平成30年度（第18回）電子航法研究所研究発表会㸯㸬ᑠᆺ↓ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ        =9!?;7A& C-1$'C
⯟✵஺㏻⟶⌮㡿ᇦͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊoperations. These drawbacks are addressed recently
with the introduction of UPRs (User Preferredࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟
Routes) due to,✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿• The possibility of designing operator⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
oriented flight routes by the operators.
ࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
• The improvement of ground-based ATCࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪
(Air Traffic Control) support systems.
㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇ• Introduction of modern aircraft types which࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
are capable of operating at reducedࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
separation margins.
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
UPRs are custom-designed for each flight accordingୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
to its departure time and take-off weight, since moreࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛
operator-friendly compared to conventionalᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘
PACOTS. Yet, UPRs too have operational
constraints such as ࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
• Aircraft on UPRs have to maintain at least࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ
50NM of lateral separation from certain᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼ
PACOTS routes.
ࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ
• Aircraft on UPRs do not have priority on 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜
selecting the desired flight altitude.
ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪Hence, DARP (Dynamic Airborne Rerouteཬࡧ➨ 132 ᮲ࡢ 3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉Procedures) Operations are introduced as one of the౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪
strategies to meet these challenges in the UPR୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ
implementation for Northern and Southern Pacific᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
regions.
࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕  "!
⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ
DARP is a procedure for re-route clearance࠸࡚ㄝ᫂ࡍࡿࠋ
which contributes towards more efficient traffic flow
2.1.1 㣕⾜ࡍࡿ✵ᇦand cost savings by implementing dynamic lateral↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせroute alterations from the initial flight plan upon࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ [2,4]considering updated weather conditions . Figure 2(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
denotes the workflow from flight plan generation toࢀࡢ࠶ࡿ✵ᇦ
departure. Generally, a flight plan is prepared 3 ~ 4࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ
hours before the departure with the latest weather࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ 150m ௨ୖࡢ㧗ࡉࡢ✵
forecast data. Weather forecast data are updatedᇦ
every six hours starting from 0000Hrs UTC. Hence,(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵it can be noted that the aircraft does not possess an࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
optimal flight plan since the weather conditions canཱྀ㞟୰ᆅ༊ࡢୖ✵be changed by the time of departure.

 " #"
Increasing demand for air travel has propelled1 ࡣࡌࡵ࡟
research & development projects in a global scale to ᖹᡂ 27 ᖺ 12 ᭶࡟⯟✵ἲࡢᨵṇࡀ᪋⾜ࡉࢀ
seek new solutions for enhancing the efficiency of࡚࠿ࡽ 2 ᖺ㏆ࡃࡀ⤒㐣ࡋ㸪ከࡃࡢ↓ே⯟✵ᶵthe current air transportation system. Studies predict࣮ࣘࢨ࣮ࡀ㣕⾜ࡢ࣮ࣝࣝ㸪㣕⾜ྍ⬟࡞✵ᇦ࡟that that larger portion of air traffic would increaseࡘ࠸࡚ㄆ㆑ࡍࡿࡼ࠺࡟࡞ࡗ࡚ࡁࡓࠋ↓ே⯟✵over the Japanese airspace due to long-haul flights,ᶵࡢ฼ά⏝ࡣ㸪㛵ಀ┬ᗇ㐃⤡఍㆟࡟࠾ࡅࡿᐁ
in other words international flights and overflights.
Ẹ༠㆟఍࡟࠾࠸࡚㸪ᢏ⾡㛤Ⓨ࡜⎔ቃᩚഛ࡟㛵
Figure 1 illustrates the complex structure ofࡍࡿ࣮ࣟࢻ࣐ࢵࣉࡀ♧ࡉࢀ [1] 㸪ᑠᆺ↓ேᶵࡢairspace around Japan.
฼⏝ࢆಁ㐍ࡋࡘࡘᏳ඲☜ಖࢆࡍࡿࡓࡵࡢไᗘ
సࡾࡢ᳨ウࡀ㐍ࡵࡽࢀ࡚࠸ࡿࠋ᳨ウ࡟࠾࠸࡚ࡣ㸪ไᗘタィ࡟㛵ࡋ࡚ࡣ㸪฼⏝ಁ㐍࡜Ᏻ඲☜ಖࡢࣂࣛࣥࢫࢆ࡜ࡿࡇ࡜ࡀ㔜せ࡜ࡉࢀ࡚࠸ࡿ
[2]ࠋࡋ࠿ࡋ㸪ᨵṇ⯟✵ἲ࡟ࡼࡾ↓ே⯟✵ᶵ࡜⯟✵ᶵࡢ㣕⾜⠊ᅖࡀ࠶ࡿ⛬ᗘศ㞳ࡉࢀࡘࡘ࠶
ࡿ୍᪉࡛㸪౛እ࡜࡞ࡿ㣕⾜ࡣከࡃᏑᅾࡋ㸪✵ᇦ฼⏝⪅ࡢᏳ඲ࢆ☜ಖࡍࡿࡓࡵ࡟ࡣ࠸ࡃࡘࡶ
ࡢㄢ㢟ࡀ࠶ࡿࠋ ᮏ✏࡛ࡣ㸪ࡇࢀࡽࡢㄢ㢟ࡢࡦ࡜ࡘ࡜⪃࠼ࡽࢀࡿ㸪᫖௒ࢻ࣮ࣟࣥ࡜࿧ࡤࢀࡿࡼ࠺࡞ᑠᆺ↓Fig. 1 Fukuoka FIR and adjacent foreign FIRs[1].
ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡛ࡢ⯟✵ᶵ㐠⯟࡟ࡘ࠸࡚⪃ᐹࡍࡿࠋ✵ እࡢప㧗ᗘࢆ㣕⾜ࡍࡿ⯟✵ᶵ
Oceanic air routes over the Fukuoka FIR (Flight࡜ᑠᆺ↓ேᶵࡢ㣕⾜࢚ࣜ࢔ࡀ➇ྜࡍࡿྍ⬟ᛶ
Information Region) are mainly consist of NOPACࡀ࠶ࡾ㸪ᑠᆺ↓ேᶵࡢ㣕⾜ᩘࡀቑຍࡍࢀࡤ᥋
(North Pacific) routes and PACOTS (Pacific㏆ࡍࡿ☜⋡ࡶ㧗ࡃ࡞ࡿࠋ௒ᚋ฼ά⏝ࡀಁ㐍ࡉ
Organized Track System) routes. NOPAC routes areࢀ㣕⾜㟂せࡀ㧗ࡲࡿ࡜ᛮࢃࢀࡿᑠᆺ↓ேᶵࡀ
five parallel air routes located between Fukuoka and㣕⾜ࡍࡿ࢚ࣜ࢔ࡢᏳ඲☜ಖࡢࡓࡵ࡟ࡶ㸪᪩ᛴAnchorage FIRs bordering the Khabarovsk and࡟ྲྀࡾ⤌ࡲ࡞ࡅࢀࡤ࡞ࡽ࡞࠸ㄢ㢟ࡢࡦ࡜ࡘ࡛
Petropavlovsk-Kamchatsky FIRs. These routes were࠶ࡿࠋ
designed in the 1980s and since then have not2 ᨵṇ⯟✵ἲ࡟ࡼࡿチྍᢎㄆ࡟ࡘ࠸࡚
undergoneany
major
modificationsnor ᨵṇ⯟✵ἲ࡟ࡼࡾ㸪↓ே⯟✵ᶵࡀᐃ⩏ࡉࢀ
improvements. On the other hand, PACOTS are㣕⾜ࡢ࣮ࣝࣝࡀᩚ⌮ࡉࢀࡓࠋ⯟✵ἲ࡛ࡣ㸪↓
optimized routes for high-altitude wind conditionsே⯟✵ᶵࡢ㣕⾜ࡀ㸪᭷ே⯟✵ᶵࡢ㣕⾜㸪ேཬpublished by either FAA or JCAB in the dailyࡧ≀௳➼࡟ᙳ㡪ࢆཬࡤࡉ࡞࠸ࡇ࡜ࢆ┠ⓗ࡟ᐃ
NOTAM or track message. Currently, 9 tracks areࡵࡽࢀ࡚࠸ࡿࠋ
published for eastbound flights while 11 tracks are
2.1 ᨵṇ⯟✵ἲࡢᴫせ
published for westbound flights[2,3]. PACOTS are ⯟✵ἲ➨ 2 ᮲➨ 22 㡯࡟࠾࠸࡚↓ே⯟✵ᶵࡣ
generated by considering the average take-offḟࡢࡼ࠺࡟ᐃ⩏ࡉࢀ࡚࠸ࡿࠋ
weights and departure times of used aircraft in the-1-7-平成30年度（第18回）電子航法研究所研究発表会

