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make generate_signals.py

master
김선중 1 year ago
parent
40d1c6824e
commit
142b9f51dd
19 changed files with 4442 additions and 1628 deletions
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  1. +5
    -1
      Data/networks/cmd.txt
  2. BIN
      Documents/1127_table_definition/table_definition_v0.8.4.xlsx
  3. +17
    -0
      Intermediates/node2num_cycles.json
  4. +215
    -0
      Intermediates/새 폴더/histid_1704431700.csv
  5. +215
    -0
      Intermediates/새 폴더/histid_1704431700_.csv
  6. +0
    -0
      Results/issues_generate_signals.txt
  7. +0
    -1
      Results/issues_intermediates.txt
  8. +0
    -0
      Results/issues_preprocess_daily.txt
  9. +716
    -0
      Results/sn_1704439800.add.xml
  10. +716
    -0
      Results/sn_1704440100.add.xml
  11. +716
    -0
      Results/sn_1704440400.add.xml
  12. +45
    -14
      Script/generate_signals.ipynb
  13. +786
    -0
      Script/generate_signals.py
  14. +0
    -587
      Script/get_intermediates.py
  15. +0
    -587
      Script/get_signals.py
  16. +0
    -19
      Script/preprocess_5min.ipynb
  17. +580
    -419
      Script/preprocess_daily.py
  18. +431
    -0
      Script/preprocess_daily_0.py
  19. BIN
      state_300.00.xml.gz

+ 5
- 1
Data/networks/cmd.txt View File

@ -1,3 +1,7 @@
sumo-gui -n sn.net.xml -a ../../Results/sn_1704411900.add.xml -r sn.rou.xml -d 100 --save-state.times 300
sumo-gui -n sn.net.xml -a ../../Results/sn_1704412200.add.xml -r sn.rou.xml -d 100 --load-state state_300.00.xml.gz
sumo-gui -n sn.net.xml -a ../../Results/sn_1704412200.add.xml -r sn.rou.xml -d 100 --load-state state_300.00.xml.gz
sumo-gui -n Data/networks/sn.net.xml -a Results/sn_1704411900.add.xml -r Data/networks/sn.rou.xml -d 100 --save-state.times 300
sumo-gui -n Data/networks/sn.net.xml -a Results/sn_1704411900.add.xml -r Data/networks/sn.rou.xml -d 100 --load-state state_300.00.xml.gz

BIN
Documents/1127_table_definition/table_definition_v0.8.4.xlsx View File


+ 17
- 0
Intermediates/node2num_cycles.json View File

@ -0,0 +1,17 @@
{
"c30": 4,
"i0": 3,
"i1": 4,
"i2": 4,
"i3": 4,
"i6": 4,
"i7": 4,
"i8": 4,
"i9": 4,
"u00": 3,
"u20": 4,
"u30": 4,
"u31": 4,
"u32": 4,
"u60": 4
}

+ 215
- 0
Intermediates/새 폴더/histid_1704431700.csv View File

@ -0,0 +1,215 @@
,inter_no,node_id,start_unix,phas_A,phas_B,duration,inc_edge_A,out_edge_A,inc_edge_B,out_edge_B
891,175,i0,1704431120,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
892,175,i0,1704431120,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
893,175,i0,1704431120,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
894,175,i0,1704431120,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
895,175,i0,1704431120,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
1539,176,i1,1704431120,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
1540,176,i1,1704431120,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
1541,176,i1,1704431120,3,3,40,571543469_02,-571542797_02.99,,
244,177,i2,1704431130,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
245,177,i2,1704431130,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
246,177,i2,1704431130,3,3,70,,,,
247,177,i2,1704431130,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
475,206,i7,1704431160,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
476,206,i7,1704431160,2,2,53,,,,
477,206,i7,1704431160,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
478,206,i7,1704431160,4,4,26,,,,
1364,178,i3,1704431180,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
1365,178,i3,1704431180,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
1366,178,i3,1704431180,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
1367,178,i3,1704431180,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
909,210,i6,1704431180,1,1,28,-571542115_01,571500535_01,,
910,210,i6,1704431180,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
911,210,i6,1704431180,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
912,210,i6,1704431180,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
913,210,i6,1704431180,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
328,202,i9,1704431200,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
329,202,i9,1704431200,2,2,114,,,,
1595,201,i8,1704431270,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
1596,201,i8,1704431270,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
1597,201,i8,1704431270,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
1598,201,i8,1704431270,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
1599,201,i8,1704431270,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
1250,175,i0,1704431301,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
1251,175,i0,1704431301,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
1252,175,i0,1704431301,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
1253,175,i0,1704431301,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
1254,175,i0,1704431301,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
311,176,i1,1704431301,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
312,176,i1,1704431301,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
313,176,i1,1704431301,3,3,40,571543469_02,-571542797_02.99,,
770,177,i2,1704431310,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
771,177,i2,1704431310,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
772,177,i2,1704431310,3,3,70,,,,
773,177,i2,1704431310,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
223,206,i7,1704431310,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
224,206,i7,1704431310,2,2,53,,,,
225,206,i7,1704431310,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
226,206,i7,1704431310,4,4,26,,,,
257,178,i3,1704431351,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
258,178,i3,1704431351,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
259,178,i3,1704431351,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
260,178,i3,1704431351,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
1578,202,i9,1704431360,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
1579,202,i9,1704431360,2,2,114,,,,
820,210,i6,1704431361,1,1,28,-571542115_01,571500535_01,,
821,210,i6,1704431361,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
822,210,i6,1704431361,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
823,210,i6,1704431361,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
824,210,i6,1704431361,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
609,201,i8,1704431440,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
610,201,i8,1704431440,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
611,201,i8,1704431440,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
612,201,i8,1704431440,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
613,201,i8,1704431440,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
4,206,i7,1704431460,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
5,206,i7,1704431460,2,2,53,,,,
6,206,i7,1704431460,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
7,206,i7,1704431460,4,4,26,,,,
270,175,i0,1704431480,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
271,175,i0,1704431480,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
272,175,i0,1704431480,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
273,175,i0,1704431480,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
274,175,i0,1704431480,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
933,176,i1,1704431480,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
934,176,i1,1704431480,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
935,176,i1,1704431480,3,3,40,571543469_02,-571542797_02.99,,
1259,177,i2,1704431490,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
1260,177,i2,1704431490,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
1261,177,i2,1704431490,3,3,70,,,,
1262,177,i2,1704431490,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
1033,178,i3,1704431520,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
1034,178,i3,1704431520,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
1035,178,i3,1704431520,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
1036,178,i3,1704431520,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
598,202,i9,1704431521,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
599,202,i9,1704431521,2,2,114,,,,
1293,210,i6,1704431541,1,1,28,-571542115_01,571500535_01,,
1294,210,i6,1704431541,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
1295,210,i6,1704431541,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
1296,210,i6,1704431541,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
1297,210,i6,1704431541,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
1245,201,i8,1704431610,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
1246,201,i8,1704431610,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
1247,201,i8,1704431610,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
1248,201,i8,1704431610,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
1249,201,i8,1704431610,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
896,206,i7,1704431610,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
897,206,i7,1704431610,2,2,53,,,,
898,206,i7,1704431610,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
899,206,i7,1704431610,4,4,26,,,,
815,175,i0,1704431660,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
816,175,i0,1704431660,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
817,175,i0,1704431660,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
818,175,i0,1704431660,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
819,175,i0,1704431660,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
1440,176,i1,1704431660,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
1441,176,i1,1704431660,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
1442,176,i1,1704431660,3,3,40,571543469_02,-571542797_02.99,,
138,177,i2,1704431670,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
139,177,i2,1704431670,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
140,177,i2,1704431670,3,3,70,,,,
141,177,i2,1704431670,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
1354,202,i9,1704431681,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
1355,202,i9,1704431681,2,2,114,,,,
559,178,i3,1704431690,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
560,178,i3,1704431690,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
561,178,i3,1704431690,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
562,178,i3,1704431690,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
178,210,i6,1704431721,1,1,28,-571542115_01,571500535_01,,
179,210,i6,1704431721,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
180,210,i6,1704431721,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
181,210,i6,1704431721,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
182,210,i6,1704431721,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
677,206,i7,1704431760,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
678,206,i7,1704431760,2,2,53,,,,
679,206,i7,1704431760,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
680,206,i7,1704431760,4,4,26,,,,
787,201,i8,1704431780,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
788,201,i8,1704431780,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
789,201,i8,1704431780,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
790,201,i8,1704431780,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
791,201,i8,1704431780,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
1288,175,i0,1704431840,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
1289,175,i0,1704431840,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
1290,175,i0,1704431840,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
1291,175,i0,1704431840,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
1292,175,i0,1704431840,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
365,176,i1,1704431840,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
366,176,i1,1704431840,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
367,176,i1,1704431840,3,3,40,571543469_02,-571542797_02.99,,
1022,202,i9,1704431841,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
1023,202,i9,1704431841,2,2,114,,,,
637,177,i2,1704431850,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
638,177,i2,1704431850,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
639,177,i2,1704431850,3,3,70,,,,
640,177,i2,1704431850,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
1189,178,i3,1704431860,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
1190,178,i3,1704431860,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
1191,178,i3,1704431860,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
1192,178,i3,1704431860,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
672,210,i6,1704431901,1,1,28,-571542115_01,571500535_01,,
673,210,i6,1704431901,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
674,210,i6,1704431901,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
675,210,i6,1704431901,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
676,210,i6,1704431901,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
485,206,i7,1704431910,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
486,206,i7,1704431910,2,2,53,,,,
487,206,i7,1704431910,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
488,206,i7,1704431910,4,4,26,,,,
1398,201,i8,1704431950,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
1399,201,i8,1704431950,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
1400,201,i8,1704431950,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
1401,201,i8,1704431950,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
1402,201,i8,1704431950,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
146,202,i9,1704432001,1,1,46,571510152_02,-571510152_01,571510152_01,571510152_01.65
147,202,i9,1704432001,2,2,114,,,,
173,175,i0,1704432020,1,1,43,-571542797_02,571500487_01,-571500487_01,571542797_02
174,175,i0,1704432020,2,2,45,-571500487_01,571545870_01,-571542797_02,571510153_01
175,175,i0,1704432020,3,3,33,571545870_02,571510153_01,571545870_02,571542797_02
176,175,i0,1704432020,3,4,22,571545870_02,571510153_01,571510153_02,571545870_01
177,175,i0,1704432020,4,4,37,571510153_02,571500487_01,571510153_02,571545870_01
848,176,i1,1704432020,1,1,37,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
849,176,i1,1704432020,2,2,103,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
850,176,i1,1704432020,3,3,40,571543469_02,-571542797_02.99,,
1166,177,i2,1704432030,1,1,43,-571542809_01,571542811_01,571542811_02,571542809_01
1167,177,i2,1704432030,2,2,27,571542811_02,571542107_01,-571542809_01,571542809_01
1168,177,i2,1704432030,3,3,70,,,,
1169,177,i2,1704432030,4,4,40,-571542809_01,571542811_01,571542107_02,571542809_01
185,178,i3,1704432030,1,1,38,571540304_02,571556450_01,571556450_02,571540304_01
186,178,i3,1704432030,2,2,39,571556450_02,571500475_01,571540304_02,571540303_01
187,178,i3,1704432030,3,3,43,571540303_02.21,571556450_01,571540303_02.21,571500475_01
188,178,i3,1704432030,4,4,22,-571500475_01,571540303_01,-571500475_01,571540304_01
1350,206,i7,1704432060,1,1,45,-571511538_02,571542073_02,571542073_01,571511538_02
1351,206,i7,1704432060,2,2,53,,,,
1352,206,i7,1704432060,3,3,26,-571511538_02,571542073_02,571542073_01,571511538_02
1353,206,i7,1704432060,4,4,26,,,,
1193,210,i6,1704432081,1,1,28,-571542115_01,571500535_01,,
1194,210,i6,1704432081,1,2,15,-571542115_01,571500535_01,571500535_02.18,571542115_01
1195,210,i6,1704432081,2,2,43,571500535_02.18,571511538_01,571500535_02.18,571542115_01
1196,210,i6,1704432081,3,3,70,571511538_02.121,571542115_01,571511538_02.121,571500585_01
1197,210,i6,1704432081,4,4,24,571500585_02,571511538_01,571500585_02,571500535_01
1110,175,i0,1704432120,1,1,24,-571542797_02,571500487_01,-571500487_01,571542797_02
1111,175,i0,1704432120,2,2,25,-571500487_01,571545870_01,-571542797_02,571510153_01
1112,175,i0,1704432120,3,3,18,571545870_02,571510153_01,571545870_02,571542797_02
1113,175,i0,1704432120,3,4,13,571545870_02,571510153_01,571510153_02,571545870_01
1114,175,i0,1704432120,4,4,20,571510153_02,571500487_01,571510153_02,571545870_01
135,176,i1,1704432120,1,1,21,-571542810_01,-571542797_02.99,571542797_02.99,571542810_01
136,176,i1,1704432120,2,2,57,-571542810_01,-571542797_02.99,-571542810_01,571543469_01
137,176,i1,1704432120,3,3,22,571543469_02,-571542797_02.99,,
442,201,i8,1704432120,1,1,33,-571500569_01,571500583_02,-571500569_01,571500618_01
443,201,i8,1704432120,2,2,36,571500618_02,571500583_02,571500618_02,571500617_01
444,201,i8,1704432120,3,3,25,571500617_02,571500618_01,571500618_02,571500617_01
445,201,i8,1704432120,4,4,58,571500617_02,571500618_01,571500617_02,571500569_01
446,201,i8,1704432120,5,5,18,571500583_01,571500617_01,571500583_01,571500569_01
1145,178,i3,1704432130,1,1,22,571540304_02,571556450_01,571556450_02,571540304_01
1146,178,i3,1704432130,2,2,23,571556450_02,571500475_01,571540304_02,571540303_01
1147,178,i3,1704432130,3,3,25,571540303_02.21,571556450_01,571540303_02.21,571500475_01
1148,178,i3,1704432130,4,4,13,-571500475_01,571540303_01,-571500475_01,571540304_01
183,202,i9,1704432140,1,1,40,571510152_02,-571510152_01,571510152_01,571510152_01.65
184,202,i9,1704432140,2,2,99,,,,
844,206,i7,1704432150,1,1,27,-571511538_02,571542073_02,571542073_01,571511538_02
845,206,i7,1704432150,2,2,32,,,,
846,206,i7,1704432150,3,3,15,-571511538_02,571542073_02,571542073_01,571511538_02
847,206,i7,1704432150,4,4,15,,,,

