schedule

Gate Dynamics: On Gate Departure Preparation

Minimal Noise Procedures NOISIM JP Clarke MIT

3° Decelerating Approach (JFK 13L) Existing ILS Approach 3° Decelerating Approach

Safety vs Capacity The system is extremely safe but conservative Separation Standards What is the true level of criticality of the ATM? NAV, COM, Surveillance, Control Redundancy architectures vs. high integrity GPS (sole means?) ATC (TCAS) Design system/procedures for non-normal operations How do you dependably predict the safety impact of changes in a complex interdependent system? Statistics of small numbers Differential analysis limited to small or isolated changes Models?? Safety Veto Effect

FACTORS AFFECTING SAFE SEPARATION ASSURANCE SURVEILLANCE EFFECTS Position uncertainty Velocity/higher states Scan effects Contingency (unknown-unknowns) Aircraft dynamics Response dynamics

SURVEILLANCE STATE VECTOR MODELING APPROACH A modeling approach is developed to provide a framework to formalize the surveillance effects component Trend towards surveilling more aircraft states, e.g. ADS-B (enabling better control precision?) Surveillance State Vector X(t) containing uncertainty & errors X(t) at time t is given by:

TYPES OF INTENT IP(t) = generalized representation of the pilot’s intent for his a/c IC(t) = generalized representation of future behavior of the aircraft to the extent it is known or assumed by the controller IFP(t), Flight plan/clearance IE(t), Alternate procedures (e.g. emergency) IAFS(t) = generalized representation of intent programmed into the autoflight system Errors & uncertainty I(t) in controller assumptions of trajectory: IC(t) ≠ IP(t), Controller misunderstanding—controller incorrectly assumes pilot intent IP(t) ≠ IFP(t), Pilot blunder—does not follow ATC clearance IC(t) ≠ IFP(t), Miscommunication—controller gives incorrect instruction IP(t) = IE(t) ≠ IC(t), Emergency—diversion off flight plan for technical reasons

ATC Workload as a System Constraint

Local Controller Communication Workload

RUNWAY INCURSIONS BY TYPE Caused by multiple aircraft occupancy of an active runway: Taxiing aircraft crosses an active runway without clearance Aircraft taxies along active runway thinking it is a taxiway Landing aircraft slow to clear runway upon landing Landing aircraft violates LAHSO Source: ASRS Database report set, 50 most recent records of runway incursions (as of 12/30/98) 26%

INCORPORATION OF HUMAN ELEMENTS CLOSEST POINT OF APPROACH STATISTICS CONTROLLER EXPOSURE STATISTICS SURVEILLANCE DETECTION SIMMOD Pro! AIRSPACE MODEL AIRCRAFT STATES, Xi RESPONSE PILOT (i) COMMS SURVEILLANCE VIOLATION GENERATOR DETECTION MODIFIED AIRCRAFT BEHAVIOR RESPONSE

SIMMOD MODELING OF RUNWAY INCURSIONS AT LAX

DETECTION / RESPONSE RESULTS NOTE: Simulation results are for demonstration purposes only. Simulations were conducted in part using hypothetical assumptions and input data. Analysis results are strictly hypothetical. (eg.AMASS, LVLASO)

Information Technology Key roles of IT in ATM Decision Support Tools (eg CTAS, URET) Shared Information Systems (eg CDM tools, Datalink) Need to Develop Information Architectures to Support Information Sharing Flight Information Object (CONOPS 2005) Articulation of Intent Incorporation of IT will result in planned and unplanned changes in human interaction and ATM processes Roles, Responsibilities, Interaction Dynamics Need to understand the “Sociology of ATM” Pilots Controllers Dispatchers

ATM Tactical Information Architecture Supervisor Controller CCI Controller CCC Controller ATC Facility CP Pilot/Controller CP AA Pilot/Airline Pilot PA Controller/Controller PA CCI Intra-Facility Controller/Controller CCC Company Dispatcher Cross-Facility AA Airline/ATM Airline

ATM Strategic Information Architecture Tower C Airline C Tower B Airline B Airline Tower A Airline A Operations Airline Supervisor TMC Center Operations Airline Center Operations TMC Center Central Flow Control Host TRACON C TMC ARTCC C B A Supervisor TRACON B TRACON A Supervisor TMC TMC

Flight Object Organization Information from NAS User Personnel and Tools Flight As Filed Cleared Flown Object Current History Planned or Projected Information from Air Traffic Management Personnel and Tools Information from Sensors and Pilot Reports Abstraction Example from MITRE CAASD

Flight Object Interaction with the Host Baseline FFP1 FFP2 (From filed flight plan) Aircraft identifier Aircraft type Filed route Filed cruise altitude Filed cruise speed Origin Destination Scheduled departure time Estimated time en route Estimated arrival time Equipment in use Aircraft identifier Aircraft type Filed route Filed cruise altitude Filed cruise speed Origin Destination Scheduled departure time Estimated time en route Estimated arrival time Equipment in use Flight As Filed Current History Planned or Projected Computer identifier Route, altitude, and speed (cleared flight plan information and amendments as known to Host*) Current History Planned or Projected Flight As Flown Cleared Computer identifier Route, altitude, and speed Radar track position Calculated speed and direction Altitude report Radar track position Calculated speed and direction Altitude report (from Mode C) Estimated time at next waypoint (based on flight plan and position reports) Estimated time at downstream fixes (based on flight plan and position reports) *Clearances made using voice communications, such as interim altitude duration and radar vector clearances, may not be available electronically. FFP1 Timeframe FFP2 Timeframe Beyond FFP2 Timeframe (TBD) Host provides information element to flight object Host uses information element from flight object

Suggested Solutions to Capacity Shortfall Privatization, the silver bullet? May improve modernization,costs and strategic management Limited impact on capacity Re-regulation Increased Costs Peak Demand Pricing Reduced service to weak markets Run System Tighter Requires improved CNS Safety vs Capacity Trade Build more capacity Local community resistance Multi-modal transportation networks