平成30年度（第18回）電子航法研究所研究発表会

application of 4D- optimal trajectories into theࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ system. These studies show that weather data

ࡀ᪋⾜ࡉࢀ
↓ே⯟✵ᶵ
⬟࡞✵ᇦ࡟
ࠋ↓ே⯟✵
࡟࠾ࡅࡿᐁ
ቃᩚഛ࡟㛵
ᆺ↓ேᶵࡢ
ࡓࡵࡢไᗘ
ウ࡟࠾࠸࡚
㐍࡜Ᏻ඲☜
ࡉࢀ࡚࠸ࡿ
ே⯟✵ᶵ࡜
ࡉࢀࡘࡘ࠶
Ꮡᅾࡋ㸪✵
ࡣ࠸ࡃࡘࡶ

ࡘ࡜⪃࠼ࡽ
࠺࡞ᑠᆺ↓
⯟࡟ࡘ࠸࡚
ࡍࡿ⯟✵ᶵ
ࡍࡿྍ⬟ᛶ
ຍࡍࢀࡤ᥋
⏝ࡀಁ㐍ࡉ
ᆺ↓ேᶵࡀ
࡟ࡶ㸪᪩ᛴ
ࡢࡦ࡜ࡘ࡛

ࡘ࠸࡚
ࡀᐃ⩏ࡉࢀ
ἲ࡛ࡣ㸪↓
㣕⾜㸪ேཬ
ࢆ┠ⓗ࡟ᐃ

ே⯟✵ᶵࡣ

⯟✵஺㏻⟶⌮㡿ᇦ ͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ contribute significantly towards the implementationof such procedures.
┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊThis paper emphasizes the influence of weather
Fig. 2 Workflow from flight plan generation to departure.
data in DARP operations through informationࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟ regarding a series of DARP implemented flights✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿Then, theflight deck weather data is updated before
provided by a national airline company. These data⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
the departure with the latest forecast data. DARP
are used as reference to show the operational benefitsࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
route is calculated only once for the entire flight andby applying DARP procedures which reflect theࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪is uplinked to the flight deck by the ground operator.
influence of weather in such operations.
㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇ
Depending on the situation, pilot decides whether to
Performance parameters are calculated by applying࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
implement the DARP route or not. DARP operations
meteorological data from the Japan Meteorologicalࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
have an implementation rate of 3.1% (384 flights out
Agency (JMA)[6] and aircraft performance data fromࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
the Base of Aircraft Data (BADA) data from theof 12,422 DARP intended flights) since OctoberୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
EUROCONTROL[7]. Obtained results are used to2012 to August 2017 with a reduction of 376,500 lbs.
discuss the improvements that can be proposed forof fuelࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛and 26 Hrs. 50 Min. of flight time [5]. Averageᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘benefits through DARP implementation are notoceanic routes and issues to overcome in validating
notable due to many challenges concerning DARP
such proposals in a real operational environment. A࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
operations. One is the dispatcher’s workload incomprehensive discussion on the enhancement of࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ
implementing DARP process. This includes DARP
this research scope is provided in future work.
᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ
㸧࡟ࡼroute calculation, data link process and coordinationࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ
withࡾ㸪㔜㔞
the pilot.200
Also,
it is difficult to accurately   "㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨132
᮲㸦㣕⾜This section introduces different data types that
estimate the DARP benefits at the time ofࡢ⚗Ṇ✵ᇦ㸧
㸪➨132
᮲ࡢ
2㸦㣕⾜ࡢ᪉ἲ㸧㸪are used to present the preliminary results.
implementation since the flight profile is updated
ཬࡧ➨
132
᮲ࡢ
3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉   "!" "only once with the weather data obtained by the౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪In this study, information regarding a series ofground operator for a flight that lasts more than 10୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ
DARP implemented flights provided by a national
hours. In order to broaden the application scope of
᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
airline company is used as reference data (indicatedsuch procedures which would eventually increase࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕
as ‘reference data’). The flights are originated fromthe benefits
in a future system, reviewing the KPIs
⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ
the Honolulu International Airport (ICAO Code:(key performance indicators) that have significant࠸࡚ㄝ᫂ࡍࡿࠋ
PHNL) and the destinations are the Tokyo
influence in implementing the procedure is of great㣕⾜ࡍࡿ✵ᇦ
2.1.1
International Airport (ICAO Code: RJTT) and Narita
importance.
↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせInternational Airport (ICAO Code: RJAA). The 3DElectronic Navigation Research Institute (ENRI)
࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
position data required for analysis are acquired
is involved in various research studies among which(a)⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
through the integration of Oceanic Air Traffic
analyzing / optimizing oceanic tracks includingࢀࡢ࠶ࡿ✵ᇦ
Control Data Processing System (ODP) data andarrival routes
and Full-4D operations are two major࣭✵➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ
Radar Data Processing System (RDP) data. ODP
projects assigned to seek solutions to improve the࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ
150m
௨ୖࡢ㧗ࡉࡢ✵data are generated according to flight plan data andcurrent ATC system. The former project investigatesᇦ
position reports downlinked by the aircraft. RDP
the impact of innovative procedures such as(b)ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵data are radar data tracked by the Oceanic Routecontinuous descent operations (CDO), flight-deck࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
Surveillance Radars (ORSR) and Air Routeinterval management (FIM) and airborneཱྀ㞟୰ᆅ༊ࡢୖ✵Surveillance Radars (ARSR). The data are further
surveillance application systems (ASAS), while theprocessed by a smoothing algorithm to treat irregularlatter project reviews the challenges towards the


