NASA/TM–2009-215383

Surface Management System Departure Event

Data Analysis

Gilena A. Monroe

Ames Research Center, Moffett Field, California

September 2009
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NASA/TM–2009-215383

Surface Management System Departure Event

Data Analysis

Gilena A. Monroe

Ames Research Center, Moffett Field, California

September 2009

National Aeronautics and

Space Administration

Ames Research Center

Moffett Field, California 94035-1000
Available from:

NASA Center for AeroSpace Information National Technical Information Service

7115 Standard Drive 5285 Port Royal Road

Hanover, MD 21076-1320 Springfield, VA 22161

(301) 621-0390 (703) 487-4650
iii 

TABLE OF CONTENTS 

LIST OF FIGURES ............................................................................................................................ iv 

ACRONYMS ....................................................................................................................................... v 

ABSTRACT.......................................................................................................................................... 1

INTRODUCTION ................................................................................................................................ 1

SMS BACKGROUND ......................................................................................................................... 3

SMS PREDICTION CAPABILITY ..................................................................................................... 4

A. Push-Back (OUT) Scheduling ..................................................................................................... 4

B. Runway Departure (OFF) Event Prediction ................................................................................. 5

DATA ANALYSIS............................................................................................................................... 5

A. OUT Event ................................................................................................................................... 6

Detection ....................................................................................................................................... 6

B. OFF Event .................................................................................................................................... 6

Detection ....................................................................................................................................... 6

Prediction accuracy ....................................................................................................................... 6

ASDI data source comparison ...................................................................................................... 6

RESULTS ............................................................................................................................................. 7

A. OUT Event ................................................................................................................................... 7

Overall detection ........................................................................................................................... 7

B. OFF Event .................................................................................................................................... 8

Overall detection ........................................................................................................................... 8

Overall prediction accuracy .......................................................................................................... 9

Data source comparison .............................................................................................................. 11

CONCLUSIONS ................................................................................................................................ 12

RECOMMENDATIONS .................................................................................................................... 13

A. Inclusion of Airline Data ........................................................................................................... 13

B. Mandatory Early Transponder Activation ................................................................................. 13

C. Surface Optimization ................................................................................................................. 13

REFERENCES ................................................................................................................................... 14
iv 

LIST OF FIGURES 

Figure 1. SMS system architecture. ...................................................................................................... 2

Figure 2. SMS map display, timeline, and load graph. ......................................................................... 3

Figure 3. OUT event detection performance. ....................................................................................... 8

Figure 4. OFF event detection rerformance. ......................................................................................... 9

Figure 5. OFF event prediction accuracy performance. ..................................................................... 10

Figure 6. ASDI OFF event time prediction comparison. .................................................................... 11
v 

ACRONYMS 

AOC Airline Operations Center 

AOFF Actual OFF 

AOUT Actual OUT 

ARTCC Air Route Traffic Control Center 

ASDE-X Airport Surface Detection Equipment Model X 

ASDI Aircraft Situation Display to Industry 

ATC Air Traffic Control 

ATCT Air Traffic Control Tower 

DFW Dallas-Ft. Worth International Airport 

ETMS Enhanced Traffic Management System 

FAA Federal Aviation Administration 

NAS National Airspace System 

NASA National Aeronautics and Space Administration 

SMS Surface Management System 

TFM Traffic Flow Management 

TMA Traffic Management Advisor 

TRACON Terminal Radar Approach Control 
vi 
1 

SURFACE MANAGEMENT SYSTEM DEPARTURE EVENT 

DATA ANALYSIS 

Gilena A. Monroe 

Ames Research Center 

ABSTRACT 

This paper presents a data analysis of the Surface Management System (SMS) performance of 

departure events, including push-back and runway departure events. The paper focuses on the detection performance, or the ability to detect departure events, as well as the prediction performance of 

SMS. The results detail a modest overall detection performance of push-back events and a significantly high overall detection performance of runway departure events. The overall detection performance of SMS for push-back events is approximately 55%. The overall detection performance of 

SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction 

performance for runway departure events as well as the timeliness of the Aircraft Situation Display 

for Industry data source for SMS predictions. 

