Airport Operations Analysis
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1 Airport Operations Analysis Using Fast-Time Simulation Models Dr. Alexander Klein Jianfeng Wang Stephen Szurgyi CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH
2 GMU Airport Operations Research Capacity Safety Economic Analysis Wide range of models & tools Stochastic Simulation Identifying best solution in a fastest way 99% Confidence Intervals Bag Throughput Bag Throughput #1 #2 #3 #4 #5 Equipment Solutions Single run #1 #2 #3 #4 #5 Equipment Solutions Multiple runs 2
3 LaGuardia Airport Upgauging Problem One of nation s most congested airports Slot controlled Large proportion of regional jets (RJs) FAA and NYPA looking for ways to increase pax throughput from 25 to 30M / year without increasing congestion Proposed action Incentivize air carriers to upgauge to larger aircraft Question posed to research community Would upgauging negatively affect LGA operations/delays? Must look at Runway, Taxiway and Gate capacity 3
4 Simulation Scenarios RWY Config Day Fleet mix A22/D (ETMS) (GRA) (ETMS) Baseline 12% less RJs 25% less RJs Baseline Upgauged (50% less RJs) Upg (757s to A321s) Baseline 12% less RJs 25% less RJs 4 A22/D (ETMS) (GRA) (ETMS) Baseline 12% less RJs 25% less RJs Baseline Upgauged (50% less RJs) Upg (757s to A321s) Baseline 12% less RJs 25% less RJs
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7 Summary of Results Run Config Schedule Number of Flights in TAAM Fleet Mix Total Available Seats per Day Avg Seats per Flight Est'd Annual Pax excluding GA 16 ETMS 1222 Baseline ,036, USExp,EGF,COM /11/ % RJ to NB ,796, USExp,EGF,COM % RJ to NB ,953, USExp,EGF,COM Avg Delay per Flight Avg Delay per Seat Average Standoff Time per Flight Est'd Peak- Time Gate Shortage Carriers Most Affected by Gate Shortages Minutes 1 ETMS 1206 Baseline ,795, None 6/14/ % RJ to NB ,461, None (filled to 3 75 ops/hr) % RJ to NB ,292, None A22/D Baseline ,810, None (Peak 5 sustained GRA Upgauged ,523, COM, EGF, USA 6 AAR/ADR = 39.5) 1190 Upg, 757to ,051, COM, EGF, USA 7 ETMS 1222 Baseline ,036, USExp, COM 8 8/11/ % RJ to NB ,796, USExp,EGF,COM % RJ to NB ,953, USExp,EGF,COM 10 ETMS 1206 Baseline ,795, COM, EGF 6/14/ (filled to % RJ to NB ,461, COM, EGF 12 A22/D31 75 ops/hr) % RJ to NB ,292, COM, EGF 13 (Peak 1178 Baseline ,810, None 14 sustained GRA Upgauged ,523, COM, EGF, USA AAR/ADR Upg, 757to ,051, COM, USA, EGF = 38)
8 Gate Activity (TAAM Simulation) - excerpt Example: A22/D13, 04/19 (GRA) Upgauged gtctba am 10 am 2 pm 6 pm 10 pm x x x x gtctba03 COA x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x.... gtctba x x. x x.... x x x x x x... x x x x.... gtctba x x x x x x x x. x x x x x x x.. x x x x x gtctba06 ACA x x.. x x x x. x x x x x x x x x. x x... x x. x x. x x.. gtctba x x x.. x x.. x x x x x x x gtctbb01 MDW x x.. x x x... x x.. x x... x x. x x.. x x x x x.... gtctbb02 COA x x x x x. x x x x x x x x x x x.. x x x.. x x x x x x x x. x x. gtctbb03 MDW x x x x x x x x x. x x x x x x.. x x x x x x x. x x x. x x x... gtctbb04 ATA/FFT x x x x x x x x x x x x x x. x x x x x x x x x x x x x x x x x x x x x. gtctbb x x x x x x... x x.. x x x.. x x x x x... x x x x x.. x x.. Spirit, JBU gtctbb x x x x x x x x x x x x x x x x.. x x. x x x x.. x x x x x x x x x x gtctbc x x x x x x x. x x x x x. x x x. x x x.. x x. x x x x x.... AAL/EGF gtctbc x x x x x x x x x. x x x x x x x x x x x x. x x x x. x x x... gtctbc Gate x x x x x x x... x x. x x... x x x... x x. x x x x x x x x. gtctbc utilization..... in.. x x x x x.. x x x x.. x x x x x x x x x x... x x x x.. x x.. gtctbc08. UAL.... TAAM... is. only x x... x x. x x.. x x x. x x... x x gtctbc an x x.. x x.. x x x. x x x x x x x... x x x x x. x x x x.... gtctbc approximation x x x x gtctbc of. a. real.. day x x... x x x x. x x x x x gtctbc of. operation x x x x x x x x x x. x x x x x x x x x x x x x.. x x x x x x x... gtctbd x x x x x x. x x x x x x. x x x.. x x. x x x x x x. x x x x... gtctbd This.. plot.. is. a.. x x x x x x x x x x x x. x x... x x x... x x x x x x x x x x.. gtctbd simplified x x x x x x x x x x x x x x x. x x x. x x.. x x. x x x x x... gtctbd representation x x x x x x x. x x x x.. x x x.. x x.. x x x x x x x..... gtctbd05 AAL/EGF..... of. continuous x x x x x x x x... x x x x.. x x x.... x x.. x x x.... gtctbd gate x x x x x x x x x x.. x x x x x... x x x x x x x gtctbd occupancy x x x x x x x x x x. x x. x x gtctbd x x x x x x x x x. x x. x x... x x.. x x... x x x x..... gtctbd x x x x x x x. x x x x x x x x x x. x x x. x x x x x x x x x x x x x.