-1-

-8-

平成30年度（第18回）電子航法研究所研究発表会

data patterns and data loss[8].
forecast data on global and local atmospheric㸯㸬ᑠᆺ↓ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ
Performanceparameter
estimationsareconditions. The Global Spectral Model (GSM)
considerably sensitive to weather data and time
nowcastdata is used in theͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ
analysis, of which the⯟✵஺㏻⟶⌮㡿ᇦ
histories of the position data. Furthermore, access toforecast data is updated at an interval of 6 hours. The┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊODP and RDP data are limited. Hence, general
precision of forecast data is already validated in areview is made based on overall data (18 flights)
previous study[6].
ࡣࡌࡵ࡟1ࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟   "   "while
the sensitive calculations are conducted based
only
ᖹᡂ
27
ᖺ
12
᭶࡟⯟✵ἲࡢᨵṇࡀ᪋⾜ࡉࢀ✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿
Aircraft performance calculations of conventionalon accessible data, five flights in total. Flight࡚࠿ࡽ2
ᖺ㏆ࡃࡀ⤒㐣ࡋ㸪ከࡃࡢ↓ே⯟✵ᶵ⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
and optimal operations are based on the BADA
plan data are available as 3D trajectory profiles with࣮ࣘࢨ࣮ࡀ㣕⾜ࡢ࣮ࣝࣝ㸪㣕⾜ྍ⬟࡞✵ᇦ࡟ࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
(version
3.12) model data. As aircraft mass data is
waypoint data and, altitude and speed assigned atࡘ࠸࡚ㄆ㆑ࡍࡿࡼ࠺࡟࡞ࡗ࡚ࡁࡓࠋ↓ே⯟✵ࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪
unknown, aircraft mass at the initial point of each
each waypoint. Hence data interpolation is necessaryᶵࡢ฼ά⏝ࡣ㸪㛵ಀ┬ᗇ㐃⤡఍㆟࡟࠾ࡅࡿᐁ
㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇflightis estimated as a ratio of the maximum taketo generate trajectories for performance estimation.
Ẹ༠㆟఍࡟࠾࠸࡚㸪ᢏ⾡㛤Ⓨ࡜⎔ቃᩚഛ࡟㛵࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
off mass definedin the BADA model. The ratio is
On the other hand, ODP data is updated at a 1-minute[1]㸪ᑠᆺ↓ேᶵࡢ
ࡍࡿ࣮ࣟࢻ࣐ࢵࣉࡀ♧ࡉࢀࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
considered
basedon
the fact that fuel consumption isinterval compared to the 10-second interval of RDP฼⏝ࢆಁ㐍ࡋࡘࡘᏳ඲☜ಖࢆࡍࡿࡓࡵࡢไᗘ
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
approximately proportional to flight time [9]. Table 1
data. These dissimilarities have influenced to adopt
సࡾࡢ᳨ウࡀ㐍ࡵࡽࢀ࡚࠸ࡿࠋ᳨ウ࡟࠾࠸࡚ୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
shows
the flight time estimated from the plan tracks
following assumptions in trajectory preparation inࡣ㸪ไᗘタィ࡟㛵ࡋ࡚ࡣ㸪฼⏝ಁ㐍࡜Ᏻ඲☜ࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛with total flight time in brackets, acquired by flightorder to implement a fair analysis on operationalಖࡢࣂࣛࣥࢫࢆ࡜ࡿࡇ࡜ࡀ㔜せ࡜ࡉࢀ࡚࠸ࡿ
ᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ
㸧ࢆ࠸࠺ࠋ
plan
data, and the estimatedinitial࠘mass value for
performance between predetermined flight plans and[2]ࠋࡋ࠿ࡋ㸪ᨵṇ⯟✵ἲ࡟ࡼࡾ↓ே⯟✵ᶵ࡜each flight case.
corresponding DARP. The context of this paper⯟✵ᶵࡢ㣕⾜⠊ᅖࡀ࠶ࡿ⛬ᗘศ㞳ࡉࢀࡘࡘ࠶
࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
refers to trajectories generated based on flight planࡿ୍᪉࡛㸪౛እ࡜࡞ࡿ㣕⾜ࡣከࡃᏑᅾࡋ㸪✵࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ
Table 1 Estimated Aircraft Initial Massdata as ‘plan tracks’ and trajectories generated basedᇦ฼⏝⪅ࡢᏳ඲ࢆ☜ಖࡍࡿࡓࡵ࡟ࡣ࠸ࡃࡘࡶ
᪋⾜つ๎㸦௨ୗ㸪㸧࡟ࡼ
on airline provided information as ‘DARP tracks’.
FlightFlightࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ
time (s)Initial mass(kg)ࡢㄢ㢟ࡀ࠶ࡿࠋ
ࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ
• Aircraft performs at a cruising speed of
F01
23,280 (33,000)
133,704 ᮏ✏࡛ࡣ㸪ࡇࢀࡽࡢㄢ㢟ࡢࡦ࡜ࡘ࡜⪃࠼ࡽ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜
Mach 0.80. This is the standard airline
F02
23,610 (33,600)
131,928ࢀࡿ㸪᫖௒ࢻ࣮ࣟࣥ࡜࿧ࡤࢀࡿࡼ࠺࡞ᑠᆺ↓ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪procedure value defined in the BADA model
F03
15,390 (32,880)
88,154ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡛ࡢ⯟✵ᶵ㐠⯟࡟ࡘ࠸࡚ཬࡧ➨ 132 ᮲ࡢ 3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉for the subjected aircraft.
F04
22,350 (32,520)
131,655⪃ᐹࡍࡿࠋ✵
እࡢప㧗ᗘࢆ㣕⾜ࡍࡿ⯟✵ᶵ౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪• Aircraft performs
from the initial point of
F05
19,670 (26,640)
137,985࡜ᑠᆺ↓ேᶵࡢ㣕⾜࢚ࣜ࢔ࡀ➇ྜࡍࡿྍ⬟ᛶ୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ
reference data to the initial point availableࡀ࠶ࡾ㸪ᑠᆺ↓ேᶵࡢ㣕⾜ᩘࡀቑຍࡍࢀࡤ᥋᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
from ODP data at the cruising altitude $"!#"!
㏆ࡍࡿ☜⋡ࡶ㧗ࡃ࡞ࡿࠋ௒ᚋ฼ά⏝ࡀಁ㐍ࡉ࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕
identical to the altitude at the initial point of
Figure 3 shows the lateral route deviations ofࢀ㣕⾜㟂せࡀ㧗ࡲࡿ࡜ᛮࢃࢀࡿᑠᆺ↓ேᶵࡀ⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ
ODP data.
DARP tracks for all the flights compared to the㣕⾜ࡍࡿ࢚ࣜ࢔ࡢᏳ඲☜ಖࡢࡓࡵ࡟ࡶ㸪᪩ᛴ࠸࡚ㄝ᫂ࡍࡿࠋ
• The aircraft follows the vertical profile
corresponding
plan tracks. Deviations are plotted㣕⾜ࡍࡿ✵ᇦ
࡟ྲྀࡾ⤌ࡲ࡞ࡅࢀࡤ࡞ࡽ࡞࠸ㄢ㢟ࡢࡦ࡜ࡘ࡛
2.1.1
acquired by the corresponding ODP andwithrespect to longitude. Most of the DARP↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせ࠶ࡿࠋRDP data in both plan tracks and DARP
operations are initiated around the 180°E meridian࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
2 ᨵṇ⯟✵ἲ࡟ࡼࡿチྍᢎㄆ࡟ࡘ࠸࡚
tracks.
exceptfor two flights which record the largest ᨵṇ⯟✵ἲ࡟ࡼࡾ㸪↓ே⯟✵ᶵࡀᐃ⩏ࡉࢀ(a)⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
• Aircraft passes the merging point of plan
deviationof 7 degrees.
㣕⾜ࡢ࣮ࣝࣝࡀᩚ⌮ࡉࢀࡓࠋ⯟✵ἲ࡛ࡣ㸪↓ࢀࡢ࠶ࡿ✵ᇦ
track and DARP track at the same time,ே⯟✵ᶵࡢ㣕⾜ࡀ㸪᭷ே⯟✵ᶵࡢ㣕⾜㸪ேཬ࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ
hence the starting time between the twoࡧ≀௳➼࡟ᙳ㡪ࢆཬࡤࡉ࡞࠸ࡇ࡜ࢆ┠ⓗ࡟ᐃ࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ 150m ௨ୖࡢ㧗ࡉࡢ✵
tracks are not identical. The difference ofࡵࡽࢀ࡚࠸ࡿࠋ
ᇦ
weather conditions due to this reason is
2.1 ᨵṇ⯟✵ἲࡢᴫせ(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵considered negligible.
⯟✵ἲ➨᮲➨ 22 㡯࡟࠾࠸࡚↓ே⯟✵ᶵࡣ࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
 "2
"ḟࡢࡼ࠺࡟ᐃ⩏ࡉࢀ࡚࠸ࡿࠋ
ཱྀ㞟୰ᆅ༊ࡢୖ✵JMA distributes a variety of numerical weather
Fig. 3 Lateral deviation due to DARP operations.
prediction (NWP) grid point value (GPV) weather-1-9-平成30年度（第18回）電子航法研究所研究発表会