+ 215
- 0
Intermediates/새 폴더/histid_1704431700_.csv View File

@ -0,0 +1,215 @@
,inter_no,node_id,start_unix,phas_A,phas_B,duration,inc_edge_A,out_edge_A,inc_edge_B,out_edge_B
517,175.0,i0,1704431120.0,1,1,43.0,-571542797_02,571500487_01,-571500487_01,571542797_02
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524,176.0,i1,1704431120.0,3,3,40.0,571543469_02,-571542797_02.99,,
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527,177.0,i2,1704431130.0,3,3,70.0,,571542107_01,,
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532,206.0,i7,1704431160.0,4,4,26.0,,571542073_02,,
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537,210.0,i6,1704431180.0,1,1,28.0,-571542115_01,571500535_01,,
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543,202.0,i9,1704431200.0,2,2,114.0,,-571510152_01,,
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556,176.0,i1,1704431301.0,3,3,40.0,571543469_02,-571542797_02.99,,
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559,177.0,i2,1704431310.0,3,3,70.0,,571542107_01,,
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564,206.0,i7,1704431310.0,4,4,26.0,,571542073_02,,
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570,202.0,i9,1704431360.0,2,2,114.0,,-571510152_01,,
571,210.0,i6,1704431361.0,1,1,28.0,-571542115_01,571500535_01,,
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592,176.0,i1,1704431480.0,3,3,40.0,571543469_02,-571542797_02.99,,
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597,178.0,i3,1704431520.0,1,1,38.0,571540304_02,571556450_01,571556450_02,571540304_01
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602,202.0,i9,1704431521.0,2,2,114.0,,-571510152_01,,
603,210.0,i6,1704431541.0,1,1,28.0,-571542115_01,571500535_01,,
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624,176.0,i1,1704431660.0,3,3,40.0,571543469_02,-571542797_02.99,,
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+ 0
- 0
Results/issues_generate_signals.txt View File


+ 0
- 1
Results/issues_intermediates.txt View File

@ -1 +0,0 @@
1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : 175

+ 0
- 0
Results/issues_preprocess_daily.txt View File


+ 716
- 0
Results/sn_1704439800.add.xml View File

@ -0,0 +1,716 @@
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+ 716
- 0
Results/sn_1704440100.add.xml View File

@ -0,0 +1,716 @@
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</additional>

+ 716
- 0
Results/sn_1704440400.add.xml View File

@ -0,0 +1,716 @@
<additional>
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<phase duration="1.0" state="ggggrgggg"/>
<phase duration="40.0" state="ggggGgggg"/>
<phase duration="4.0" state="ggggygggg"/>
<phase duration="1.0" state="ggggrgggg"/>
<phase duration="28.0" state="ggggrgggg"/>
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<tlLogic id="u20" type="static" programID="u20_prog" offset="-150.0">
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<phase duration="1.0" state="ggrggg"/>
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<phase duration="1.0" state="ggrggg"/>
<phase duration="35.0" state="ggrggg"/>
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<phase duration="1.0" state="ggrggg"/>
<phase duration="65.0" state="ggGggg"/>
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<phase duration="1.0" state="ggrggg"/>
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<phase duration="4.0" state="ggrggg"/>
<phase duration="1.0" state="ggrggg"/>
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<phase duration="22.0" state="ggrggg"/>
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<phase duration="65.0" state="ggGggg"/>
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<phase duration="35.0" state="ggrggg"/>
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<phase duration="1.0" state="ggrggg"/>
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<phase duration="4.0" state="ggyggg"/>
<phase duration="1.0" state="ggrggg"/>
<phase duration="35.0" state="ggrggg"/>
</tlLogic>
<tlLogic id="u30" type="static" programID="u30_prog" offset="-80.0">
<phase duration="33.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggGggg"/>
</tlLogic>
<tlLogic id="u31" type="static" programID="u31_prog" offset="-80.0">
<phase duration="33.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="33.0" state="ggggGggg"/>
<phase duration="4.0" state="ggggyggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="34.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="38.0" state="ggggrggg"/>
<phase duration="4.0" state="ggggrggg"/>
<phase duration="1.0" state="ggggrggg"/>
<phase duration="17.0" state="ggggrggg"/>
</tlLogic>
<tlLogic id="u32" type="static" programID="u32_prog" offset="-80.0">
<phase duration="33.0" state="gggggggG"/>
<phase duration="4.0" state="gggggggy"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="34.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="38.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="17.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="33.0" state="gggggggG"/>
<phase duration="4.0" state="gggggggy"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="34.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="38.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="17.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="33.0" state="gggggggG"/>
<phase duration="4.0" state="gggggggy"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="34.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="38.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="17.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="33.0" state="gggggggG"/>
<phase duration="4.0" state="gggggggy"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="34.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="38.0" state="gggggggr"/>
<phase duration="4.0" state="gggggggr"/>
<phase duration="1.0" state="gggggggr"/>
<phase duration="17.0" state="gggggggr"/>
</tlLogic>
<tlLogic id="u60" type="static" programID="u60_prog" offset="-100.0">
<phase duration="23.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="10.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="38.0" state="ggggggggG"/>
<phase duration="4.0" state="ggggggggy"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="65.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="19.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="23.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="10.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="38.0" state="ggggggggG"/>
<phase duration="4.0" state="ggggggggy"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="65.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="19.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="23.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="10.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="38.0" state="ggggggggG"/>
<phase duration="4.0" state="ggggggggy"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="65.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="19.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="23.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="10.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="38.0" state="ggggggggG"/>
<phase duration="4.0" state="ggggggggy"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="65.0" state="ggggggggr"/>
<phase duration="4.0" state="ggggggggr"/>
<phase duration="1.0" state="ggggggggr"/>
<phase duration="19.0" state="ggggggggr"/>
</tlLogic>
</additional>