INTRODUCTION 

 

Enhanced management of air traffic and airports will achieve future goals of greater safety and efficiency in the National Airspace System (NAS) as capacity and operations increase. As the NAS 

grows, decision support tools will continue to play an integral role in the future of air traffic management. These tools and their data sources should sustain a high level of information integrity, as 

they are used to make decisions regarding air traffic operations. 

NASA Ames Research Center and the Federal Aviation Administration (FAA) jointly developed a 

decision support tool known as the Surface Management System (SMS). SMS improves capacity, 

efficiency, and flexibility of aircraft movement on the surface of busy airports. In order to do so, 

SMS enables shared surface awareness that can be utilized by the air traffic control tower (ATCT), 

Terminal Radar Approach Control (TRACON), Air Route Traffic Control Center (Center), and airline personnel through the use of map displays, timelines, and load graphs. SMS provides forecasting and advisory tools to assist in the management of surface activities (ref. 1). 

Given multiple data sources, as shown in figure 1 of the system architecture, SMS detects and predicts both arrival and departure event times. These detections and predictions offer valuable traffic 

information on arrival or departure aircraft status, aircraft arrival or departure time for various reference points (e.g., spot, runway), and future arrival or departure demand. This information also assists 

air traffic controllers in performing tactical tasks such as runway assignments, as well as traffic 

management coordinators in performing strategic tasks such as runway balancing and configuration 

changes. Airline ramp controllers might also use this information in gate assignment tasks. Having 
2 

more accurate aircraft state information and reliable predictions allows for more informed decisionmaking regarding surface operations. 

As the air traffic system evolves, so will operational procedures and the tools used to perform them. 

To meet that evolution process, SMS will need to provide its users with reliable data and timely, 

reliable data sources. This must be true of the system as a stand-alone tool, as well as through integration with other air traffic management tools. 

 A previous analysis of SMS arrival events showed a significant improvement in prediction accuracy 

through integration with the Traffic Management Advisor (TMA) arrival scheduler (ref. 2). The 

results indicated that the integrated system produced more accurate runway arrival times than the 

stand-alone SMS system throughout the prediction horizon (ref. 3). No such analysis of SMS has 

been done specifically for departure events. 

The goal of this analysis was to determine the overall detection and accuracy performance of the 

SMS system as well as the timeliness and accuracy of its data sources, specifically for departure 

events. The results of the departure event data analysis presented here will help to identify areas of 

improvement in departure event detection and prediction. This analysis will also help illustrate SMS 

timing behavior with its actual event data that will be integral for its planned future integrations with 

other air traffic management tools. 

Figure 1. SMS system architecture. 
3 

This paper focuses specifically on the detection and prediction accuracy of SMS, given its data 

sources for departure events, the Aircraft Situation Display for Industry (ASDI) and the Airport Surface Detection Equipment Model X (ASDE-X). ASDI allows near real-time air traffic data to be distributed to the aviation industry while ASDE-X provides position and identification information of 

aircraft on the airport surface, as well as within 5 miles of the airport. This analysis used recorded 

traffic data from the Dallas/Ft. Worth International Airport (DFW). A general description of the 

push-back, or OUT, event detection capabilities and OUT event scheduling assistance capabilities 

will be provided. A description of the data sources for runway departure, or OFF event detection and 

OFF event time prediction will also be included. A comparison of the ASDI data source’s OFF time 

prediction performance and the actual SMS OFF time was completed and the results will be presented. 

SMS BACKGROUND 

SMS is a decision support tool that manages aircraft on the surface of busy airports and within the 

terminal airspace. SMS enables shared situation awareness between both airline and FAA facilities. 

SMS also provides surface predictions and advisories to assist in the management of surface operations and to enable air carriers and air traffic control (ATC) coordination (ref. 4). 

SMS consists of three primary toolsets to assist users. The first of these are the Controller Tools that 

use map displays, timelines, and load graphs to provide air traffic control tower and airline ramp 

controllers with flight-specific information, as well as tactical advisories for efficient traffic operations (see figure 2). The second set of user tools are the Traffic Manager Tools that provide ATC 

Tower, TRACON, and Center managers with predictions of future demand in order to strategically 

plan future airport surface operations. The final set of tools are the NAS Information Tools that provide surface demand predictions to NAS users for improved decision making. 