平成30年度（第18回）電子航法研究所研究発表会

 "!!!!!"  ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹThis section compares the aircraft performance,

ࡀ᪋⾜ࡉࢀ
↓ே⯟✵ᶵ
⬟࡞✵ᇦ࡟
ࠋ↓ே⯟✵
࡟࠾ࡅࡿᐁ
ቃᩚഛ࡟㛵
ᆺ↓ேᶵࡢ
ࡓࡵࡢไᗘ
ウ࡟࠾࠸࡚
㐍࡜Ᏻ඲☜
ࡉࢀ࡚࠸ࡿ
ே⯟✵ᶵ࡜
ࡉࢀࡘࡘ࠶
Ꮡᅾࡋ㸪✵
ࡣ࠸ࡃࡘࡶ

ࡘ࡜⪃࠼ࡽ
࠺࡞ᑠᆺ↓
⯟࡟ࡘ࠸࡚
ࡍࡿ⯟✵ᶵ
ࡍࡿྍ⬟ᛶ
ຍࡍࢀࡤ᥋
⏝ࡀಁ㐍ࡉ
ᆺ↓ேᶵࡀ
࡟ࡶ㸪᪩ᛴ
ࡢࡦ࡜ࡘ࡛

ࡘ࠸࡚
ࡀᐃ⩏ࡉࢀ
ἲ࡛ࡣ㸪↓
㣕⾜㸪ேཬ
ࢆ┠ⓗ࡟ᐃ

ே⯟✵ᶵࡣ

mainly fuelconsumption between
plan tracks and⯟✵஺㏻⟶⌮㡿ᇦͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ
DARP tracks to understand the benefits obtained by┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊimplementing DARP. Flight time is the other key
parameter which is compared to review the benefitsࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟
obtainedthrough implementing DARP operations.
Table 2✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿denotes the numerical values of total fuel⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
consumption,
flight time and flight range for the fiveࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
subjected flight cases.
ࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪
Table 2㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇ
Aircraft Performance of Planned and DARP Flights࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
FlightFuel(kg) Time (s)Range (m)ࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
plan
25,58423,2805.489×106
F01 ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
DARP
24,72322,6405.334×106ୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
plan
25,10923,6105.474×106
F02ࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛DARP
24,96923,4805.510×106
ᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ
plan
12,331 㸧ࢆ࠸࠺ࠋ15,390 ࠘3.344×106
F03
DARP
11,98615,0903.412×106
plan
23,57922,3505.376×106 ࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
F04
DARP
23,12822,0005.303×106
࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ
plan
22,066
19,6704.747×106
F05᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼ
DARP 20021,870
19,5004.705×106ࡾ㸪㔜㔞ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜
Numerical
results
operations 㸪ࡢ⚗Ṇ✵ᇦ㸧
㸪➨show
132 that
᮲ࡢDARP
2㸦㣕⾜ࡢ᪉ἲ㸧
wereཬࡧ➨successful
reducing
fuel consumption for all
132in᮲ࡢ
3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉flightsby considering the 㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪
updates in weather౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪
forecast
data. Figure 4 shows the bar plot for fuel୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ
consumption
difference for each flight. Flight F02
᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
records
the lowest fuel difference since the DARP࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕
track⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ
has deviated significantly from the plan trackand ࠸࡚ㄝ᫂ࡍࡿࠋ
has recorded the largest positive range difference
amongthe 㣕⾜ࡍࡿ✵ᇦsubjected five flights. Hence, it is2.1.1
assumedthat a larger amount of fuel was reduced↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせwhenconsidering the total flight. Figure 5 depicts࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
the percentage
of fuel consumption difference with(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
respect ࢀࡢ࠶ࡿ✵ᇦto flight range difference. Similar to Fig. 4,
both parameters
are evaluated based on plan track࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ
performanceparameters.
࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ
150m ௨ୖࡢ㧗ࡉࡢ✵It is consideredthat
tradeoffwith longer flightᇦ
path(b)has ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵paid off well in reducing fuel consumption
for flights
F02 and F03. It is considered that tradeoff࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
with longer
flight path has paid off well in reducingཱྀ㞟୰ᆅ༊ࡢୖ✵fuel consumption for flights F02 and F03. It is also



-1-

-10-

Fig. 4 Fuel Consumption Reduction with DARP.