+ 45
- 14
Script/generate_signals.ipynb View File

@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@ -102,7 +102,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -172,7 +172,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@ -232,7 +232,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 8,
"metadata": {},
"outputs": [
{
@ -258,7 +258,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"outputs": [
{
@ -281,7 +281,7 @@
" 'u60': 4}"
]
},
"execution_count": 7,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -305,7 +305,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@ -328,7 +328,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@ -351,7 +351,7 @@
" 'u60': 4}"
]
},
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@ -362,7 +362,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"metadata": {},
"outputs": [
{
@ -410,7 +410,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"metadata": {},
"outputs": [
{
@ -729,7 +729,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@ -781,6 +781,37 @@
"SIGTABLE = assign_red_yellow(Sigtable)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1577804400\n",
"2145884400\n",
"4021\n",
"4021\n"
]
}
],
"source": [
"max_unix, min_unix = int(datetime(2020, 1, 1).timestamp()), int(datetime(2038, 1, 1).timestamp())\n",
"print(max_unix)\n",
"print(min_unix)\n",
"history = pd.read_csv('../Data/tables/history.csv', index_col=0)\n",
"K = 0\n",
"for _, row in history.iterrows():\n",
" unixbool = min_unix <= row['end_unix'] <= max_unix\n",
" print(min_unix, row['end_unix'], max_unix)\n",
" if not unixbool:\n",
" K += 1\n",
"print(K)\n",
"print(len(history))"
]
},
{
"cell_type": "code",
"execution_count": 13,