Figure 2. SMS map display, timeline, and load graph. 
4 

Research activity and testing of Build I SMS, a grouping of SMS capabilities, has been conducted in 

both simulation and operational environments. In 2001 and 2002, simulations were conducted to 

assess the system information requirements (ref. 5). In 2003, an operational evaluation to assess traffic management coordination usage of SMS was conducted at Memphis International Airport. Shadow testing of the traffic management functions of SMS at the Memphis Airport in 2004 assessed 

local and ground controller use of SMS. The results indicated controller acceptance of the display 

tools, as well as the accuracy of the displayed data on SMS (ref. 6). The results also showed the need 

for improved prediction accuracy (ref. 1). 

Currently, SMS is fielded in various locations throughout the nation. It is operational at the both the 

Federal Express and Northwest Airlines Ramp Towers at the Memphis International Airport as well 

as at the United Parcel Service facilities at Louisville International Airport. It is also installed at the 

center tower of the DFW airport to support NASA research. SMS is will also be used in future 

NASA research as an engineering testbed for surface simulations. 

SMS PREDICTION CAPABILITY 

Using multiple data sources, SMS possesses predictive capabilities to assist in the scheduling and 

routing of aircraft and in the allocation of airport resources. The predictions assist in the generation 

of advisories, which are used for managing the airport surface flow (ref. 4). SMS is able to assist airline operators in scheduling push-back events to improve flight prioritization, as well as predict OFF 

events. 

A. Push-Back (OUT) Scheduling 

The OUT time delineates when an aircraft pushes back from its gate. Currently, flights are authorized to push back from the gate when they notify the ramp controller that they are ready for pushback. SMS predicts the amount of taxi delay that each aircraft may incur once it has pushed back 

and can advise ramp controllers to modify their push-back procedures to minimize the total taxi 

delay of the aircraft. 

Accurate push-back time prediction is beneficial to airport surface operations. Estimates of actual 

push-back times could allow for faster and more accurate runway departure time predictions (ref. 7). 

When SMS predicts a taxi delay, and a possibility of waiting in a departure queue when an aircraft 

indicates readiness for an early push-back, SMS may advise ramp controllers to temporarily hold the 

push-back of the aircraft. By holding the push-back time of these flights, other late operating pushback aircraft may be given priority and placed ahead of these early flights, thus improving overall 

efficiency. Aircraft wait time in taxi queues may also be minimized, which may reduce the cost to 

the airlines through reduced fuel use. 

Flight plan data containing scheduled push-back times are provided to SMS by ASDI (ref. 1). Unlike 

runway departure predictions, which receive updated aircraft track data throughout the taxi status of 

the aircraft, the push-back prediction is largely uncertain since the only input to this prediction is the 

flight plan push-back time. With the inclusion of airline data, which provides aircraft gate informa-
5 

tion, estimated push-back time, and flight status, SMS can better predict push-back times, gate arrival times, and takeoff times. Also airport terminal area activities (i.e., passenger delay, catering, and 

maintenance) introduce uncertainty in push-back times, which would greatly affect any predictions. 

Although SMS does not provide precise OUT time predictions in the absence of airline data, it is 

capable of assisting ramp operators with scheduling OUT events with the knowledge of predicted 

taxi delay. 

B. Runway Departure (OFF) Event Prediction 

The OFF time delineates the time at which an aircraft starts its take-off roll from its designated runway. The prediction time of an OFF event is calculated based on when an aircraft pushes back from 

its gate, the amount of taxi time and delay incurred, and the time spent in the departure queue. 

Depending upon the status of an aircraft, along with the availability of other data sources, SMS can 

predict OFF times for flights using three methods: Enhanced Traffic Management System (ETMS)/ 

ASDI data only (i.e., planned push-back times from the Collaborative Decision Making data stream, 

which is part of ETMS), planned push-back data as well as real-time push-back event data, or 

planned push-back data with complete real-time surface surveillance data (ref. 1). 