Fig. 5 Tradeoff between Fuel and Range with DARP.

considered that airline companies are eager toimplement DARP operations when dynamic rerouting provide benefits on fuel burn and/or flighttime. Effect on flight time due to DARP operations
is not reviewed in this study, because the timeparameter was not available in the acquired DARP
information.
 "!!!!!"   
A trajectory optimization model based on the
Dynamic Programming (DP) method has beendeveloped by the authors which minimizes fuelconsumption according to given arrival time
constraints by exerting maximum aircraft
performance. This section focuses on the 4D- TBO
with free arrival time. These results are based on theassumption that fuel consumption could be reducedby allowing the aircraft to optimize its trajectory

平成30年度（第18回）電子航法研究所研究発表会

through considering real-time weather conditions in㸯㸬ᑠᆺ↓ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ
anideal 3D- operational environment. Table 3
denotes the numerical values of fuel consumption⯟✵஺㏻⟶⌮㡿ᇦ ͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅand flight time corresponding to each optimal flight.
┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊ
Table 3 Optimal Performance with 4D-TBO

ࡣࡌࡵ࡟1Flight
Fuel (kg)Time (s)Range (m) ᖹᡂ 27 ᖺ 12 ᭶࡟⯟✵ἲࡢᨵṇࡀ᪋⾜ࡉࢀF01
20,442
21,051
4.991×106
࡚࠿ࡽ 2 ᖺ㏆ࡃࡀ⤒㐣ࡋ㸪ከࡃࡢ↓ே⯟✵ᶵ
F0220,523
21,431
5.064×106
࣮ࣘࢨ࣮ࡀ㣕⾜ࡢ࣮ࣝࣝ㸪㣕⾜ྍ⬟࡞✵ᇦ࡟
F03
10,41814,8893.287×106
ࡘ࠸࡚ㄆ㆑ࡍࡿࡼ࠺࡟࡞ࡗ࡚ࡁࡓࠋ↓ே⯟✵F04
21,132
22,0275.121×106ᶵࡢ฼ά⏝ࡣ㸪㛵ಀ┬ᗇ㐃⤡఍㆟࡟࠾ࡅࡿᐁ
F05
20,756
20,6224.640×106Ẹ༠㆟఍࡟࠾࠸࡚㸪ᢏ⾡㛤Ⓨ࡜⎔ቃᩚഛ࡟㛵ࡍࡿ࣮ࣟࢻ࣐ࢵࣉࡀ♧ࡉࢀ [1] 㸪ᑠᆺ↓ேᶵࡢ
Figures 6 and 7 show fuel consumption difference฼⏝ࢆಁ㐍ࡋࡘࡘᏳ඲☜ಖࢆࡍࡿࡓࡵࡢไᗘ
and fuel difference percentage versus flight range
సࡾࡢ᳨ウࡀ㐍ࡵࡽࢀ࡚࠸ࡿࠋ᳨ウ࡟࠾࠸࡚difference between optimal tracks and DARP tracksࡣ㸪ไᗘタィ࡟㛵ࡋ࡚ࡣ㸪฼⏝ಁ㐍࡜Ᏻ඲☜respectively. It is understood that a significantಖࡢࣂࣛࣥࢫࢆ࡜ࡿࡇ࡜ࡀ㔜せ࡜ࡉࢀ࡚࠸ࡿ
[2]
reductionof fuel consumption was obtained throughࠋࡋ࠿ࡋ㸪ᨵṇ⯟✵ἲ࡟ࡼࡾ↓ே⯟✵ᶵ࡜
trajectory optimization. These results include the⯟✵ᶵࡢ㣕⾜⠊ᅖࡀ࠶ࡿ⛬ᗘศ㞳ࡉࢀࡘࡘ࠶
assumption errors in trajectory generation for DARP
ࡿ୍᪉࡛㸪౛እ࡜࡞ࡿ㣕⾜ࡣከࡃᏑᅾࡋ㸪✵flights. Yet, it can be speculated that even withoutᇦ฼⏝⪅ࡢᏳ඲ࢆ☜ಖࡍࡿࡓࡵ࡟ࡣ࠸ࡃࡘࡶ
considering the assumption errors, the optimizerࡢㄢ㢟ࡀ࠶ࡿࠋ
reduce fuel consumption compared to thecouldᮏ✏࡛ࡣ㸪ࡇࢀࡽࡢㄢ㢟ࡢࡦ࡜ࡘ࡜⪃࠼ࡽ
subjected DARP flights. Results also show that flightࢀࡿ㸪᫖௒ࢻ࣮ࣟࣥ࡜࿧ࡤࢀࡿࡼ࠺࡞ᑠᆺ↓range was also reduced for all five flights. It isேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡛ࡢ⯟✵ᶵ㐠⯟࡟ࡘ࠸࡚considered that thisdifference has mainly caused the⪃ᐹࡍࡿࠋ✵
እࡢప㧗ᗘࢆ㣕⾜ࡍࡿ⯟✵ᶵ
reduction of fuel consumption. Figure 8 shows that࡜ᑠᆺ↓ேᶵࡢ㣕⾜࢚ࣜ࢔ࡀ➇ྜࡍࡿྍ⬟ᛶthe optimal track selects the minimum distant track,ࡀ࠶ࡾ㸪ᑠᆺ↓ேᶵࡢ㣕⾜ᩘࡀቑຍࡍࢀࡤ᥋
commonly known as the Great Circle Route㏆ࡍࡿ☜⋡ࡶ㧗ࡃ࡞ࡿࠋ௒ᚋ฼ά⏝ࡀಁ㐍ࡉ
compared to its counterpart. Contours represent theࢀ㣕⾜㟂せࡀ㧗ࡲࡿ࡜ᛮࢃࢀࡿᑠᆺ↓ேᶵࡀwind distribution at 250 hPa pressure altitude㣕⾜ࡍࡿ࢚ࣜ࢔ࡢᏳ඲☜ಖࡢࡓࡵ࡟ࡶ㸪᪩ᛴ(approximately 33,000 ft). The maximum fuel࡟ྲྀࡾ⤌ࡲ࡞ࡅࢀࡤ࡞ࡽ࡞࠸ㄢ㢟ࡢࡦ࡜ࡘ࡛
reduction is recorded at about 17% while the࠶ࡿࠋmaximumrange reduction is recorded atᨵṇ⯟✵ἲ࡟ࡼࡿチྍᢎㄆ࡟ࡘ࠸࡚
2
8%. Results show that dynamic reapproximatelyᨵṇ⯟✵ἲ࡟ࡼࡾ㸪↓ே⯟✵ᶵࡀᐃ⩏ࡉࢀ
routing according to weather conditions provide fuel㣕⾜ࡢ࣮ࣝࣝࡀᩚ⌮ࡉࢀࡓࠋ⯟✵ἲ࡛ࡣ㸪↓saving benefits to airline operators. It is speculatedே⯟✵ᶵࡢ㣕⾜ࡀ㸪᭷ே⯟✵ᶵࡢ㣕⾜㸪ேཬthat, though fuel savings from a standalone flightࡧ≀௳➼࡟ᙳ㡪ࢆཬࡤࡉ࡞࠸ࡇ࡜ࢆ┠ⓗ࡟ᐃ
would not be so significant, cumulative evaluationsࡵࡽࢀ࡚࠸ࡿࠋ
wouldᨵṇ⯟✵ἲࡢᴫせshow that DARP could bring significantly
2.1
impactto 22
airlineoperators. Results also
positive⯟✵ἲ➨
2 ᮲➨㡯࡟࠾࠸࡚↓ே⯟✵ᶵࡣ
show that 4D- TBO applications consideringḟࡢࡼ࠺࡟ᐃ⩏ࡉࢀ࡚࠸ࡿࠋ
updated weather data conditions in oceanic flightsࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
ࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
ࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇFig. 6 Fuel Consumption Reduction with 4D-TBO.
࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
ࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
ୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
ࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘ ࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪ཬࡧ➨132 ᮲ࡢ
3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉Fig. 7 Tradeoff
between
Fuel and Range with 4D-TBO.
౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ࠸࡚ㄝ᫂ࡍࡿࠋ
2.1.1 㣕⾜ࡍࡿ✵ᇦ↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせ࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
Fig. 8 Lateral Route Profile Comparison.
(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
ࢀࡢ࠶ࡿ✵ᇦ
recordsignificant fuel savings compared to࣭✵➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦconventional
procedures and arrival time࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ
150m
management capability added
4D-௨ୖࡢ㧗ࡉࡢ✵
TBO would be aᇦpotential enhancement for oceanic operations.
(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵#"#  ࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
ཱྀ㞟୰ᆅ༊ࡢୖ✵Preliminary results discussed in this paper are
based on information that had to be gathered in heaps-1-11-

