1. Introduction
The Electronic Navigation Research Institute(ENRI) is analysing surface movements atTokyo International (Haneda) airport to createa simulation model that will be used to explore
ways of relieving airport surface congestion. To
faithfully reproduce congestion and allow us toinvestigate methods of reducing it, thesimulation requires information on the spots(gates) used by flights and turnaround times(the time elapsed between an aircraft arriving
at a spot and leaving the spot on its next flight).
Spot information is provided to ENRI for
research purposes by the Japan Civil AviationBureau (JCAB). Data are obtained from twosystems: the Spot Management and Planning(SMAP) system and the Online Data Archive
Processor (ODAP). However, these areindependent systems that derive spot data fromdifferent sources, and there are sometimes
discrepancies between them. To create realistic
simulation scenarios that can be used to
examine surface congestion, it is necessary toprocess this spot data and to resolve
automatically any discrepancies to the greatest
extent possible.
In this paper, we examine the characteristicsof SMAP and ODAP spot information andinvestigate the feasibility of using radar trackdata for spot determination. We also report on apreliminary analysis of turnaround times.

2. Data Sources and Processing
2.1 SMAP and ODAP Data
Spot data for Haneda airport are obtained
from the SMAP and ODAP systems. SMAP isan air traffic flow management subsystem thatcollects spot operational information from each
airport and provides it to air traffic flow
management-related functions. The ODAP
system is also a part of the air traffic flow
management system, but details of its function
are not publicly released.
Data from the SMAP and ODAP systems andflight plan data are provided in electronic files
for seven days bimonthly. Flight plan data
includeeachflight’s
callsign,
aircraftregistration number and aircraft type. Spot
information includes the departure spot andvarious times: EOBT (Estimated Off BlockTime), AOBT (Actual Off Block Time), EIBT
(Estimated In Block Time) and AIBT (Actual InBlock Time)1. Not all data are present for all
flights, however.

2.2 Surface Movement Radar Data
Haneda airport’s surface surveillance system
is a multilateration radar that derives theposition of an aircraft by comparing the time ofarrival of signals from its transponder atdifferent receivers. Data from the Haneda
airport radar are processed by the AutomatedRadar Terminal System (ARTS). For each
multilateration radar target, ARTS outputs atimestamp, a position and a “track number” at
one-second intervals.
ARTS data are provided to ENRI by JCAB for
the same days as SMAP/ODAP data. The
received data are “smoothed” to reduce noise,and track numbers are correlated with otherdata to allow radar targets to be associatedwith flight plan, SMAP and ODAP records.

2.3 Determining Spot from Radar Data
For an aircraft to be detected and identified
by a multilateration radar system its
transponder must be switched on. Aircraft
operators have been requested to turn on thetransponder while taxiing at Haneda airport2.
For departures, the transponder is typicallyswitched on just before the aircraft is pushed
back from the spot, and so the initial position of
the radar track should be close to the spot.
Off block time is the time at which an aircraft
departs the spot; in block time is the time at which
an aircraft arrives at the spot.
2 However, since the surface movement radarsystem is not formally used operationally, this is notmandatory.
12.3.1 Calculating Turnaround Times
The SMAP system provides records of AOBT
and AIBT for each spot, along with flight plandetails including callsign and aircraft
registration. Since the callsign changes
between flights, corresponding block in andblock out records must be matched by aircraft
registration number. Turnaround times foreach spot are calculated as follows:••

For each aircraft registration number,collect AIBT/spot and AOBT/spot tuples
and order them by time.
For each pair of AIBT and AOBT in timeorder, if the spot is the same then computethe time difference AOBT–AIBT and add it
to the list of turnaround times for that spot.
(The block-in and block-out spots may differ
if the aircraft has been towed.)

3. Analysis
3.1 Flight Categories
Different types of flights at Haneda airportpark at different areas and follow different
patterns of operation. For this analysis, flights
departing Haneda airport were categorised asfollows:

Domestic airline
passenger carriers.
Foreign airline
carriers.

JapanesescheduledForeign scheduled passenger

Domestic registration Japanese registeredaircraft, non-airline (‘JA’ callsign prefix),
including coast guard aircraft and businessjets.
Government Japanese government flights,mostly navigation aid calibration aircraft.
Other Mostly foreign registered business jets.
The layout of Haneda airport is shown in
Figure 1. Domestic airline flights primarily use
the two domestic terminals, while foreign
airline flights use the international terminal.
Aircraft in the latter three categories primarily
use the ‘N’ apron at the northwest corner of theairport.