The large uncertainty in aircraft OFF time predictions impacts all other predictions downstream. By 

modeling the movement of the traffic that is actually on the surface of the airport and, thereby, supplying accurate taxi time estimates, SMS can further improve the time predictions of OFF events. 

These SMS-predicted OFF times can then be used to improve all other predictions and products that 

are based on predicted OFF times such as regional traffic flow management, including arrival metering scheduling. 

DATA ANALYSIS 

The data used in these analyses were taken from the Dallas-Fort Worth International Airport for 

departure events only. Data were collected over a period of five days, from January 22-26, 2007, 

during the three major departure pushes: 8:00 a.m., 11:00 a.m., and 1:30 p.m. Central Standard 

Time, when there is a higher capacity of departure operations than at other times of the day. The data 

collected from those days were recorded for one-hour time durations and were averaged over the 

five days for the reported results. The data were collected under normal traffic and weather conditions with a North departure flow configuration. No anomalies were removed from the data sets, and 

each reported value reflects the actual data collected. ASDI and ASDE-X data were used as sources 

for event detection and prediction time. Though airline data may also be used as a source for event 

detection and prediction time, it was not available when these analyses were conducted. Here, an 

accurate prediction of detection and prediction time is defined as a time error within a –1 to 1 minute 

range. 
6 

A. OUT Event 

Detection 

The accurate detection of push-back events can help to improve departure event predictions. SMS 

uses surface surveillance data to detect push-back events by examining both the position and speed 

of an aircraft while it is in the ramp area. SMS uses model output AOUT (actual OUT) data, a collection of data variables, to identify an actual OUT event of an aircraft. SMS also considers an aircraft to have pushed back when the aircraft is first detected on the movement area of the airport; 

SMS then “guesses” and assigns its gate push-back time to be the same as the time it was actually 

detected. SMS denotes these occurrences as guessed detections. This OUT event detection analysis 

determines the number of push-back events that actually occur during 15-minute intervals over 

1-hour durations. Because cooperative surveillance data, such as multi-lateration, is used in the 

detection of push-back events, this analysis gives insight into the number of aircraft that have their 

transponders on before leaving the ramp area. 

B. OFF Event 

Detection 

Just as surface surveillance data are used to detect push-back events, they are also used to detect 

runway departure events of aircraft on the surface of the airport. SMS uses model output AOFF 

(actual OFF) data, a collection of data variables, to identify an actual off event occurrence of an aircraft. SMS also considers an aircraft to have departed the runway when it is first detected, either on 

the surface or within the terminal airspace, and calculates or “guesses” the runway departure time of 

the aircraft. SMS denotes these events as guessed detections. This OFF event detection analysis 

determines the number of runway departure events that actually occur during 15-minute intervals 

over 1-hour durations. 

Prediction accuracy 

As SMS predicts the OFF time of an aircraft, it uses a timestamp from the GUIFLIGHT data, a collection of controlled and undelayed timestamps for reference points on the surface and in the terminal airspace, to provide predictions to the system. The timestamps from these data are matched with 

the model output AOFF data to identify the total number of matching timestamps between the 

GUIFLIGHT and AOFF data sets. This OFF event prediction accuracy analysis determines the overall prediction accuracy, within 1 minute of the actual event, of runway departure events. 

ASDI data source comparison 

SMS receives data input from various sources, which in turn affect the timeliness as well as accuracy of the system. SMS receives the DZ message, a transmitted departure message for all eligible 

activated flight plans, from its ASDI data source to supply the system with runway departure times 

for aircraft (ref. 8). The timestamps from the DZ messages are compared with the SMS model AOFF 

timestamps to determine the difference in minutes for the arrival of the OFF event data. This OFF 

event ASDI data source comparison analysis compares the timeliness of the ASDI data with the 

actual time of the OFF event. 
7 

RESULTS 

A. OUT Event 

Overall detection 

The data analysis was performed on the overall OUT event detection performance of SMS. When 