平成30年度（第18回）電子航法研究所研究発表会

平成30年度（第18回）電子航法研究所研究発表会

and integrated due to the lack of data resources. In

magnitude,wind

directionandatmosphericࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ
order to implement a more comprehensive review ontemperature at different flight levels along the

ࡀ᪋⾜ࡉࢀ
↓ே⯟✵ᶵ
⬟࡞✵ᇦ࡟
ࠋ↓ே⯟✵
࡟࠾ࡅࡿᐁ
ቃᩚഛ࡟㛵
ᆺ↓ேᶵࡢ
ࡓࡵࡢไᗘ
ウ࡟࠾࠸࡚
㐍࡜Ᏻ඲☜
ࡉࢀ࡚࠸ࡿ
ே⯟✵ᶵ࡜
ࡉࢀࡘࡘ࠶
Ꮡᅾࡋ㸪✵
ࡣ࠸ࡃࡘࡶ

ࡘ࡜⪃࠼ࡽ
࠺࡞ᑠᆺ↓
⯟࡟ࡘ࠸࡚
ࡍࡿ⯟✵ᶵ
ࡍࡿྍ⬟ᛶ
ຍࡍࢀࡤ᥋
⏝ࡀಁ㐍ࡉ
ᆺ↓ேᶵࡀ
࡟ࡶ㸪᪩ᛴ
ࡢࡦ࡜ࡘ࡛

ࡘ࠸࡚
ࡀᐃ⩏ࡉࢀ
ἲ࡛ࡣ㸪↓
㣕⾜㸪ேཬ
ࢆ┠ⓗ࡟ᐃ

ே⯟✵ᶵࡣ

the impact⯟✵஺㏻⟶⌮㡿ᇦ
of weather data forecast
error, we needplanned flight route. Also, DARP flight plansͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ
more reliable data on original flight planning andinclude the same weather data parameters along the┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊreal time aircraft performance. The scope of thisupdated flight route. As QAR data provides real time
research is expected to be enhanced with a series ofmeasured data of the above parameters, aࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟
QAR (Quick Access Recorder) data andcomprehensive and quantitative evaluation can be✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿corresponding
filed flight plan data that had been
conducted on the forecast error on weather data. This⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
acquired from a national airline company between
will provide a concrete platform to review theࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
May 2015 and July 2017. Figure 9 illustrates thedeviation of aircraft performance from actual valuesࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪acquired QAR data. Data are mainly based on flights
due to performance calculations based on forecast㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇ
between PHNL and RJTT / RJAA, and flights fromweather data. Evaluations are planned to be࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
RJTT / RJAA to San Francisco (ICAO code: KSFO)
conducted for deviations on operational benefits andࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
/ Los Angeles (ICAO code: KLAX). It is clear fromenhanced benefits from 4D-TBO application.
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
the color
distinction that DARP flights are mainly
Obtained results would pave the way to a moreୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
concentrated on inbound flights to Tokyo fromrealistic understanding on the benefits due to currentࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛
Honolulu and outbound flights from Tokyo to theand future improvements in operational proceduresᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘West coast of the United States.
while emphasizing the challenges of the current
Flight plan data generated by airline operators
system that have to overcome in order to meet the࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
consist of wind aloft data that include wind
future expectations.
࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪ཬࡧ➨ 132 ᮲ࡢ 3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ࠸࡚ㄝ᫂ࡍࡿࠋ
2.1.1 㣕⾜ࡍࡿ✵ᇦ↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせ࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
ࢀࡢ࠶ࡿ✵ᇦ࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ 150m ௨ୖࡢ㧗ࡉࡢ✵ᇦ(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
Fig. 9 QAR data on DARP flights.
 ཱྀ㞟୰ᆅ༊ࡢୖ✵