+ 786
- 0
Script/generate_signals.py View File

@ -0,0 +1,786 @@
import pandas as pd
import numpy as np
import os, sys
import json
import copy
from tqdm import tqdm
import sumolib, traci
from datetime import datetime
class SignalGenerator():
def __init__(self):
self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.issues = []
self.midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())
self.next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())
self.fsecs = range(self.midnight, self.next_day, 5) # fsecs : unix time by Five SECondS
self.fmins = range(self.midnight, self.next_day, 300) # fmins : unix time by Five MINuteS
self.present_time = datetime.now().replace(month=1, day=5).timestamp()
print(self.present_time)
self.present_time = max([fmin for fmin in list(self.fmins) if fmin <= self.present_time])
self.adder = 600
# 1. 데이터 준비
def prepare_data(self):
print("1. 데이터를 준비합니다.")
self.load_networks()
self.load_tables()
self.check_networks()
self.check_tables()
self.prepare_auxiliaries()
# 1-1. 네트워크 불러오기
def load_networks(self):
self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml'))
print("1-1. 네트워크가 로드되었습니다.")
# 1-2. 테이블 불러오기
def load_tables(self):
# 모든 컬럼에 대하여 데이터타입 지정
loading_dtype = {
'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int',
'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str',
'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str',
'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float',
'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str'
}
for alph in ['A', 'B']:
for j in range(1,9):
loading_dtype[f'angle_{alph}{j}'] = 'str'
loading_dtype[f'dura_{alph}{j}'] = 'int'
self.path_table = os.path.join(self.path_root, 'Data', 'tables')
# 테이블 불러오기
self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype)
self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype)
self.history = pd.read_csv(os.path.join(self.path_table, 'history.csv'), dtype=loading_dtype)
self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype)
self.matching = pd.read_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv'), dtype=loading_dtype)
self.movements = pd.read_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv'), dtype=loading_dtype)
self.match6 = pd.read_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv'), dtype=loading_dtype)
self.match6 = self.match6[['node_id', 'phase_no', 'ring_type', 'inc_edge', 'out_edge']].reset_index(drop=True)
# 교차로목록 정의
self.inter_nos = sorted(self.inter_info.inter_no.unique())
print("1-2. 테이블들이 로드되었습니다.")
# 1-3. 네트워크 무결성 검사
def check_networks(self):
# https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
if tools not in sys.path:
sys.path.append(tools)
else:
raise EnvironmentError("please declare environment variable 'SUMO_HOME'")
traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')])
nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light']
for node in nodes:
node_id = node.getID()
from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0])
from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id))
if from_xml != from_traci:
sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'}
self.issues.append(sub)
traci.close()
print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.")
# 1-4. 테이블 무결성 검사
def check_tables(self):
self.check_history()
# 교차로정보, 방위각정보, 신호계획에 대해서는 preprocess_daily.py에서
# 무결성검사를 완료했으므로 여기에서는 따로 검사하지 않음.
# self.check_moves() # 이동류번호에 대한 무결성검사 필요하나 아직 작성하지 않음. (24. 2. 5 화)
print("1-4. 테이블들의 무결성 검사를 완료했습니다.")
# 1-4-1. 신호이력(history) 검사
def check_history(self):
# 1-4-1-1. inter_no 검사
# self.history.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드
missing_inter_nos = set(self.history.inter_no) - set(self.inter_nos)
if missing_inter_nos:
msg = f"1-4-1-1. history의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}"
self.issues.append(msg)
# 1-4-1-2. 종료유닉스 검사
# self.history.loc[0, 'end_unix'] = 38.0 # 에러 발생을 위한 코드
self.min_unix, self.max_unix = int(datetime(2020, 1, 1).timestamp()), int(datetime(2038, 1, 1).timestamp())
for _, row in self.history.iterrows():
unixbool = self.min_unix <= row['end_unix'] <= self.max_unix
if not unixbool:
msg = f"1-4-1-2. 적정 범위를 벗어난 유닉스시각(end_unix)이 존재합니다 : inter_no : {row['inter_no']}"
self.issues.append(msg)
# 1-4-1-3. 현시시간 검사
# self.history.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드
durations = self.history[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]]
valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1)
invalid_inter_nos = sorted(self.history[~ valid_indices].inter_no.unique())
if invalid_inter_nos:
msg = f"1-4-1-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}"
# 1-5. 보조 딕셔너리, 데이터프레임, 리스트 등 만들기
def prepare_auxiliaries(self):
# inter2node : a dictionary that maps inter_no to the node_id
inter_node_p = self.inter_node[self.inter_node.inter_type=='parent']
self.inter2node = dict(zip(inter_node_p['inter_no'], inter_node_p['node_id']))
self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no']))
# hours : 정각에 해당하는 시각들 목록
self.hours = np.array(range(self.midnight - 7200, self.next_day + 1, 3600))
# split, isplit : A,B 분리 혹은 통합시 사용될 수 있는 딕셔너리
self.splits = {} # splits maps (inter_no, start_hour, start_minute) to split
for i, row in self.plan.iterrows():
inter_no = row.inter_no
start_hour = row.start_hour
start_minute = row.start_minute
cycle = row.cycle
cums_A = row[[f'dura_A{j}' for j in range(1,9)]].cumsum()
cums_B = row[[f'dura_B{j}' for j in range(1,9)]].cumsum()
self.splits[(inter_no, start_hour, start_minute)] = {} # split maps (phas_A, phas_B) to k
k = 0
for t in range(cycle):
new_phas_A = len(cums_A[cums_A < t]) + 1
new_phas_B = len(cums_B[cums_B < t]) + 1
if k == 0 or ((new_phas_A, new_phas_B) != (phas_A, phas_B)):
k += 1
phas_A = new_phas_A
phas_B = new_phas_B
self.splits[(inter_no, start_hour, start_minute)][(phas_A, phas_B)] = k
self.isplits = {} # the inverse of splits
for i in self.splits:
self.isplits[i] = {self.splits[i][k]:k for k in self.splits[i]} # isplit maps k to (phas_A, phas_B)
# timetable : 교차로별 프로그램 시작시각
self.timetable = self.plan[['start_hour', 'start_minute']].drop_duplicates()
self.timetable['start_seconds'] = self.midnight + self.timetable['start_hour'] * 3600 + self.timetable['start_minute'] * 60
# A dictionary that maps parent_id to a list of child_ids
self.pa2ch = {'i0':['u00'], 'i1':[], 'i2':['u20'], 'i3':['c30', 'u30', 'u31', 'u32'], 'i6':['u60'], 'i7':[], 'i8':[], 'i9':[]}
self.node_ids = sorted(self.inter_node.node_id.unique())
self.parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique())
self.nodes = [self.net.getNode(node_id) for node_id in self.node_ids]
# node2num_cycles : A dictionary that maps a node_id to the number of cycles
with open(os.path.join(self.path_root, 'Intermediates', 'node2num_cycles.json'), 'r') as file:
# json.load() 함수를 사용해 파일 내용을 Python 딕셔너리로 불러옵니다.
self.node2num_cycles = json.load(file)
# 2. 신호이력 전처리
def process_history(self):
print("2. 신호이력 테이블을 변환합니다.")
self.make_rhistory()
self.make_rhists()
self.make_hrhists()
# 2-1. rhistory
def make_rhistory(self):
# 1. 조회시점의 유닉스 타임 이전의 신호이력 수집
self.rhistory = self.history.copy() # recent history
self.rhistory = self.rhistory[(self.rhistory.end_unix <= self.present_time) & (self.rhistory.end_unix > self.present_time - 9000)] # 두 시간 반 전부터 현재까지의 신호이력을 가져옴. 9000 = 3600 * 2.5
# rhistory에 모든 교차로번호가 존재하지 않으면 해당 교차로번호에 대한 신호이력을 추가함 (at 최근 프로그램 시작시각)
whole_inter_nos = sorted(self.history.inter_no.unique())
recent_inter_nos = sorted(self.rhistory.inter_no.unique())
if not whole_inter_nos==recent_inter_nos:
for inter_no in set(whole_inter_nos) - set(recent_inter_nos):
program_start, prow = self.load_prow(inter_no, self.present_time - 9000)
cycle = prow.cycle.iloc[0]
row1 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()
row2 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()
# prow에서 필요한 부분을 rhistory에 추가
row1['end_unix'] = program_start
row2['end_unix'] = program_start + cycle
self.rhistory = pd.concat([self.rhistory, row1, row2]).reset_index(drop=True)
# present_time + adder 의 시각에 한 주기의 신호 추가
for inter_no in set(whole_inter_nos):
program_start, prow = self.load_prow(inter_no, self.present_time)
cycle = prow.cycle.iloc[0]
row3 = prow.drop(['start_hour', 'start_minute'], axis=1).copy()
# prow에서 필요한 부분을 rhistory에 추가
row3['end_unix'] = self.present_time + self.adder
self.rhistory = pd.concat([self.rhistory, row3]).reset_index(drop=True)
# 2. 시작 유닉스 타임컬럼 생성 후 종류 유닉스 타임에서 현시별 현시기간 컬럼의 합을 뺀 값으로 입력
# - 현시시간의 합을 뺀 시간의 +- 10초 이내에 이전 주기정보가 존재하면 그 유닉스 시간을 시작 유닉스시간 값으로 하고, 존재하지 않으면 현시시간의 합을 뺀 유닉스 시간을 시작 유닉스 시간으로 지정
for i, row in self.rhistory.iterrows():
inter_no = row.inter_no
end_unix = row.end_unix
elapsed_time = row[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]].sum() // 2 # 현시시간 합
# 이전 유닉스 존재하지 않음 : 현시시간 합의 차
start_unix = end_unix - elapsed_time
pre_rows = self.history[:i] # previous rows
if inter_no in pre_rows.inter_no.unique(): # 이전 유닉스 존재
pre_unix = pre_rows[pre_rows.inter_no == inter_no]['end_unix'].iloc[-1] # previous unix time
# 이전 유닉스 존재, abs < 10 : 이전 유닉스
if abs(pre_unix - start_unix) < 10:
start_unix = pre_unix
# 이전 유닉스 존재, abs >=10 : 현시시간 합의 차
else:
pass
self.rhistory.loc[i, 'start_unix'] = start_unix
self.rhistory[self.rhistory.isna()] = 0
self.rhistory['start_unix'] = self.rhistory['start_unix'].astype(int)
self.rhistory = self.rhistory[['inter_no', 'start_unix'] + [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)] + ['cycle']]
def load_prow(self, inter_no, time):
'''
load planned row
'''
# 프로그램 시작시각
program_starts = np.array(self.timetable.start_seconds)
idx = (program_starts <= time).sum() - 1
program_start = program_starts[idx]
# 최근 프로그램 시작시각에 대한 신호계획
start_hour = self.timetable.iloc[idx].start_hour
start_minute = self.timetable.iloc[idx].start_minute
prow = self.plan[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row
return program_start, prow