9 Average Delay & Standoff Time / Flight 04/19/05 Sample (GRA) A22/D13, 04/19 (GRA 2005) A22/D31, 04/19 (GRA 2005) 3 Avg Delay / Flight, mins Avg Standoff Time / Flight 3 Avg Delay / Flight, mins Avg Standoff Time / Flight Delay Standoff Time Delay Standoff Time Baseline Upgauged Upg, 757to321 Baseline Upgauged Upg, 757to321 9
10 Understanding Differences in Delays Impact of Schedule Lumpiness (1 of 2) ARR DEP 20:00 21:00 22:00 23:00 20:00 21:00 22:00 23:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 4:00 22:00 5:00 23:00 6:00 0:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 4:00 4:00 5: ARR DEP Demand - Base Demand - Upgauged Delays - Base Delays - Upgauged Standoff (secs) Gate (secs) Taxiway (secs) Sequencing (secs) Runway (secs) 23:00 5:00 0:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 4:00 5:00
11 Understanding Differences in Delays Impact of Schedule Lumpiness (2 of 2) Same schedule, larger fleet: delay increase is small Modified, lumpier schedule, larger fleet: delay increase is noticeable A22/D13, 0614 (2004) A22/D31, 0614 (2004) A22/D31, 04/19 (GRA 2005) 3 2 Avg Delay / Flight, mins Avg Standoff Time / Flight Avg Delay / Flight, mins Avg Standoff Time / Flight Avg Delay / Flight, mins Avg Standoff Time / Flight 4.0 Delay Standoff Time Delay Standoff Time Delay Standoff Time Baseline 12% RJ to NB 25% RJ to NB Baseline 12% RJ to NB 25% RJ to NB Baseline Upgauged Upg, 757to321 11
12 Stochastic Simulations Departure time, aircraft performance randomized At congested airports like LGA, small variations in traffic demand or throughput can lead to large fluctuations in delays LGA Movements Example for 04/19 schedule, upgauged, A22/D31 runway configuration, is shown 12 LGA Delays
13 Average Delay Per Flight (04/19, GRA) 20 Randomized TAAM Runs for the 6 Scenarios 3 LGA Avg Delay Per Flight - Six Randomized Multi-Runs (20 Iterations Each) Primary parameter randomized is departure time 3 Delay per flight, minutes A22D13 Base A22D13 Upgauged A22D13 757to321 A22D31 Base A22D31 Upgauged A22D31 757to Run # 13 Comparison with actual data: average delay/flight at LGA was 19.5 min in Aug 2005
14 LaGuardia Upgauging - Discussion Upgauging does not lead to significant delay increase if schedule lumpiness can be mitigated At peak-demand times, LGA can be short of about 3-4 gates Gate shortages are highly local (carrier and time specific) Some un-used gates are always present, even at peak times Optimized scheduling is key to reducing delays at a given demand level Fine-tuning through simulation Combined simulation of aircraft and passenger/bag movement at LGA is highly desirable 14
15 Fast-Time Simulation Enables Broader Analyses E.g. TAAM as a Carrier, Platform for Other Tools & Models Airport & airspace design tools Noise analysis TAAM simulation Scenario generation for real-time simulators Cost / benefit analysis Passenger and baggage flow simulation 15 Conflict analysis Capacity statistics
16 CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH
17 CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH
18 IAD: Delay/Utilization Tradeoff Security Lane Utilization Comparison Between Baseline and DSS with Modified Schedule Avg open Modified Avg open SM DSS-Modified Average Passenger Delay :18: :00-04:14 04:30-04:44 05:00-05:14 05:30-05:44 06:00-06:14 06:30-06:44 07:00-07:14 07:30-07:44 08:00-08:14 08:30-08:44 09:00-09:14 09:30-09:44 10:00-10:14 10:30-10:44 11:00-11:14 11:30-11:44 12:00-12:14 12:30-12:44 13:00-13:14 13:30-13:44 Average Passenger 14:00-14:14 Delay 14:30-14:44 15:00-15:14 15:30-15:44 16:00-16:14 16:30-16:44 17:00-17:14 17:30-17:44 18:00-18:14 18:30-18:44 19:00-19:14 19:30-19:44 20:00-20:14 20:30-20:44 21:00-21:14 21:30-21:44 22:00-22:14 22:30-22:44 23:00-23:14 23:30-23:44 Opening additional 6-8 lanes reduces wait time during off-peak periods Time Period 0:15:59 0:13:19 0:10:39 0:08:00 0:05:20 0:02:40 0:00:00 0 Utilization (%) 05:00-05:14 05:45-05:59 06:30-06:44 07:15-07:29 08:00-08:14 08:45-08:59 09:30-09:44 10:15-10:29 11:00-11:14 11:45-11:59 12:30-12:44 13:15-13:29 14:00-14:14 14:45-14:59 15:30-15:44 16:15-16:29 17:00-17:14 17:45-17:59 18:30-18:44 19:15-19:29 20:00-20:14 20:45-20:59 21:30-21:44 22:15-22:29 23:00-23:14 23:45-00:00 SS DSS DSS-Modified Time Period
19 From Individual Models to Toolsets From NAS-wide effects to individual airport models First-class simulation tools Stochastic modeling: scientific approach to using fast-time simulation models Plug-ins and data analysis tools built at GMU Air traffic analysis (capacity/demand/procedures/expansion), safety analysis, and economic analysis go hand-in-hand Use of sophisticated tools and models to educate the next generation of model users and analysts 19
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