3.2 Spot DataSMAP and ODAP data for three weeks inMay, July and September 2011 were analysed.
The breakdown of flights for these periods isshown in Table 1. Of the 10,255 departures
recorded, more than 95% were by domestic
airlines, and just under 4% were by foreign

airlines. The remainder (less than 2%) were
‘JA’registration
non-scheduled
flights,calibration flights, and business jet flights.
Table 1 Haneda Departure Flights by
Category
Category
Domestic airline
Foreign airline
Domestic registration
Government
OtherTotal

Departures
9,751 (95.1%)
375 (3.7%)
61 (0.6%)
19 (0.2%)
49 (0.5%)
10,255

Table 2 shows SMAP and ODAP dataavailability. Both SMAP and ODAP departure
spot records were available for more than 85%of departures. However, either SMAP or ODAP
spot information were missing for more than
12% of departures, and around 2% ofdepartures had no departure spot information.
Table 2 Spot Data Availability
SMAP有有無無ODAP
有無有無TotalDepartures
8,783 (85.6%)
119 (1.2%)
1,130 (11.0%)
223 (2.2%)
10,255

The relationship between spot data
availability and flight category was examined.
For SMAP data, while most of the missing data
were for domestic airlines (since these compriseover 90% of total traffic), less than 10% of
domestic airline departures had no SMAP
departure spot information and over 80% of themissing data were for flights by one particularcarrier. Further, 15% of flights by domestic
airlines departing from the Internationalterminal were found to have no SMAP
departure spot data. There were no SMAP data
for nearly all other types of flights.
For ODAP data, less than 2% of domestic
airline departures had no ODAP information.
Moreover, ODAP spot information wasavailable for more than 75% of foreign airlinesand for more than 75% of ‘Other’ category
aircraft (i.e. foreign business jets). In contrast,
there was no ODAP spot information available
for more than 70% of ‘Domestic registration’flights and none for ‘Government’ flights. For

foreign airlines, spot information availability
depended on the airline, with data missing fornearly all departures by five of the 17 foreign
airlines serving Haneda airport. Finally, nospot data were available for more than 70% of‘Domestic registration’ aircraft and for any‘Government’ flights.
We conclude that SMAP or ODAP spot data
are available for the majority of departuresfrom the domestic and international passenger
terminals, although availability depends on theairline. For other categories of flights, spot
information is limited but these flights
comprise less than 3% of departures.

3.2.1 SMAP and ODAP Spot Agreement
We now examine cases where the SMAP andODAP spot information disagree, and thefeasibility of determining the spot from radardata.
When both SMAP and ODAP spot
information were available, they were found toagree in over 98% of cases. For the cases where
they disagreed, the spot data from each systemwere compared with the closest spot to the startof the aircraft’s surface radar track. In morethan 90% of those cases, the radar spot
estimate agreed with the SMAP spot anddisagreed with the ODAP spot. We therefore
conclude that the SMAP spot data are morereliable than ODAP data. This agrees with thefact that SMAP data are derived from theApron Management System and are supposed
to provide a real-time picture of spot usage.

3.2.2 Radar Spot Estimate Accuracy
The assumption that SMAP spot data areaccurate allows us to validate the accuracy ofspot estimates from radar track data. The
results of comparing SMAP spot data withradar spot estimates are shown in Table 3. Thetable shows the number of cases where theSMAP spot agreed with the radar estimate (S =R) and where it disagreed (S ≠ R). The “SingleSamp.” and “Multi. Samp.” columns show theproportion of spot determinations based on asingle position datum or an average of multiple
positions; i.e. whether the position data wereaveraged for a stationary target or derived froma single sample for a moving target.

Table 3 Agreement between SM AP spotand radar estim ate

S=RS≠RTotalCases
2,345(90.3%)
252(9.7%)
2,597Single Samp.
1,396(59.5%)66(26.2%)

Multi. Samp.
949(40.5%)
186(73.8%)

The spot estimated from radar track dataagreed with the SMAP data in more than 90%of cases. Disagreements were further analysedby categorising them as follows:

ADJ The radar closest spot is adjacent to theSMAP spot.
PB

The radar closest spot is near the likely
pushback path from the SMAP spot.