# 2-2. rhists
def make_rhists(self):
self.rhists = []
for inter_no in sorted(self.rhistory.inter_no.unique()):
self.rhist = self.rhistory.copy()[self.rhistory.inter_no==inter_no]
self.rhist = self.rhist.drop_duplicates(subset=['start_unix']).reset_index(drop=True)
# D_n 및 S_n 값 정의
self.rhist['D_n'] = 0 # D_n : 시간차이
self.rhist['S_n'] = 0 # S_n : 현시시간합
for n in range(len(self.rhist)):
curr_unix = self.rhist.iloc[n].start_unix # current start_unix
self.rhist.loc[n, ['D_n', 'S_n']] = self.calculate_DS(self.rhist, curr_unix)
# 이전시각, 현재시각
prev_unix = self.rhist.loc[0, 'start_unix'] # previous start_unix
curr_unix = self.rhist.loc[1, 'start_unix'] # current start_unix
# rhist의 마지막 행에 도달할 때까지 반복
while True:
n = self.rhist[self.rhist.start_unix==curr_unix].index[0]
cycle = self.rhist.loc[n, 'cycle']
D_n = self.rhist.loc[n, 'D_n']
S_n = self.rhist.loc[n, 'S_n']
# 참값인 경우
if (abs(D_n - S_n) <= 5):
pass
# 참값이 아닌 경우
else:
# 2-1-1. 결측치 처리 : 인접한 두 start_unix의 차이가 계획된 주기의 두 배보다 크면 결측이 일어났다고 판단, 신호계획의 현시시간으로 "대체"
if curr_unix - prev_unix >= 2 * cycle:
# prev_unix를 계획된 주기만큼 늘려가면서 한 행씩 채워나간다.
# (curr_unix와의 차이가 계획된 주기보다 작거나 같아질 때까지)
while curr_unix - prev_unix > cycle:
prev_unix += cycle
# 신호 계획(prow) 불러오기
start_seconds = np.array(self.timetable.start_seconds)
idx = (start_seconds <= prev_unix).sum() - 1
start_hour = self.timetable.iloc[idx].start_hour
start_minute = self.timetable.iloc[idx].start_minute
prow = self.plan.copy()[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row
# prow에서 필요한 부분을 rhist에 추가
prow['start_unix'] = prev_unix
prow = prow.drop(['start_hour', 'start_minute', 'offset'], axis=1)
cycle = prow.iloc[0].cycle
self.rhist = pd.concat([self.rhist, prow])
self.rhist = self.rhist.sort_values(by='start_unix').reset_index(drop=True)
n += 1
# 2-1-2. 이상치 처리 : 비율에 따라 해당 행을 "삭제"(R_n <= 0.5) 또는 "조정"(R_n > 0.5)한다
R_n = (curr_unix - prev_unix) / cycle # R_n : 비율
# R_n이 0.5보다 작거나 같으면 해당 행을 삭제
if R_n <= 0.5:
self.rhist = self.rhist.drop(index=n).reset_index(drop=True)
if n >= self.rhist.index[-1]:
break
# 행삭제에 따른 curr_unix, R_n 재정의
curr_unix = self.rhist.loc[n, 'start_unix']
R_n = (curr_unix - prev_unix) / cycle # R_n : 비율
# R_n이 0.5보다 크면 해당 행 조정 (비율을 유지한 채로 현시시간 대체)
if R_n > 0.5:
# 신호 계획(prow) 불러오기
start_seconds = np.array(self.timetable.start_seconds)
idx = (start_seconds <= curr_unix).sum() - 1
start_hour = self.timetable.iloc[idx].start_hour
start_minute = self.timetable.iloc[idx].start_minute
prow = self.plan[(self.plan.inter_no==inter_no) & (self.plan.start_hour==start_hour) & (self.plan.start_minute==start_minute)] # planned row
# 조정된 현시시간 (prow에 R_n을 곱하고 정수로 바꿈)
adjusted_dur = prow.copy()[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] * R_n
int_parts = adjusted_dur.iloc[0].apply(lambda x: int(x))
frac_parts = adjusted_dur.iloc[0] - int_parts
difference = round(adjusted_dur.iloc[0].sum()) - int_parts.sum()
for _ in range(difference): # 소수 부분이 가장 큰 상위 'difference'개의 값에 대해 올림 처리
max_frac_index = frac_parts.idxmax()
int_parts[max_frac_index] += 1
frac_parts[max_frac_index] = 0 # 이미 처리된 항목은 0으로 설정
# rhist에 조정된 현시시간을 반영
self.rhist.loc[n, [f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]] = int_parts.values
self.rhist.loc[n, 'cycle'] = int_parts.sum().sum() // 2
if n >= self.rhist.index[-1]:
break
prev_unix = curr_unix
curr_unix = self.rhist.loc[n+1, 'start_unix']
# 생략해도 무방할 코드
self.rhist = self.rhist.reset_index(drop=True)
self.rhist = self.rhist.sort_values(by=['start_unix'])
# D_n 및 S_n 값 재정의
for n in range(len(self.rhist)):
curr_unix = self.rhist.iloc[n].start_unix # current start_unix
self.rhist.loc[n, ['D_n', 'S_n']] = self.calculate_DS(self.rhist, curr_unix)
self.rhists.append(self.rhist)
self.rhists = pd.concat(self.rhists).sort_values(by=['start_unix','inter_no'])
self.rhists = self.rhists[self.rhists.start_unix >= self.present_time - 3600]
self.rhists = self.rhists.drop(columns=['D_n', 'S_n'])
def calculate_DS(self, rhist, curr_unix):
program_starts = np.array(self.timetable.start_seconds)
idx = (program_starts <= self.present_time).sum() - 1
program_start = program_starts[idx]
if list(self.hours[self.hours <= curr_unix]):
ghour_lt_curr_unix = self.hours[self.hours <= curr_unix].max() # the greatest hour less than or equal to curr_unix
else:
ghour_lt_curr_unix = program_start
start_unixes = rhist.start_unix.unique()
start_unixes_lt_ghour = np.sort(start_unixes[start_unixes < ghour_lt_curr_unix]) # start unixes less than ghour_lt_curr_unix
# 기준유닉스(base_unix) : curr_unix보다 작은 hour 중에서 가장 큰 값으로부터 다섯 번째로 작은 start_unix
if len(start_unixes_lt_ghour) > 5:
base_unix = start_unixes_lt_ghour[-5]
# start_unixes_lt_ghour의 길이가 5 미만일 경우에는 맨 앞 start_unix로 base_unix를 지정
else:
base_unix = rhist.start_unix.min()
D_n = curr_unix - base_unix
S_n_durs = rhist[(rhist.start_unix > base_unix) & (rhist.start_unix <= curr_unix)] \
[[f'dura_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]
S_n = S_n_durs.values.sum() // 2
return D_n, S_n
# 2-2. hrhists
def make_hrhists(self):
# 계층화된 형태로 변환
self.hrhists = [] # hierarchied recent history
for i, row in self.rhists.iterrows():
inter_no = row.inter_no
start_unix = row.start_unix
ind = (self.timetable['start_seconds'] <= row.start_unix).sum() - 1
start_hour = self.timetable.iloc[ind].start_hour
start_minute = self.timetable.iloc[ind].start_minute
self.isplit = self.isplits[(inter_no, start_hour, start_minute)]
phas_As = [self.isplit[j][0] for j in self.isplit.keys()]
phas_Bs = [self.isplit[j][1] for j in self.isplit.keys()]
durs_A = row[[f'dura_A{j}' for j in range(1,9)]]
durs_B = row[[f'dura_B{j}' for j in range(1,9)]]
durations = []
for j in range(1, len(self.isplit)+1):
ja = self.isplit[j][0]
jb = self.isplit[j][1]
if ja == jb:
durations.append(min(durs_A[ja-1], durs_B[jb-1]))
else:
durations.append(abs(durs_A[ja-1] - durs_B[ja-1]))
new_rows = pd.DataFrame({'inter_no':[inter_no] * len(durations), 'start_unix':[start_unix] * len(durations),
'phas_A':phas_As, 'phas_B':phas_Bs, 'duration':durations})
self.hrhists.append(new_rows)
self.hrhists = pd.concat(self.hrhists)
self.hrhists = self.hrhists.sort_values(by = ['start_unix', 'inter_no', 'phas_A', 'phas_B']).reset_index(drop=True)
# 3. 이동류정보 전처리
def process_movement(self):
print("3. 이동류정보 테이블을 변환합니다.")
self.make_movement()
self.update_movement()
# 3-1. movement
def make_movement(self):
# # - 아래 절차를 5초마다 반복
# for fsec in range(self.midnight, self.present_time + 1, 5): # fsec : unix time by Five SECond
# # 1. 상태 테이블 조회해서 전체 데이터중 필요데이터(교차로번호, A링 현시번호, A링 이동류번호, B링 현시번호, B링 이동류번호)만 수집 : A
# # move = time2move[fsec]
# move = pd.read_csv(f'../Data/tables/move/move_{fsec}.csv', index_col=0)
# # 2. 이력 테이블 조회해서 교차로별로 유닉스시간 최대인 데이터(교차로변호, 종료유닉스타임)만 수집 : B
# recent_histories = [group.iloc[-1:] for _, group in self.history[self.history['end_unix'] < fsec].groupby('inter_no')] # 교차로별로 유닉스시간이 최대인 행들
# if not recent_histories:
# rhistory = pd.DataFrame({'inter_no':[], 'end_unix':[]}) # recent history
# else:
# rhistory = pd.concat(recent_histories)
# recent_unix = rhistory[['inter_no', 'end_unix']]
# # 3. 상태 테이블 조회정보(A)와 이력 테이블 조회정보(B) 조인(키값 : 교차로번호) : C
# move = pd.merge(move, recent_unix, how='left', on='inter_no')
# move['end_unix'] = move['end_unix'].fillna(0).astype(int)
# move = move.drop_duplicates()
# # 4. C데이터 프레임에 신규 컬럼(시작 유닉스타임) 생성 후 종료유닉스 타임 값 입력, 종료 유닉스 타임 컬럼 제거
# move = move.rename(columns = {'end_unix':'start_unix'})
# # 5. 이동류 이력정보 READ
# # - CSV 파일로 서버에 저장된 이동류정보를 읽어옴(파일이 없는 경우에는 데이터가 없는 프레임 D 생성)
# try:
# if isinstance(self.movement, pd.DataFrame): # movement가 존재할 경우 그걸 그대로 씀.
# pass
# else:
# self.movement = pd.DataFrame()
# except NameError: # movement가 존재하지 않는 경우 생성
# self.movement = pd.DataFrame()
# # 6. 이동류 이력정보 데이터테이블(D)에 C데이터 add
# self.movement = pd.concat([self.movement, move])
# # 7. D데이터 프레임에서 중복데이터 제거(교차로번호, 시작 유닉스타임, A링 현시번호, B링 현시번호 같은 행은 제거)
# self.movement = self.movement.drop_duplicates(['inter_no','phas_A','phas_B','start_unix'])
# # 8. D데이터 보관 시간 기준시간을 시작 유닉스 타임의 최대값 - 3600을 값으로 산출하고, 보관 시간 기준시간보다 작은 시작 유닉스 타임을 가진 행은 모두 제거(1시간 데이터만 보관)
# self.movement = self.movement[self.movement.start_unix > fsec - 3600]
# self.movement = self.movement.sort_values(by=['start_unix','inter_no','phas_A','phas_B']).reset_index(drop=True)
self.movement = pd.read_csv(os.path.join(self.path_root, 'Intermediates', 'movement', f'movement_{self.present_time}.csv'), index_col=0)
# 3-2. movement_updated
def update_movement(self):
# 중복을 제거하고 (inter_no, start_unix) 쌍을 만듭니다.
hrhists_inter_unix = set(self.hrhists[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None))
movement_inter_unix = set(self.movement[['inter_no', 'start_unix']].drop_duplicates().itertuples(index=False, name=None))
# hrhists에는 있지만 movement에는 없는 (inter_no, start_unix) 쌍을 찾습니다.
missing_in_movement = hrhists_inter_unix - movement_inter_unix
# 새로운 행들을 생성합니다.
new_rows = []
if missing_in_movement:
for inter_no, start_unix in missing_in_movement:
# movements에서 해당 inter_no의 데이터를 찾습니다.
new_row = self.movements[self.movements['inter_no'] == inter_no].copy()
# start_unix 값을 설정합니다.
new_row['start_unix'] = start_unix
new_rows.append(new_row)
# 새로운 데이터프레임을 생성하고 기존 movement 데이터프레임과 합칩니다.
new_movement = pd.concat(new_rows, ignore_index=True)
self.movement_updated = pd.concat([self.movement, new_movement], ignore_index=True)
else:
self.movement_updated = self.movement
# 4. 통합테이블 생성
def make_histids(self):
print("4. 통합 테이블을 생성합니다.")
self.merge_dfs()
self.attach_children()
# 4-1. histid
def merge_dfs(self):
# movements and durations
movedur = pd.merge(self.hrhists, self.movement_updated, how='inner', on=['inter_no', 'start_unix', 'phas_A', 'phas_B'])
movedur = movedur.sort_values(by=['start_unix', 'inter_no', 'phas_A','phas_B'])
movedur = movedur[['inter_no', 'start_unix', 'phas_A', 'phas_B', 'move_A', 'move_B', 'duration']]
# 이동류 매칭 테이블에서 진입id, 진출id를 가져와서 붙임.
for i, row in movedur.iterrows():
inter_no = row.inter_no
start_unix = row.start_unix