SMS detects that an aircraft has pushed back from the gate, via surface surveillance data, an actual 

push-back detection is identified. If an aircraft is detected on the surface after it has actually pushed 

back from its gate, SMS will “guess” its OUT time. For example, if an aircraft is scheduled to pushback from its gate at 9:15 a.m., but it is not detected on the surface until 9:20 a.m. and it is already 

taxiing, SMS will identify the aircraft as having pushed back and will “guess” the OUT time of the 

aircraft to be 9:20 a.m., even though its current location is on a taxiway. SMS assigns the current 

time for an OUT event, because there is no way to accurately back calculate an OUT time of an aircraft after it has left its gate. This is due to the uncertainty of the actions of the aircraft before it is 

actually detected. For instance, an aircraft may have been sitting in the ramp area for an extended 

amount of time or may have had to return to its gate after departing. Because the system is unaware 

of any of these potential events, a truthful OUT time calculation cannot be provided. 

Figure 3 shows the number of flights that were detected or guessed by the SMS system over three 

hours of departure pushes at the DFW airport. The data are shown in 15-minute intervals from the 

start of the departure push hour. Figure 3 also shows the percentage of actual detections made by 

SMS, not including the guessed detections. 

As the figure shows, a little more than half of the departure flights were identified as actual surface 

surveillance detections. There were no significant differences at the various rush periods. These data 

reveal room for improvement in the detection of OUT events within the SMS system, especially during heavy departure traffic times. These improvements, though, are greatly dependent upon the aircraft capabilities and the undelayed activation of transponders by pilots. 

SMS uses aircraft position and speed to detect push-back events, but there are times when SMS does 

not have this information. One major reason is pilot delay in turning on the transponder, which 

projects the aircraft position data for the surface surveillance to identify it. If the transponder is not 

turned on before the aircraft pushes back from its gate, it is possible that an aircraft does not get 

detected until it is taxiing or even until it is departing its assigned runway. By increasing transponder 

usage at the gate, OUT event detection within SMS will improve. As a result, OFF time predictions 

would be improved and lead to more efficient surface and en-route operations. 
8 

Figure 3. OUT event detection performance. 

B. OFF Event 

Overall detection 

This data analysis was performed on the overall OFF event detection performance of SMS. When 

SMS detects that an aircraft has departed its assigned runway, via surface surveillance data, an 

actual runway departure is identified. If an aircraft is detected after it has actually started its roll for a 

runway departure, SMS will calculate, or “guess,” the OFF time of the aircraft, dependent upon its 

current location. SMS will identify this as a guessed departure. For example, if an aircraft is scheduled to depart its runway at 2:00 p.m., but it is not detected until it has already left the runway, 

SMS will identify the aircraft as having departed the runway and will use the speed of the aircraft to 

back calculate its runway departure time and “guess” the OFF time of the aircraft to be 2:03 p.m., 

since its current location is beyond the runway. 

Figure 4 shows the number of flights that were detected or guessed by the SMS system over three 

hours of departure pushes at the DFW airport. The data are shown in 15-minute intervals from the 

start of the departure push hour. Figure 4 also shows the percentage of actual detections made by 

SMS, not including the guessed detections. 
9 

Figure 4. OFF event detection rerformance. 

As shown in figure 4, the results of the analysis indicate that SMS is successful in detecting runway 

departure events. For each of the 15-minute intervals over the 3 hours of operations, figure 4 shows 

100% detection, or nearly 100% detection, of the OFF events. 

Because figure 3 indicates that many transponders are not activated while still at the gate, it appears 

that they are being turned on before they reach their departing runway. The few guessed OFF events 

in figure 4 may be the result of aircraft that did not activate their transponders prior to their take-off 

roll. There were no significant differences between the various rush periods. 

Overall prediction accuracy 

The accuracy analysis was performed to determine the overall precision of the SMS event time predictions for runway departures. The prediction accuracy is determined by an error rate of no more 

than 1 minute from the actual runway departure time. Since departure events typically happen within 

a 1–11⁄2 minute timeframe, a 1- minute error rate provides high integrity to the precision of the prediction. Within the system, SMS is provided with messages from its data sources containing runway 

departure times. SMS uses these supplied departure times in its advisories and predictions that are 

displayed to the user. 
10 

Figure 5 shows the percentage of correctly predicted runway departure times by SMS, given the 

ASDI data prior to push-back and ASDE-X data after push-back. The data represent OFF time prediction accuracy for three separate departure pushes containing varying numbers of included aircraft. 