TXY The transponder was switched on while
the aircraft was taxiing to the runway.
OTH Other.
The numbers of disagreements by category
are shown in Table 4. In the more than two
thirds of cases the radar estimate of the closest
initial spot is adjacent to the SMAP departure
spot. For nearly a quarter of disagreements theradar-derived spot was a non-adjacent spot
close to the pushback path. From Table 3 itappears that in cases of discrepancy between
the radar estimate and SMAP spot information,
the majority of radar spot determinations (morethan 70%) are based on the initial position of amoving aircraft. It is therefore thought that in
the majority of cases of disagreement thetransponder was switched on while the aircraftwas being pushed back, or during the time it
was stationary while the tug was being
disconnected, or just after the aircraft hadstarted taxiing.

reliably discriminate between closely spaced
spots that share a common number, e.g. 67, 67Land 67R.
In the ‘TXY’ cases (just under 6% of the total)
the transponder was selected on while theaircraft was taxiing at some distance from thedeparture spot. The ‘OTH’ cases comprisevarious anomalies in the radar track data: forexample apparent cases of the transponder
being on before arrival at the spot and ‘random’initial position samples. These cases accounted
for just under 5% of spot discrepancies. Some
examplesshowingradartrackdata
corresponding to a few of these cases are shownin Figure 2.
For the ‘N’ apron, comparison of ODAP spot
and the radar data showed only two
agreements and 12 disagreements, indicatingthat radar estimates are not reliable for thatarea. A detailed analysis of the reasons was not
carried out, but it has been observed that someaircraft do not park at a designated spot.

(a) Departure from Terminal 1 via runway 05

Table 4 Disagreement between SM AP
spot and radar estimate
CategoryADJPB
TXYOTH
Total

Number170 (67.5%)
56 (22.2%)
14 (5.6%)
12 (4.8%)252

For the ‘ADJ’ cases, some spots appear to be
more prone to misidentification of the adjacent
spot from radar data than others; in particular
it appears that radar data cannot be used to(b) Departure from Terminal 1 via runway 16R

Figure 2 Sample radar data from OTH
cases

3.2.3 ODAP Spot Characteristics
Although SMAP spot information is morereliable than ODAP information, it is mostly
available only for domestic airlines. We
therefore look at the characteristics of ODAP
spot information.

ODAP spot data agreed with the radar spot
estimate in 87.3% of cases, so it appears fairlyreliable. However, foreign carriers mainly use
the International terminal but many spots
adjacent to that terminal have L/R spots, whichcannot be discriminated using radar data. If
the distinction is not important, ignoring the ‘L’and ‘R’ suffix can strengthen the validation ofODAP spot information by radar track data
where SMAP data are not available.
While SMAP spot data are records of actual
spot use, ODAP spot information is thought tobeplanninginformation.
Discrepancies
between the two might arise when an aircraft isplanned to use a certain spot but another spot
was in fact used.

3.3 Turnaround Times
We now report on a preliminary analysis ofturnaround times computed from the AIBT andAOBT recorded by the SMAP system. A total of
7,423 turnaround times were analysed for three
separate weeks in February, May and July
2011. For SMAP data availability reasons, theanalysis is limited largely to spots used bydomestic carriers. It can be consideredreasonably complete for spots at the domestic
terminals, while partial data exist for spots at
the international terminal.

Figure 3 Turnaround times for each spot
The distribution of turnaround times for eachspot is plotted in Figure 3. The vertical axis
shows times in minutes up to 24 hours, withhorizontal lines drawn at 3, 7 and 12 hours.
Eight data points had values greater than 24hours and are not shown. Turnaround times
are grouped largely into two bands: less than 3
hours (approximately 90% of all times) and
7–12 hours (7%). Less than 1% of turnaroundtimes lie between 3 and 7 hours.

