AIS data analysis and GIS based information sharing system

AIS広域モニタリングによる危険物積
載タンカーリスク軽減対策について
沢野伸浩
星稜女子短期大学
AIS（Automatic Identification System)
• SOLAS条約によって300トン以上の船舶に
搭載が義務付けられた装置
• 自船の位置、速度、目的地、識別子、大き
さ、積み荷種類等を「データ放送」により周
囲に送信
• VHF帯の電波を使う
• 通信原理はモデムやFAXとほぼ同じ
• 転送されるデータは「デジタル」
AIS データの受信と国際的共有
gCAPTAINによる船舶航行データのリアルタイム配信
AIS データの受信と国際的共有
残念ながらオホーツク海エリアの情報はない
自前で受信！
AIS Data Receiving Station at Soya
42km = 22.5 NM
AIS Antenna Position
141.948009E, 45.516399B
AIS Data Receiving Station at Soya
↓AIS Antenna
Roof top of Soya Fishery Association, about 15mH
AIS Data Receiving Station at Soya
AIS Data Receiving Station at Soya
Transas T300
Data recoding laptop
How we process AIS data
09/06/01 00:00:03,$GPRMC,135736.148,V,36000.0000,N,72000.0000,E,0.00,,091203,,*3B
09/06/01 00:00:03,!AIRSS,00129,00227,00096,00189*58
09/06/01 00:00:03,!AIVDM,1,1,,A,144OQg002?b;6WnJ>klJ<`9N0@Lt,0*6F
09/06/01
00:00:04,$GPGGA,135737.148,0000.0000,N,00000.0000,E,0,00,50.0,0.0,M,0.0,M,0.0,0000*7F
09/06/01 00:00:04,$GPRMC,135737.148,V,36000.0000,N,72000.0000,E,0.00,,091203,,*3A
09/06/01 00:00:04,!AIVDM,1,1,,B,1459rbP00h::ImhJ>?F:S8UP05kd,0*1B
09/06/01 00:00:05,$PRPT[Pos. unknown][Zones = 1][A=2087;B=2088]*5C
09/06/01 00:00:05,$AIALR,,,,*5709/06/01 01:20:03,$PRPT[Pos. unknown][Zones
= ][A=2087;B=2088]*5C
09/06/01
01:20:04,$GPGGA,151736.856,0000.0000,N,00000.0000,E,0,00,50.0,0.0,M,0.0,M,0.0,0000*7A
09/06/01 01:20:04,$GPRMC,151736.856,V,36000.0000,N,72000.0000,E,0.00,,091203,,*3F
09/06/01 01:20:04,!AIVDM,1,1,,A,144OQg002B:92<6J=c;bS8SN061<,0*15
09/06/01 01:20:04,!AIVDM,1,1,,B,1459rbP00k:9`B<J>1obK`aN061<,0*5D
6bit character code that have to be decoded into normal character
How we process AIS data
09/04/01 00:01:34,!AIVDM,2,2,2,B,5,0,273149600,0,7941849,UFMK ,KAPITAN
SERGIEVSKIY ,79,1032798,1,02041800,6.0,VLDV-KORSAKOV-VLDV ,0,0
09/04/01 00:01:35,!AIVDM,1,1,,B,1,0,273149600,0,0,13.2,0,141,46,24.900,45,49,42.780,270.0,276,59,0,0,0,32937
09/04/01 00:01:37,!AIVDM,1,1,,B,1,0,273824020,0,0,9.0,0,141,48,20.340,45,49,49.140,276.5,341,4,0,0,0,2282
09/04/01 00:01:45,!AIVDM,1,1,,A,1,0,273149600,0,0,13.2,0,141,46,21.420,45,49,42.780,271.0,276,11,0,0,0,23784
09/04/01 00:01:47,!AIVDM,1,1,,A,1,0,273824020,0,0,9.0,0,141,48,18.300,45,49,49.380,277.8,342,14,0,0,0,114691
09/04/01 00:01:51,!AIVDM,1,1,,B,1,0,273450570,0,0,11.1,0,141,52,16.500,45,55,01.200,117.0,110,21,0,0,0,230171