# incoming and outgoing edges A
move_A = row.move_A
if move_A in [17, 18]:
inc_edge_A = np.nan
outhedge_A = np.nan
else:
match_A = self.matching[(self.matching.inter_no == inter_no) & (self.matching.move_no == move_A)].iloc[0]
inc_edge_A = match_A.inc_edge
out_edge_A = match_A.out_edge
movedur.loc[i, ['inc_edge_A', 'out_edge_A']] = [inc_edge_A, out_edge_A]
# incoming and outgoing edges B
move_B = row.move_B
if move_B in [17, 18]:
inc_edge_B = np.nan
out_edge_B = np.nan
else:
match_B = self.matching[(self.matching.inter_no == inter_no) & (self.matching.move_no == move_B)].iloc[0]
inc_edge_B = match_B.inc_edge
out_edge_B = match_B.out_edge
movedur.loc[i, ['inc_edge_B', 'out_edge_B']] = [inc_edge_B, out_edge_B]
# 이동류 컬럼 제거
movedur = movedur.drop(['move_A', 'move_B'], axis=1)
self.histid = movedur.copy() # history with edge ids (incoming and outgoing edge ids)
self.histid['node_id'] = self.histid['inter_no'].map(self.inter2node)
self.histid = self.histid[['inter_no', 'node_id', 'start_unix', 'phas_A', 'phas_B', 'duration', 'inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']]
histid_start = self.present_time - 600
self.histid = self.histid[self.histid.start_unix > histid_start]
# 4-2. histids
def attach_children(self):
'''
자식교차로에 대한 진입·진출 엣지 정보를 붙여주는 함수
input :
(1) histid
- 각 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지
- 부모교차로(주교차로)에 대해서만 값이 지정되어 있음
(2) match6
- (현시, 링)별 진입·진출엣지
- 자식교차로(유턴 및 연동교차로)에 대해서도 값이 지정되어 있음
(3) parent_ids : 부모교차로 목록
(4) pa2ch : 각 부모교차로id를 부모교차로가 포함하고 있는 자식교차로들의 id들의 리스트로 대응시키는 딕셔너리
output : histids
- 모든(부모 및 자식) 교차로에 대한 시작유닉스 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지
'''
new_histids = []
for parent_id in self.parent_ids:
for child_id in self.pa2ch[parent_id]:
new_histid = self.histid.copy()[self.histid.node_id==parent_id]
new_histid[['inc_edge_A', 'out_edge_A', 'inc_edge_B', 'out_edge_B']] = np.nan
for i, row in new_histid.iterrows():
phas_A = row.phas_A
phas_B = row.phas_B
new_match = self.match6[self.match6.node_id==child_id]
Arow = new_match[(new_match.phase_no==phas_A) & (new_match.ring_type=='A')]
if ~ Arow[['inc_edge', 'out_edge']].isna().all().all():
inc_edge = Arow.iloc[0].inc_edge
out_edge = Arow.iloc[0].out_edge
new_histid.loc[i, ['inc_edge_A', 'out_edge_A']] = [inc_edge, out_edge]
Brow = new_match[(new_match.phase_no==phas_B) & (new_match.ring_type=='B')]
if ~ Brow[['inc_edge', 'out_edge']].isna().all().all():
inc_edge = Brow.iloc[0].inc_edge
out_edge = Brow.iloc[0].out_edge
new_histid.loc[i, ['inc_edge_B', 'out_edge_B']] = [inc_edge, out_edge]
new_histid.loc[i, 'node_id'] = child_id
new_histids.append(new_histid)
new_histids = pd.concat(new_histids)
self.histids = pd.concat([self.histid.copy(), new_histids])
self.histids = self.histids.sort_values(by=['start_unix', 'node_id', 'phas_A', 'phas_B']).reset_index(drop=True)
# 5. 신호 생성
print("5. 신호를 생성합니다.")
def get_signals(self):
self.initialize_states()
self.assign_signals()
self.set_timepoints()
self.assign_red_yellow()
self.make_tl_file()
# 5-1. 신호초기화
def initialize_states(self):
'''
신호 초기화
input :
(1) net : 네트워크
(2) nodes : 노드 목록
(3) histids : 모든 교차로에 대한 시작유닉스 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지
output : node2init
- 각 노드를 초기화된 신호로 맵핑하는 딕셔너리
- 초기화된 신호란, 우회전을 g로 나머지는 r로 지정한 신호를 말함.
'''
self.node2init = {}
for node in self.nodes:
node_id = node.getID()
conns = [(c.getJunctionIndex(), c) for c in node.getConnections()]
conns = [c for c in conns if c[0] >= 0]
conns = sorted(conns, key=lambda x: x[0])
state = []
for i, ci in conns:
if ci.getTLLinkIndex() < 0:
continue
are_foes = False
for j, cj in conns:
if ci.getTo() == cj.getTo():
continue
if node.areFoes(i, j):
are_foes = True
break
state.append('r' if are_foes else 'g')
self.node2init[node_id] = state
# 어떤 연결과도 상충이 일어나지는 않지만, 신호가 부여되어 있는 경우에는 r을 부여
for _, row in self.histids.iterrows():
node_id = row['node_id']
inc_edge_A = row.inc_edge_A
inc_edge_B = row.inc_edge_B
out_edge_A = row.out_edge_A
out_edge_B = row.out_edge_B
if pd.isna(inc_edge_A) or pd.isna(out_edge_A):
pass
else:
inc_edge_A = self.net.getEdge(inc_edge_A)
out_edge_A = self.net.getEdge(out_edge_A)
for conn in inc_edge_A.getConnections(out_edge_A):
index = conn.getTLLinkIndex()
if index >= 0:
self.node2init[node_id][index] = 'r'
if pd.isna(inc_edge_B) or pd.isna(out_edge_B):
pass
else:
inc_edge_B = self.net.getEdge(inc_edge_B)
out_edge_B = self.net.getEdge(out_edge_B)
for conn in inc_edge_B.getConnections(out_edge_B):
index = conn.getTLLinkIndex()
if index >= 0:
self.node2init[node_id][index] = 'r'
# 5-2. 녹색신호 부여
def assign_signals(self):
'''
진입·진출엣지를 신호문자열로 배정
input :
(1) histids : 모든 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 진입·진출엣지
(2) node2init : 각 노드를 초기화된 신호로 맵핑하는 딕셔너리
(3) net : 네트워크
output : sigtable
- 모든 교차로에 대한 (시작유닉스, A현시, B현시)별 현시시간, 신호문자열
- 황색 및 적색신호는 아직 반영되지 않았음.
'''
self.sigtable = self.histids.copy()
self.sigtable['init_state'] = self.sigtable['node_id'].map(self.node2init)
self.sigtable['state'] = self.sigtable['init_state'].map(lambda x:''.join(x))
for i, row in self.sigtable.iterrows():
node_id = row.node_id
inc_edge_A = row.inc_edge_A
inc_edge_B = row.inc_edge_B
out_edge_A = row.out_edge_A
out_edge_B = row.out_edge_B
state = copy.deepcopy(self.node2init)[node_id]
if pd.isna(inc_edge_A) or pd.isna(out_edge_A):
pass
else:
inc_edge_A = self.net.getEdge(inc_edge_A)
out_edge_A = self.net.getEdge(out_edge_A)
for conn in inc_edge_A.getConnections(out_edge_A):
index = conn.getTLLinkIndex()
if index >= 0:
state[index] = 'G'
self.sigtable.at[i, 'state'] = ''.join(state)
if pd.isna(inc_edge_B) or pd.isna(out_edge_B):
pass
else:
inc_edge_B = self.net.getEdge(inc_edge_B)
out_edge_B = self.net.getEdge(out_edge_B)
for conn in inc_edge_B.getConnections(out_edge_B):
index = conn.getTLLinkIndex()
if index >= 0:
state[index] = 'G'
self.sigtable.at[i, 'state'] = ''.join(state)
self.sigtable = self.sigtable.dropna(subset='state')
self.sigtable = self.sigtable.reset_index(drop=True)
self.sigtable['phase_sumo'] = self.sigtable.groupby(['node_id', 'start_unix']).cumcount()
self.sigtable = self.sigtable[['node_id', 'start_unix', 'phase_sumo', 'duration', 'state']]
self.sigtable = self.sigtable.sort_values(by=['start_unix', 'node_id'])
self.sigtable['start_dt'] = self.sigtable['start_unix'].apply(lambda x:datetime.fromtimestamp(x))
# 5-3. 신호 파일의 시작 및 종료시각 설정
def set_timepoints(self):
self.offsets = {}
self.Sigtable = []
sim_start = self.present_time - 300
for node_id, group in self.sigtable.groupby('node_id'):
lsbs = group[group['start_unix'] < sim_start]['start_unix'].max() # the last start_unix before sim_start
self.offsets[node_id] = lsbs - sim_start
group = group[group.start_unix >= lsbs]
start_unixes = np.array(group.start_unix)
start_unixes = np.sort(np.unique(start_unixes))[:self.node2num_cycles[node_id]]
group = group[group.start_unix.isin(start_unixes)]
self.Sigtable.append(group)
self.Sigtable = pd.concat(self.Sigtable)
# 5-4. 적색 및 황색신호 부여
def assign_red_yellow(self):
'''
적색, 황색신호를 반영한 신호문자열 배정
input : Sigtable
- 모든 교차로에 대한 (시작유닉스, 세부현시번호)별 현시시간, 신호문자열, 진입·진출엣지
* 세부현시란 오버랩을 반영한 현시번호를 뜻함.
output : SIGTABLE
- 모든 교차로에 대한 (시작유닉스, 녹황적세부현시번호)별 현시시간, (황·적색신호가 포함된) 신호문자열
* 녹황적세부현시번호란 세부현시번호에 r, g, y 옵션까지 포함된 현시번호를 뜻함.
'''
self.SIGTABLE = []
for node_id, group in self.Sigtable.groupby('node_id'):
new_rows_list = []
for i in range(1, len(group)):
prev_row = group.iloc[i-1:i].copy()
next_row = group.iloc[i:i+1].copy()
new_rows = pd.concat([prev_row, prev_row, next_row]).reset_index(drop=True)
new_rows.loc[0, 'phase_sumo'] = str(prev_row.phase_sumo.iloc[0]) + '_g'
new_rows.loc[0, 'duration'] = new_rows.loc[0, 'duration'] - 5
new_rows.loc[1, 'phase_sumo'] = str(prev_row.phase_sumo.iloc[0]) + '_y'
new_rows.loc[1, 'duration'] = 4
yellow_state = ''
red_state = ''
for a, b in zip(prev_row.state.iloc[0], next_row.state.iloc[0]):
if a == 'G' and b == 'r':
yellow_state += 'y'
red_state += 'r'
else:
yellow_state += a
red_state += a
new_rows.loc[2, 'phase_sumo'] = str(next_row.phase_sumo.iloc[0]) + '__r'
new_rows.loc[2, 'duration'] = 1
new_rows.loc[1, 'state'] = yellow_state
new_rows.loc[2, 'state'] = red_state
new_rows_list.append(new_rows)
next_row['phase_sumo'] = str(next_row.phase_sumo.iloc[0]) + '_g'
next_row['duration'] -= 5
# next_row.loc['duration'] -= 5
new_rows_list.append(next_row)
new_rows = pd.concat(new_rows_list)
self.SIGTABLE.append(new_rows)
self.SIGTABLE = pd.concat(self.SIGTABLE).sort_values(by=['node_id', 'start_unix', 'phase_sumo']).reset_index(drop=True)
# 5-5. 신호파일 생성
def make_tl_file(self):
strings = ['<additional>\n']
for node_id, group in self.SIGTABLE.groupby('node_id'):
strings.append(f' <tlLogic id="{node_id}" type="static" programID="{node_id}_prog" offset="{self.offsets[node_id]}">\n')
for i, row in group.iterrows():
duration = row.duration
state = row.state
strings.append(f' <phase duration="{duration}" state="{state}"/>\n')
strings.append(' </tlLogic>\n')
strings.append('</additional>')
strings = ''.join(strings)
# 저장
self.path_output = os.path.join(self.path_root, 'Results', f'sn_{self.present_time}.add.xml')
with open(self.path_output, 'w') as f:
f.write(strings)
# 6. 이슈사항 저장
def write_issues(self):
print('6. 이슈사항을 저장합니다.')
path_issues = os.path.join(self.path_root, "Results", "issues_generate_signals.txt")
with open(path_issues, "w", encoding="utf-8") as file:
for item in self.issues:
file.write(item + "\n")
if self.issues:
print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :")
for review in self.issues:
print(review)
def main(self):
# 1. 데이터 준비
self.prepare_data()
# 2. 신호이력 전처리
self.process_history()
# 3. 이동류정보 전처리
self.process_movement()
# 4. 통합테이블 생성
self.make_histids()
# 5. 신호 생성
self.get_signals()
# 6. 이슈사항 저장
self.write_issues()
if __name__ == '__main__':
self = SignalGenerator()
self.main()
# self.histid.to_csv(os.path.join('Intermediates', 'histid', f'histid_{self.present_time}_.csv'))