3.3.1 Domestic Flights
We examined turnaround times for aircraftwith a turnaround time of less than two hours
that arrived from and then departed to an
airport in Japan. There were 6,343 times
analysed, with a minimum of 17 minutes. The
median was 52 minutes and the mean was 56.3
minutes, with a standard deviation of 17.9
minutes. The mode was between 40 and 50
minutes.
Turnaround times were also analysed byaircraft type. Aircraft were divided into three
categories: regional/commuter, narrowbody
mainline and widebody mainline. The medianand mean times for regional/commuter andnarrowbody aircraft were shorter than for
widebody aircraft, as would be expected fromtheir relative passenger capacities (medianturnaround times of 48, 49 and 55 minutes
respectively for regional/commuter, narrowbody and widebody), but the difference is slightand its statistical significant was not tested.

3.3.2 International Flights
We also analysed turnaround times forflights that arrived from and then departed to aforeign airport. Due to the limited availabilityof SMAP data for international flights, only 61
turnaround times could be extracted. Of these,57 were for a domestic carrier and theremaining four for various foreign carriers.
Aircraft types were all widebody with theexception of a single narrowbody service toSeoul.
Over 70% of flights were turned aroundwithin three hours, with a minimum time of 90
minutes. All flights within the 90–120 minutebin were either arrivals from Seoul being
turned around to Seoul, or arrivals fromHonolulu being turned around to Honolulu.
These data suggest that these flights are being
operated practically as “shuttle” services.

4. Departure Spot Validation
Based on the spot data analysis, we devised
the following method for automaticallydetermining departure spot from SMAP, ODAP
and radar data for creating simulation
scenarios.

1.

If SMAP spot exists, set the departure
spot to the SMAP spot.

2.

Otherwise compute the closest spot to
the radar track initial position. If thedistance from the closest spot is greaterthan 50 m the radar position is rejected.

3.

If ODAP spot exists, compare it with theradar estimate computed in step (2),ignoring ‘L’ and ‘R’ suffixes. If they are
the same, set the departure spot to theODAP spot.

4.

5. Conclusions
The above findings are summarised below.

•••

In the absence of SMAP and ODAP
information, the spot can only be estimatedfrom radar track data. Most of such cases
are flights originating from the ‘N’ apron,
wheredeterminationofspot
frommultilateration data is unreliable. However,these flights comprise only a small
proportion (less than 3%) of departures.

•Of flights for which SMAP AOBT and AIBT
data are available, 90% of flights are turned
around within three hours, while 7% offlights have turnaround times of 7–12 hours.
The former are considered as aircraft
turning around between successive services,while the latter are considered as long-termor overnight parking.

•For domestic flights, the median turnaround time is 52 minutes and the mean is56.3 minutes. Turnaround time appears to
depend on aircraft size.

•For international flights, only a limited
amount of data were available but over 70%
were turned around within three hours.
Aircraft turned around within 90–120
minutes were services to Seoul (a short haul
destination) and Honolulu.

Otherwise leave the departure spot
blank (to be determined by inspection ofradar track or similar flights).

This method was applied to flights for three
weeks in May, July and September 2011. Of
10,255 departures, the method failed todetermine the spot in 393 cases, less than 4% of
the total. Of these, 276 cases were for Domestic
or Foreign carriers, so timetable information orcomparison with the same flight number onother days may be used to determine the likely
spot used.

•aircraft transponder during pushback or
taxiing.

SMAP information is a reliable indicator ofdeparture spot used, but is largely available
only for Japanese domestic airlines.
ODAP spot information is available for themajority of airline departures, both foreignand domestic, and the majority of ‘Other’category (mostly foreign general aviation)
flights, but availability depends on theairline. ODAP spot information is mostly
accurate but is thought to be derived fromplanning information and spot reassignment for operational reasons cancause it to differ from the actual spot used.
If only ODAP spot information is available,
it may be validated with estimates frommultilateration radar data.
The spot estimated from radar track data isaccurate in about 80% of cases. However,certain spots (the ‘N’ apron, spots withassociated ‘L’ and ‘R’ spots) appear more
likely than others to be misidentified. A
major cause of misidentification isconsidered to be the activation of theFrom this study, we devised a method thatmakes a “best guess” of the departure spot fromavailable sources of information. The method
fails in less than 4% of cases, and for themajority of these cases the spot can beestimated by referring to the spot used by thesame timetabled flight on another day.
Further analysis of turnaround time willcontinue, and the results will be incorporated
into our simulation model.

Acknowledgements
The authors gratefully thank the Japan Civil
Aviation Bureau for providing spot andsurveillance data for this research.

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