09/04/01 00:02:11,!AIVDM,1,1,,B,1,0,273450570,0,0,11.1,0,141,52,21.360,45,54,59.460,117.5,112,60,0,0,0,245764
09/04/01 00:02:14,!AIVDM,1,1,,B,1,0,273149600,0,0,13.2,0,141,46,12.600,45,49,42.900,271.0,275,38,0,0,0,67190
09/04/01 00:02:18,!AIVDM,1,1,,B,1,0,273824020,0,0,9.0,0,141,48,11.820,45,49,50.340,280.7,338,47,0,0,0,81923
09/04/01 00:02:21,!AIVDM,1,1,,A,1,0,273450570,0,0,11.1,0,141,52,23.460,45,54,58.620,118.2,113,50,0,0,0,165743
09/04/01 00:02:24,!AIVDM,1,1,,A,1,0,273149600,0,0,13.2,0,141,46,09.480,45,49,42.960,270.0,275,51,0,0,0,2304
09/04/01 00:02:25,!AIVDM,2,2,9,A,5,0,273824020,0,8857796,UEFY ,NIKOLAY
KASATKIN ,70,332291,1,04031700,4.5,$$$VLADIVOSTOK$$$ ,0,0
Decoded for one path of the vessel
How we process AIS data for GIS
Date and time
Name of the vessel
destination
X Y coordinate
Data have to be arranged one line for one each navigation of the vessel
Putting data into GIS
2009.04.01 – 2009.04.30 (17-23 were not acquired)
Putting data into GIS
2009.05.01 – 2009.05.31
Putting data into GIS
2009.06.01 – 2009.06.30
解析１航行密度の解析
航行密度の高い場所を把握
GSH
Hamada
Time-line & Statistical Analysis
Channel line
Some 40km channel of Soya is divided into 8 parts (about 5km each part)
Time-line & Statistical Analysis
April, 2009 (126 vessels)
８
７
６
５
←（Ｅ２Ｗ）
→（Ｗ２Ｅ）
４
３
2
１
-30
-20
-10
0
10
20
30
Number of vessels crossed the channel line
Time-line & Statistical Analysis
May, 2009 (277 vessels)
８
７
６
５
←（Ｅ２Ｗ）
→（Ｗ２Ｅ）
４
３
２
１
-80
-60
-40
-20
0
20
40
60
80
Number of vessels crossed the channel line
Time-line & Statistical Analysis
June, 2009 (462 vessels)
8
7
6
5
←（Ｅ２Ｗ）
→（Ｗ２Ｅ）
4
3
2
1
-150
-100
-50
0
50
100
150
Number of vessels crossed the channel line
実際にこんな場合が起こっ
ています。
５月２１日の例
結論
• 現状の解析は船舶は「危険物積載船舶」のみを抽
出しているわけではない。
• さらに年間にわたる全データの解析から、航行海
域の危険度の面的な分布を明らかにする（船の軌
跡ベクトルに沿って衝突確率分布でバッファリング
後、航行に応じて確率を積分する → 藤井理論）。
• が、面的な分布のみを明らかできても意味はなく、
微少領域（高密度領域）に船舶が入る（出る）時間
間隔をポアソン分布でモデル化し、その確率分布
を同時に把握する。
• 以上で、「衝突」「座礁」等の空間・時間的分布の確
率論的モデル化が可能となる。
• 「時間×空間」の確率論的モデルができると、「回
避予測」の合理性が担保できる。
Acknowledgements:
• This research was completely supported by the
Ministry of Education, Science, Sports and
Culture, Grant-in-Aid for Specific Research type B.
No. 19310110, Research Representative
Nobuhiro Sawano.
• Also, supported by the same grant of Grant-in-Aid
for Specific Research type B. No. 21310111,
Research Representative Sei’ichi Hamada.

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