The time horizon spans from an hour prior to the actual OFF event taking place. 

Figure 5 illustrates that the farther out from the actual OFF event, SMS does not perform well in 

predicted OFF times. Until approximately 10 minutes before an OFF event, the system is constant at 

just below 10% accuracy. It is not until 10 minutes out, that prediction accuracy begins to increase, 

yet it does not reach 100% accuracy until aircraft are within approximately 1 minute of departure. 

The 10-minute timeframe before takeoff often is representative of the time taken for an aircraft to 

taxi from its gate to its departure runway at DFW. Because this is when track data are being collected for a moving aircraft, the OFF time predictions are continually updated, becoming more accurate the closer it is to the time of departure. Though inaccuracy for OFF time predictions is high 

prior to this 10-minute timeframe, this might be due to the lack of surveillance data before an aircraft 

has departed its runway. 

Figure 5. OFF event prediction accuracy performance. 
11 

Data source comparison 

The data analysis was performed on the ASDI OFF time prediction function to determine the timeliness of the ASDI data source. ASDI provides a DZ message to SMS, which supplies it with runway 

departure times. Upon receiving this message, SMS continually updates in order to output more 

recent runway departure times. To discern how quickly the ASDI data source is supplying this information to SMS, the ASDI information arrival time is compared to the actual OFF time given by 

the system. 

Figure 6 shows the difference in minutes between the ASDI DZ message and the actual departure 

time. These data are shown for the total number of OFF events that occurred during the 3 departure 

pushes at the DFW airport. 

Figure 6. ASDI OFF event time prediction comparison. 
12 

The data suggest that ASDI is providing timely runway departure times to SMS. Most of the ASDI 

DZ messages arrive to SMS within 1 minute of the system OFF time. There are outliers that mostly 

fall within the 5–6 minute range, which might be caused by data messages being missed or dropped 

out of the system during the transition from the ASDI data source to SMS. Further investigation 

would be required to evaluate this hypothesis. 

CONCLUSIONS 

It is imperative that SMS provide reliable data and timely, reliable data sources. Its detection and 

prediction accuracy, as both a stand-alone tool and an integrated tool, should be high-quality for all 

surface operations, both arrival and departure events. The goal of this analysis was to determine the 

overall detection and accuracy performance of the SMS system as well as the timeliness and accuracy of its data sources, specifically for departure events. 

The results of the analysis have shown that lack of aircraft surveillance data contributes to the low 

detection performance of SMS for OUT events, while surveillance data allows nearly perfect SMS 

detection performance for OFF events. 

Improvements in the accuracy of SMS push-back time predictions are anticipated to assist push-back 

scheduling that may aid the airline operators in reducing operating costs and improving flight prioritization. 

Although SMS OFF time prediction accuracy is better the closer an aircraft is to its departure, SMS 

can improve OFF time prediction accuracy farther out in time through improved OUT time prediction. These improvements will likely result in NAS-wide improvement in air traffic management 

scheduling predictions. 

The analysis also shows that the ASDI data source is usually getting runway departure data to SMS 

in a timely manner. 
13 

RECOMMENDATIONS 

As SMS can offer valuable decision support to its users through its detection and prediction capabilities, there are a few areas of interest that might assist in the improvement of SMS functionality. 

These recommended areas are identified below. 

A. Inclusion of Airline Data 

Airline data play an integral part in the functionality of SMS. Airline data contain parking gate assignments, updated predicted push-back time, flight priority, gate availability, and even maximum 

de-icing holdover time. SMS can use these data to more efficiently construct taxi routes on the surface and provide more accurate predictions of push-back and departure times as well as gate IN and 

runway arrival times. 