+ 0
- 587
Script/get_intermediates.py View File

@ -1,587 +0,0 @@
import pandas as pd
import numpy as np
import os, sys
import json
import sumolib, traci
from tqdm import tqdm
class DailyPreprocess():
def __init__(self):
self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.issues = []
# 1. 데이터 불러오기
def load_data(self):
self.load_networks()
self.load_tables()
self.check_networks()
self.check_tables()
print('1. 모든 데이터가 로드되었습니다.')
# 1-1. 네트워크 불러오기
def load_networks(self):
self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml'))
print("1-1. 네트워크가 로드되었습니다.")
# 1-2. 테이블 불러오기
def load_tables(self):
# 모든 컬럼에 대하여 데이터타입 지정
loading_dtype = {
'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int',
'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str',
'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str',
'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float',
'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str'
}
for alph in ['A', 'B']:
for j in range(1,9):
loading_dtype[f'angle_{alph}{j}'] = 'str'
loading_dtype[f'dura_{alph}{j}'] = 'int'
self.path_table = os.path.join(self.path_root, 'Data', 'tables')
self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype)
self.angle = pd.read_csv(os.path.join(self.path_table, 'angle.csv'), dtype=loading_dtype)
self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype)
self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype)
self.uturn = pd.read_csv(os.path.join(self.path_table, 'child_uturn.csv'), dtype=loading_dtype)
self.coord = pd.read_csv(os.path.join(self.path_table, 'child_coord.csv'), dtype=loading_dtype)
self.nema = pd.read_csv(os.path.join(self.path_table, 'nema.csv'), encoding='cp949', dtype=loading_dtype)
print("1-2. 테이블들이 로드되었습니다.")
# 1-3. 테이블 불러오기
def check_networks(self):
# https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
if tools not in sys.path:
sys.path.append(tools)
else:
raise EnvironmentError("please declare environment variable 'SUMO_HOME'")
traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')])
nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light']
for node in nodes:
node_id = node.getID()
from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0])
from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id))
if from_xml != from_traci:
sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'}
self.issues.append(sub)
traci.close()
print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.")
# 1-4. 테이블의 무결성 검사
def check_tables(self):
self.check_inter_info()
self.check_angle()
self.check_plan()
print("1-4. 모든 테이블들의 무결성을 검사했고 이상 없습니다.")
pass
# 1-4-1. 교차로정보(inter_info) 검사
def check_inter_info(self):
# 1-4-1-1. inter_lat, inter_lon 적절성 검사
# self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드
self.max_lon, self.min_lon = 127.207888, 127.012492
self.max_lat, self.min_lat = 37.480693, 37.337112
for _, row in self.inter_info.iterrows():
latbool = self.min_lat <= row['inter_lat'] <= self.max_lat
lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon
if not(latbool and lonbool):
msg = f"1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}"
self.issues.append(msg)
# 교차로목록 정의
self.inter_nos = sorted(self.inter_info.inter_no.unique())
# 1-4-2. 방위각정보(inter_info) 검사
def check_angle(self):
# 1-4-2-1. inter_no 검사
# self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드
missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos)
if missing_inter_nos:
msg = f"1-4-2-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}"
self.issues.append(msg)
# 1-4-3. 신호계획(plan) 검사
def check_plan(self):
# 1-4-3-1. inter_no 검사
# self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드
missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos)
if missing_inter_nos:
msg = f"1-4-3-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}"
self.issues.append(msg)
# 1-4-3-2. 시작시각 검사
# self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드
for _, row in self.plan.iterrows():
start_hour = row.start_hour
start_minute = row.start_minute
if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59):
msg = f"1-4-3-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}"
self.issues.append(msg)
# 1-4-3-3. 현시시간 검사
# self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드
durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]]
valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1)
invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique())
if invalid_inter_nos:
msg = f"1-4-3-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}"
# 1-4-3-4. 주기 일관성 검사
# self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드
inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1)
if inconsistent_cycle.any():
inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0]
msg = f"1-4-3-4. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다."
self.issues.append(msg)
# 1-4-3-5. 현시시간 / 주기 검사
# self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드
right_duration = True
for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']):
A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum()
B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum()
# A_sum = group[group['ring_type']=='A']['duration'].sum()
# B_sum = group[group['ring_type']=='B']['duration'].sum()
cycle = group['cycle'].unique()[0]
if not (A_sum == B_sum == cycle):
right_duration = False
inc_inter_no = inter_no
if not right_duration:
msg = f"1-4-4-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다."
self.issues.append(msg)
# 2. 중간산출물 만들기
def get_intermediates(self):
self.get_matches()
# self.get_movements()
self.get_node2num_cycles()
# 2-1 매칭테이블들 생성
def get_matches(self):
self.make_match1()
self.make_match2()
self.make_match3()
self.make_match4()
self.make_match5()
self.make_match6()
self.make_matching()
# 2-1-1
def make_match1(self):
'''
신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다.
'../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다.
return : 통합된 이동류정보
- 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보
match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다.
'''
# [이동류번호] 불러오기 (약 1분의 소요시간)
path_move = os.path.join(self.path_root, 'Data', 'tables', 'move')
csv_moves = os.listdir(path_move)
moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')]
self.match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True)
self.match1.to_csv(os.path.join(self.path_root, 'Intermediates', 'match1.csv'))
# 2-1-2
def make_match2(self):
'''
match1을 계층화함.
- match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
'''
# 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no')
matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy()
matchA.columns = ['inter_no', 'phase_no', 'move_no']
matchA['ring_type'] = 'A'
matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy()
matchB.columns = ['inter_no', 'phase_no', 'move_no']
matchB['ring_type'] = 'B'
self.match2 = pd.concat([matchA, matchB]).drop_duplicates()
self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']]
self.match2 = self.match2.sort_values(by=list(self.match2.columns))
# 2-1-3
def make_match3(self):
'''
각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함.
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
nema :
- 컬럼 : move_no, inc_dir, out_dir
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# nema 정보 불러오기 및 병합
self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates()
# 2-1-4
def make_match4(self):
'''
방위각 정보를 매칭시켜 추가함.
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
angle_original :
- 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8)
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# 계층화
angles = []
for i, row in self.angle.iterrows():
angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]
new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()})
angles.append(new)
angles = pd.concat(angles)
angles = angles.dropna().reset_index(drop=True)
# 병합
six_chars = angles.angle_code.apply(lambda x:len(x)==6)
angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3])
angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:])
angles = angles.drop('angle_code', axis=1)
self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'],
right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates()
# 2-1-5
def make_match5(self):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로).
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
- match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) net
- 성남시 정자동 부근의 샘플 네트워크
(2) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(3) inter_info
- 교차로 정보. 여기에서는 위도와 경도가 쓰임.
- 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no
진입엣지id, 진출엣지id를 얻는 과정 :
- match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함.
* 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음
- 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장.
* inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge
- inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄.
- 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄.
- 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함.
'''
# parent node만 가져옴.
inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1)
inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']]
inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'],
right_on=['inter_no']).drop_duplicates()
self.inter2node = dict(zip(inter['inter_no'], inter['node_id']))
self.match5 = self.match4.copy()
# 진입진출ID 매칭
for index, row in self.match5.iterrows():
node_id = self.inter2node[row.inter_no]
node = self.net.getNode(node_id)
# 교차로의 모든 (from / to) edges
inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges
out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges
# 교차로의 모든 (from / to) directions
inc_dirs = []
for inc_edge in inc_edges:
start = inc_edge.getShape()[-2]
end = inc_edge.getShape()[-1]
inc_dir = np.array(end) - np.array(start)
inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5
inc_dirs.append(inc_dir)
out_dirs = []
for out_edge in out_edges:
start = out_edge.getShape()[0]
end = out_edge.getShape()[1]
out_dir = np.array(end) - np.array(start)
out_dir = out_dir / (out_dir ** 2).sum() ** 0.5
out_dirs.append(out_dir)
# 진입각, 진출각 불러오기
if not pd.isna(row.inc_angle):
inc_angle = int(row.inc_angle)
out_angle = int(row.out_angle)
# 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환
inc_angle = (-90 - inc_angle) % 360
inc_angle = inc_angle * np.pi / 180.
inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)])
out_angle = (90 - out_angle) % 360
out_angle = out_angle * np.pi / 180.
out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)])
# 매칭 엣지 반환
inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()
out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax()
inc_edge_id = inc_edges[inc_index].getID()
out_edge_id = out_edges[out_index].getID()
self.match5.at[index, 'inc_edge'] = inc_edge_id
self.match5.at[index, 'out_edge'] = out_edge_id
self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node)
self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True)
# 2-1-6
def make_match6(self):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로).
- match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(2) uturn (유턴정보)
- 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 유턴교차로id
- direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향)
- condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나
- inc_edge, out_edge : 유턴에 대한 진입진출엣지
(3) coord (연동교차로정보)
- 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 연동교차로id
- 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지
설명 :
- match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음.
여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함.
유턴교차로 :
- directions를 정북기준 시계방향의 8방위로 정함.
- 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음.
- 예) 진입방향(direction)이 '북'일 때,
- 직진 : (북, 남)
* 남 : directions[(ind + 4) % len(directions)]
- 좌회전 : (북, 동)
* 동 : directions[(ind + 2) % len(directions)]
- 보행 : (서, 동)
* 서 : directions[(ind - 2) % len(directions)]
- uturn의 각 행을 순회하면서 아래 과정을 반복함
- match5에서 parent_id에 해당하는 행들을 가져옴(cmatch).
- condition 별로 진입방향, 진출방향A, 진출방향B 정함.
- 상술한 directions를 활용하여 정함.
- (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함.
- ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
- 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함.
연동교차로 :
- 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음.
- 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음.
- 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음.
match6 :
- 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄.
'''
self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no']))
child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()
ch2pa = {} # child to parent
for child_id in child_ids:
parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0]
sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no]
ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id
directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향
# 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여
cmatches = []
for _, row in self.uturn.iterrows():
child_id = row.child_id
parent_id = row.parent_id
direction = row.direction
condition = row.condition
inc_edge_id = row.inc_edge
out_edge_id = row.out_edge
# match5에서 parent_id에 해당하는 행들을 가져옴
cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node
cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True)
cmatch['node_id'] = child_id
cmatch[['inc_edge', 'out_edge']] = np.nan
# condition 별로 inc_dire, out_dire_A, out_dire_B를 정함
ind = directions.index(direction)
if condition == "좌회전시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)]
elif condition == "직진시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)]
elif condition == "보행신호시":
inc_dire = directions[(ind + 2) % len(directions)]
out_dire_A = directions[(ind - 2) % len(directions)]
out_dire_B = directions[(ind - 2) % len(directions)]
# (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함
if condition == '보행신호시':
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다.
cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
else: # '직진시', '좌회전시'
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 유턴신호의 이동류번호를 19로 부여한다.
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19
cmatches.append(cmatch)
# 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여
self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter)
self.coord = self.coord.rename(columns={'child_id':'node_id'})
self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan
self.coord['move_no'] = 20
self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']]
# display(coord)
cmatches = pd.concat(cmatches)
self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])
self.match6.to_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv'))
# 2-1-7
def make_matching(self):
'''
이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다.
모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다.
- matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id
설명 :
- 필요한 리스트, 딕셔너리 등을 정의
(1) 가능한 (진입방향, 진출방향) 목록 [리스트]
(2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리]
(3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리]
(4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리]
(5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리]
- matching은 빈 리스트로 지정.
- 모든 노드id에 대하여 다음 과정을 반복
- 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복
- (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄
- 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append
'''
self.match7 = self.match6.copy()
self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']]
parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique())
child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique())
# (1) 가능한 (진입방향, 진출방향) 목록
flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist()
# (2) 각 교차로별 방향 목록 : pdires (possible directions)
pdires = {}
for node_id in parent_ids:
dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten()
dires = {dire for dire in dires if type(dire)==str}
pdires[node_id] = dires
# (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id)
inc2id = {}
for node_id in parent_ids:
for inc_dir in pdires[node_id]:
df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)]
inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0]
# (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id)
out2id = {}
for node_id in parent_ids:
for out_dir in pdires[node_id]:
df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)]
out2id[(node_id, out_dir)] = df.out_edge.iloc[0]
# (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows)
pflow = {}
for node_id in parent_ids:
pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])]
# (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching
# node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no']))
dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow
self.matching = []
for node_id in parent_ids:
inter_no = self.node2inter[node_id]
# 좌회전과 직진(1 ~ 16)
for (inc_dir, out_dir) in pflow[node_id]:
move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0]
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
self.matching.append(new_row)
# 보행신호(17), 전적색(18)
new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18],
'inc_dir':[None]*2, 'out_dir':[None]*2,
'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2})
self.matching.append(new_row)
# 신호우회전(21)
for d in range(len(dires_right)-1):
inc_dir = dires_right[d]
out_dir = dires_right[d+1]
if {inc_dir, out_dir}.issubset(pdires[node_id]):
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
self.matching.append(new_row)
self.matching.append(self.match7[self.match7.node_id.isin(child_ids)])
self.matching = pd.concat(self.matching)
self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)
self.matching['move_no'] = self.matching['move_no'].astype(int)
self.matching.to_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv'))
# 2-2
def get_movements(self):
movements_path = os.path.join(self.path_root, 'Intermediates', 'movement')
movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')]
movements = pd.concat(movements_list)
movements = movements.drop(columns=['start_unix'])
movements = movements.drop_duplicates()
movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])
movements = movements.reset_index(drop=True)
movements.to_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv'))
return movements
# 2-3 node2num_cycles : A dictionary that maps a node_id to the number of cycles
def get_node2num_cycles(self):
# node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))
self.node_ids = sorted(self.inter_node.node_id.unique())
Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']]
grouped = Aplan.groupby('inter_no')
df = grouped.agg({'cycle': 'min'}).reset_index()
df = df.rename(columns={'cycle': 'min_cycle'})
df['num_cycle'] = 300 // df['min_cycle'] + 2
inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle']))
node2numcycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids}
with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file:
json.dump(node2numcycles, file, indent=4)
return node2numcycles
# 3. 이슈사항 저장
def write_issues(self):
path_issues = os.path.join(self.path_root, "Results", "issues_intermediates.txt")
with open(path_issues, "w", encoding="utf-8") as file:
for item in self.issues:
file.write(item + "\n")
if self.issues:
print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :")
for review in self.issues:
print(review)
def main(self):
# 1. 데이터 불러오기
self.load_data()
# 2. 중간산출물 만들기
self.get_intermediates()
# 3. 이슈사항 저장
self.write_issues()
if __name__ == '__main__':
self = DailyPreprocess()
self.main()