-1-

-12-

平成30年度（第18回）電子航法研究所研究発表会

 !http://www/jmbsc.or.jp/hp/online/f-online0a.html
㸯㸬ᑠᆺ↓ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ
[1] ICAO FIR World – ICAO GIS.
[7] Eurocontrol Experiment Center. User Manualhttps://gis.icao.int/firmsd/
forthe Base of Aircraftͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ
Data (BADA) Revision⯟✵஺㏻⟶⌮㡿ᇦ
[2] Japan Aeronautical Information Service Center.
3.11. EEC Technical/Scientific Report,┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊhttps://aisjapan.mlit.go.jp/
No.13/04/16-01, 2013.
[3] PACOTS Flight Planning Guidance, Federal[8] Bach R E and Paielli R A., “A User guide for1 ࡣࡌࡵ࡟
ࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟Aviation Administration, March 2018.
Smoothing Air Traffic Radar Data.” NASA
[4]ᖹᡂ
27Preferredᖺ 12 ᭶࡟⯟✵ἲࡢᨵṇࡀ᪋⾜ࡉࢀ✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿
User
Route (UPR) Flight Planning
Technical Reports, NASA/TM 216520, 2014.
࡚࠿ࡽ2
ᖺ㏆ࡃࡀ⤒㐣ࡋ㸪ከࡃࡢ↓ே⯟✵ᶵ⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
Guidance, Federal Aviation Administration,[9] Wickramasinghe N K, Brown M, Miyamoto M࣮ࣘࢨ࣮ࡀ㣕⾜ࡢ࣮ࣝࣝ㸪㣕⾜ྍ⬟࡞✵ᇦ࡟ࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
March 2018.
and Miyazawa Y., “Effects of Aircraft Mass andࡘ࠸࡚ㄆ㆑ࡍࡿࡼ࠺࡟࡞ࡗ࡚ࡁࡓࠋ↓ே⯟✵ࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪[5] 0/<'CDARP 65  – 4
Weather
Data
Errorson
Trajectoryᶵࡢ฼ά⏝ࡣ㸪㛵ಀ┬ᗇ㐃⤡఍㆟࡟࠾ࡅࡿᐁ
㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇB> #8@+B>C:Optimization and Benefits Estimation,” AIAAẸ༠㆟఍࡟࠾࠸࡚㸪ᢏ⾡㛤Ⓨ࡜⎔ቃᩚഛ࡟㛵࡜ࢆ࠸࠺ࠋ
55 %B>2C./=9)*Modeling㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
and Simulation Technologies[1]㸪ᑠᆺ↓ேᶵࡢ
ࡍࡿ࣮ࣟࢻ࣐ࢵࣉࡀ♧ࡉࢀࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
("C03,. 2017.
Conference, SCITECH2016, San Diego, 2016.
฼⏝ࢆಁ㐍ࡋࡘࡘᏳ඲☜ಖࢆࡍࡿࡓࡵࡢไᗘ
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
[6] Japan Meteorological Business Support
సࡾࡢ᳨ウࡀ㐍ࡵࡽࢀ࡚࠸ࡿࠋ᳨ウ࡟࠾࠸࡚ୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
Center Online Data Service.
ࡣ㸪ไᗘタィ࡟㛵ࡋ࡚ࡣ㸪฼⏝ಁ㐍࡜Ᏻ඲☜ࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ಖࡢࣂࣛࣥࢫࢆ࡜ࡿࡇ࡜ࡀ㔜せ࡜ࡉࢀ࡚࠸ࡿ
ᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘
[2]ࠋࡋ࠿ࡋ㸪ᨵṇ⯟✵ἲ࡟ࡼࡾ↓ே⯟✵ᶵ࡜⯟✵ᶵࡢ㣕⾜⠊ᅖࡀ࠶ࡿ⛬ᗘศ㞳ࡉࢀࡘࡘ࠶ ࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
ࡿ୍᪉࡛㸪౛እ࡜࡞ࡿ㣕⾜ࡣከࡃᏑᅾࡋ㸪✵࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲᇦ฼⏝⪅ࡢᏳ඲ࢆ☜ಖࡍࡿࡓࡵ࡟ࡣ࠸ࡃࡘࡶ
᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼࡢㄢ㢟ࡀ࠶ࡿࠋ
ࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ ᮏ✏࡛ࡣ㸪ࡇࢀࡽࡢㄢ㢟ࡢࡦ࡜ࡘ࡜⪃࠼ࡽ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜ࢀࡿ㸪᫖௒ࢻ࣮ࣟࣥ࡜࿧ࡤࢀࡿࡼ࠺࡞ᑠᆺ↓ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪ேᶵࡀ㣕⾜ࡍࡿ✵ᇦ࡛ࡢ⯟✵ᶵ㐠⯟࡟ࡘ࠸࡚ཬࡧ➨ 132 ᮲ࡢ 3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉⪃ᐹࡍࡿࠋ✵ እࡢప㧗ᗘࢆ㣕⾜ࡍࡿ⯟✵ᶵ౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪࡜ᑠᆺ↓ேᶵࡢ㣕⾜࢚ࣜ࢔ࡀ➇ྜࡍࡿྍ⬟ᛶ୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧࡀ࠶ࡾ㸪ᑠᆺ↓ேᶵࡢ㣕⾜ᩘࡀቑຍࡍࢀࡤ᥋᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
㏆ࡍࡿ☜⋡ࡶ㧗ࡃ࡞ࡿࠋ௒ᚋ฼ά⏝ࡀಁ㐍ࡉ࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕ࢀ㣕⾜㟂せࡀ㧗ࡲࡿ࡜ᛮࢃࢀࡿᑠᆺ↓ேᶵࡀ⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ㣕⾜ࡍࡿ࢚ࣜ࢔ࡢᏳ඲☜ಖࡢࡓࡵ࡟ࡶ㸪᪩ᛴ࠸࡚ㄝ᫂ࡍࡿࠋ
࡟ྲྀࡾ⤌ࡲ࡞ࡅࢀࡤ࡞ࡽ࡞࠸ㄢ㢟ࡢࡦ࡜ࡘ࡛
2.1.1 㣕⾜ࡍࡿ✵ᇦ↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせ࠶ࡿࠋ࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
2 ᨵṇ⯟✵ἲ࡟ࡼࡿチྍᢎㄆ࡟ࡘ࠸࡚ ᨵṇ⯟✵ἲ࡟ࡼࡾ㸪↓ே⯟✵ᶵࡀᐃ⩏ࡉࢀ(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
㣕⾜ࡢ࣮ࣝࣝࡀᩚ⌮ࡉࢀࡓࠋ⯟✵ἲ࡛ࡣ㸪↓ࢀࡢ࠶ࡿ✵ᇦ
ே⯟✵ᶵࡢ㣕⾜ࡀ㸪᭷ே⯟✵ᶵࡢ㣕⾜㸪ேཬ࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦࡧ≀௳➼࡟ᙳ㡪ࢆཬࡤࡉ࡞࠸ࡇ࡜ࢆ┠ⓗ࡟ᐃ࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ 150m ௨ୖࡢ㧗ࡉࡢ✵ࡵࡽࢀ࡚࠸ࡿࠋ
ᇦ
2.1 ᨵṇ⯟✵ἲࡢᴫせ(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵ ⯟✵ἲ➨ 2 ᮲➨ 22 㡯࡟࠾࠸࡚↓ே⯟✵ᶵࡣ࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
ḟࡢࡼ࠺࡟ᐃ⩏ࡉࢀ࡚࠸ࡿࠋ
ཱྀ㞟୰ᆅ༊ࡢୖ✵-1-13-