B. Mandatory Early Transponder Activation 

When the transponder of an aircraft is activated, it can be identified on both radar and collision 

avoidance systems. Without transponder activation, air traffic controllers may not see the aircraft on 

a radar screen. If all aircraft transponders are activated before they leave the gate area, SMS will not 

only show all aircraft on the airport surface on its map display, but will also improve on its detection 

of aircraft departure events, thus improving prediction accuracy of these events. Many pilots do not 

activate their transponders before leaving the gate area; it is not a required, standard procedure, possibly because the aircraft at this state are usually under airline ramp rather than ATC supervision. 

Pilots may also prefer not to activate their transponders due to desires to preserve battery life. Mandatory, early transponder activation will prove to be beneficial to SMS prediction accuracy and subsequent surface scheduling. 

C. Surface Optimization 

The optimization of surface operations can be helpful for SMS OFF time prediction accuracy. 

Through optimized operations such as departure aircraft scheduling and taxi route planning, more 

accurate data regarding aircraft maneuvers and processes will be available to SMS. This accessibility 

to more accurate data will allow SMS to provide improved advisories and predictions to its users. 
14 

REFERENCES 

1. Raytheon: CTO-05 Surface Management System (SMS). CTOD 5-24-1 Final Report, 2004. 

2. Swenson, H. N.; Hoang, T.; Engelland, S.; Vincent, D.; Sanders, T.; Sanford, B.; and Heere, K.: 

Design and Operational Evaluation of the Traffic Management Advisor at the Fort Worth Air 

Route Traffic Control Center. 1st USA/Europe Air Traffic Management R&D Seminar, Saclay, 

France, 1997. 

3. Jung, Y.; and Monroe, G.: Development of Surface Management System Integrated with CTAS 

Arrival Tool. AIAA 5th Aviation, Technology, Integration, and Operations Conference, Arlington, Va., 2005. 

4. Atkins, S.; Brinton, C.; and Walton, D.: Functionalities, Displays, and Concept of Use for the 

Surface Management System. 21st AIAA/IEEE Digital Avionics Systems Conference, Irvine, 

Calif., 2002. 

5. Walton, D.; Quinn, C.; and Atkins, S.: Human Factors Lessons Learned from a Surface Management System Simulation. AIAA 2nd Aviation, Technology, Integration, and Operations Conference, Los Angeles, Calif., 2002. 

6. Atkins, S.; Jung, Y.; Brinton, C.; Stell, L.; and Rogowski, S.: Surface Management System Field 

Trials Results. AIAA 4th Aviation, Technology, Integration, and Operations Conference, Chicago, Ill., 2004. 

7. Clow, M.; Howard, K.; Midwood, B.; and Oiesen, R.: Analysis of the Benefits of Surface Data 

for ETMS. 2004. 

8. Volpe Center Traffic Flow Management Division: Aircraft Situation Display to Industry: Functional Description and Interface Control Document. Version 5.4, 2005. 
15 
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Unclassified 22

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Subject Category: 03

Availability: NASA CASI (301) 621-0390

NASA/TM–2009-215383

21/09/2009

Ames Research Center, Moffett Field, CA 94035-1000

National Aeronautics and Space Administration

Washington, D.C. 20546-0001

Gilena A. Monroe

Surface Management System Departure Event Data Analysis

Gilena A. Monroe

This paper presents a data analysis of the Surface Management System (SMS) performance of departure events,

including push-back and runway departure events.The paper focuses on the detection performance, or the ability to

detect departure events, as well as the prediction performance of SMS. The results detail a modest overall detection

performance of push-back events and a significantly high overall detection performance of runway departure events.

The overall detection performance of SMS for push-back events is approximately 55%.The overall detection performance of SMS for runway departure events nears 100%. This paper also presents the overall SMS prediction

performance for runway departure events as well as the timeliness of the Aircraft Situation Display for Industry

data source for SMS predictions.

Air traffic management, Departure events, Airport ground operations, Surface Management Systems (SMS)

Point of Contact: Gilena A. Monroe, Ames Research Center, MS 210-6, Moffett Field, CA 94035-1000

(650) 604-2545

Technical Memorandum

Unclassified Unclassified

NASA

(650) 604-2545 Unclassified

A-090016

WBS 305295.02.07.01.10