平成30年度（第18回）電子航法研究所研究発表会

平成30年度（第18回）電子航法研究所研究発表会ࡍࡿ✵ᇦ࡟࠾ࡅࡿ⯟✵ᶵ㐠⯟࡟㛵ࡍࡿ⪃ᐹ⯟✵஺㏻⟶⌮㡿ᇦ ͤᖹᯘ༤Ꮚ㸪⹡㇂኱ᆅ┘ど㏻ಙ㡿ᇦ ἙᮧᬡᏊࠗࡇࡢἲᚊ࡟࠾࠸࡚ࠕ↓ே⯟✵ᶵࠖ࡜ࡣ㸪⯟✵ࡢ⏝࡟౪ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㣕⾜ᶵ㸪ᅇ㌿⩼⯟✵ᶵ㸪⁥✵ᶵ㸪㣕⾜⯪ࡑࡢ௚ᨻ௧࡛ᐃ
ࡵࡿᶵჾ࡛࠶ࡗ࡚ᵓ㐀ୖேࡀ஌ࡿࡇ࡜ࡀ࡛
ࡁ࡞࠸ࡶࡢࡢ࠺ࡕ㸪㐲㝸᧯సཪࡣ⮬ື᧯⦪㸦ࣉࣟࢢ࣒ࣛ࡟ࡼࡾ⮬ືⓗ࡟᧯⦪ࢆ⾜࠺ࡇ࡜ࢆ࠸࠺ࠋ㸧࡟ࡼࡾ㣕⾜ࡉࡏࡿࡇ࡜ࡀ࡛ࡁࡿ
ࡶࡢ㸦ࡑࡢ㔜㔞ࡑࡢ௚ࡢ஦⏤ࢆ຺᱌ࡋ࡚ࡑ
ࡢ㣕⾜࡟ࡼࡾ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲୪ࡧ࡟ᆅ
ୖཬࡧỈୖࡢேཬࡧ≀௳ࡢᏳ඲ࡀᦆ࡞ࢃࢀ
ࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࢆ㝖ࡃࠋ㸧ࢆ࠸࠺ࠋ࠘

ࡀ᪋⾜ࡉࢀ
↓ே⯟✵ᶵ
⬟࡞✵ᇦ࡟
ࠋ↓ே⯟✵
࡟࠾ࡅࡿᐁ
ቃᩚഛ࡟㛵
ᆺ↓ேᶵࡢ
ࡓࡵࡢไᗘ
ウ࡟࠾࠸࡚
㐍࡜Ᏻ඲☜
ࡉࢀ࡚࠸ࡿ
ே⯟✵ᶵ࡜
ࡉࢀࡘࡘ࠶
Ꮡᅾࡋ㸪✵
ࡣ࠸ࡃࡘࡶ

 ࡞࠾㸪Ᏻ඲ࡀᦆ࡞ࢃࢀࡿ࠾ࡑࢀࡀ࡞࠸ࡶࡢ
࡜ࡋ࡚ᅜᅵ஺㏻┬௧࡛ᐃࡵࡿࡶࡢࡣ㸪⯟✵ἲ᪋⾜つ๎㸦௨ୗ㸪ࠕ᪋⾜つ๎ࠖ࡜ࡍࡿࠋ㸧࡟ࡼࡾ㸪㔜㔞 200 ࢢ࣒ࣛᮍ‶࡜ࡉࢀ࡚࠸ࡿࠋ 㣕⾜ࡢ࣮ࣝࣝ࡟㛵ࡋ࡚ࡣ㸪➨ 132 ᮲㸦㣕⾜ࡢ⚗Ṇ✵ᇦ㸧㸪➨ 132 ᮲ࡢ 2㸦㣕⾜ࡢ᪉ἲ㸧㸪ཬࡧ➨ 132 ᮲ࡢ 3㸦ᤚ⣴㸪ᩆຓ➼ࡢࡓࡵࡢ≉౛㸧࡟ᐃࡵࡽࢀ࡚࠾ࡾ㸪㣕⾜⚗Ṇ✵ᇦࡢヲ⣽㸪୪ࡧ࡟㣕⾜⚗Ṇ✵ᇦ࡟࠾ࡅࡿ㣕⾜ࡢチྍཬࡧ᪉ἲࡢヲ⣽࡟㛵ࡋ࡚ࡣ㸪᪋⾜つ๎࡛ᐃࡵࡽࢀ
࡚࠸ࡿࠋᨵṇ⯟✵ἲ࡟࠾࠸࡚↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ✵ᇦཬࡧ㣕⾜ࡢ᪉ἲ࡟㛵ࡍࡿᴫせ࡟ࡘ࠸࡚ㄝ᫂ࡍࡿࠋ
2.1.1 㣕⾜ࡍࡿ✵ᇦ↓ே⯟✵ᶵࡀ㣕⾜ࡍࡿ࡟࠶ࡓࡾチྍࢆᚲせ࡜ࡍࡿ✵ᇦࡣ㸪ḟࡢ㏻ࡾ࡛࠶ࡿࠋ
(a) ⯟✵ᶵࡢ⯟⾜ࡢᏳ඲࡟ᙳ㡪ࢆཬࡰࡍ࠾ࡑ
ࢀࡢ࠶ࡿ✵ᇦ࣭✵ ➼ࡢ࿘㎶ࡢୖ✵ࡢ✵ᇦ࣭ᆅ⾲ཪࡣỈ㠃࠿ࡽ 150m ௨ୖࡢ㧗ࡉࡢ✵ᇦ(b) ேࡲࡓࡣᐙᒇࡢᐦ㞟ࡋ࡚࠸ࡿᆅᇦࡢୖ✵࣭ᅜໃㄪᰝࡢ⤖ᯝࢆཷࡅタᐃࡉࢀ࡚࠸ࡿே
ཱྀ㞟୰ᆅ༊ࡢୖ✵

ࡘ࡜⪃࠼ࡽ
࠺࡞ᑠᆺ↓
⯟࡟ࡘ࠸࡚
ࡍࡿ⯟✵ᶵ
ࡍࡿྍ⬟ᛶ
ຍࡍࢀࡤ᥋
⏝ࡀಁ㐍ࡉ
ᆺ↓ேᶵࡀ
࡟ࡶ㸪᪩ᛴ
ࡢࡦ࡜ࡘ࡛

ࡘ࠸࡚
ࡀᐃ⩏ࡉࢀ
ἲ࡛ࡣ㸪↓
㣕⾜㸪ேཬ
ࢆ┠ⓗ࡟ᐃ

ே⯟✵ᶵࡣ

-1-

-14-