+ 0
- 587
Script/get_signals.py View File

@ -1,587 +0,0 @@
import pandas as pd
import numpy as np
import os, sys
import json
from tqdm import tqdm
from datetime import datetime
class SignalGenerator():
def __init__(self):
self.path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
self.issues = []
# 1. 데이터 불러오기
def load_data(self):
self.load_networks()
self.load_tables()
self.check_networks()
self.check_tables()
print('1. 모든 데이터가 로드되었습니다.')
# 1-1. 네트워크 불러오기
def load_networks(self):
self.net = sumolib.net.readNet(os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml'))
print("1-1. 네트워크가 로드되었습니다.")
# 1-2. 테이블 불러오기
def load_tables(self):
# 모든 컬럼에 대하여 데이터타입 지정
loading_dtype = {
'inter_no':'int', 'start_hour':'int', 'start_minute':'int', 'cycle':'int','offset':'int',
'node_id':'str', 'inter_type':'str', 'parent_id':'str','child_id':'str',
'direction':'str', 'condition':'str', 'inc_edge':'str', 'out_edge':'str',
'end_unix':'int', 'inter_name':'str', 'inter_lat':'float', 'inter_lon':'float',
'group_no':'int', 'main_phase_no':'int', 'phase_no':'int','ring_type':'str'
}
for alph in ['A', 'B']:
for j in range(1,9):
loading_dtype[f'angle_{alph}{j}'] = 'str'
loading_dtype[f'dura_{alph}{j}'] = 'int'
self.path_table = os.path.join(self.path_root, 'Data', 'tables')
self.inter_info = pd.read_csv(os.path.join(self.path_table, 'inter_info.csv'), dtype=loading_dtype)
self.angle = pd.read_csv(os.path.join(self.path_table, 'angle.csv'), dtype=loading_dtype)
self.plan = pd.read_csv(os.path.join(self.path_table, 'plan.csv'), dtype=loading_dtype)
self.inter_node = pd.read_csv(os.path.join(self.path_table, 'inter_node.csv'), dtype=loading_dtype)
self.uturn = pd.read_csv(os.path.join(self.path_table, 'child_uturn.csv'), dtype=loading_dtype)
self.coord = pd.read_csv(os.path.join(self.path_table, 'child_coord.csv'), dtype=loading_dtype)
self.nema = pd.read_csv(os.path.join(self.path_table, 'nema.csv'), encoding='cp949', dtype=loading_dtype)
print("1-2. 테이블들이 로드되었습니다.")
# 1-3. 테이블 불러오기
def check_networks(self):
# https://sumo.dlr.de/docs/Netedit/neteditUsageExamples.html#simplify_tls_program_state_after_changing_connections
if 'SUMO_HOME' in os.environ:
tools = os.path.join(os.environ['SUMO_HOME'], 'tools')
if tools not in sys.path:
sys.path.append(tools)
else:
raise EnvironmentError("please declare environment variable 'SUMO_HOME'")
traci.start([sumolib.checkBinary('sumo'), "-n", os.path.join(self.path_root, 'Data', 'networks', 'sn.net.xml')])
nodes = [node for node in self.net.getNodes() if node.getType()=='traffic_light']
for node in nodes:
node_id = node.getID()
from_xml = len([c for c in node.getConnections() if c.getTLLinkIndex() >= 0])
from_traci = len(traci.trafficlight.getRedYellowGreenState(node_id))
if from_xml != from_traci:
sub = {'id': node_id, 'type': 'node', 'note': '유효하지 않은 연결이있음. netedit에서 clean states 필요.'}
self.issues.append(sub)
traci.close()
print("1-3. 네트워크의 모든 clean state requirement들을 체크했습니다.")
# 1-4. 테이블의 무결성 검사
def check_tables(self):
self.check_inter_info()
self.check_angle()
self.check_plan()
print("1-4. 모든 테이블들의 무결성을 검사했고 이상 없습니다.")
pass
# 1-4-1. 교차로정보(inter_info) 검사
def check_inter_info(self):
# 1-4-1-1. inter_lat, inter_lon 적절성 검사
# self.inter_info.loc[0, 'inter_lat'] = 38.0 # 에러 발생을 위한 코드
self.max_lon, self.min_lon = 127.207888, 127.012492
self.max_lat, self.min_lat = 37.480693, 37.337112
for _, row in self.inter_info.iterrows():
latbool = self.min_lat <= row['inter_lat'] <= self.max_lat
lonbool = self.min_lon <= row['inter_lon'] <= self.max_lon
if not(latbool and lonbool):
msg = f"1-4-1-1. 위도 또는 경도가 범위를 벗어난 교차로가 있습니다: inter_no : {row['inter_no']}"
self.issues.append(msg)
# 교차로목록 정의
self.inter_nos = sorted(self.inter_info.inter_no.unique())
# 1-4-2. 방위각정보(inter_info) 검사
def check_angle(self):
# 1-4-2-1. inter_no 검사
# self.angle.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드
missing_inter_nos = set(self.angle.inter_no) - set(self.inter_nos)
if missing_inter_nos:
msg = f"1-4-2-1. angle의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}"
self.issues.append(msg)
# 1-4-3. 신호계획(plan) 검사
def check_plan(self):
# 1-4-3-1. inter_no 검사
# self.plan.loc[0, 'inter_no'] = '4' # 에러 발생을 위한 코드
missing_inter_nos = set(self.plan.inter_no) - set(self.inter_nos)
if missing_inter_nos:
msg = f"1-4-3-1. plan의 inter_no 중 교차로 목록(inter_nos)에 포함되지 않는 항목이 있습니다: {missing_inter_nos}"
self.issues.append(msg)
# 1-4-3-2. 시작시각 검사
# self.plan.loc[0, 'start_hour'] = 27 # 에러 발생을 위한 코드
for _, row in self.plan.iterrows():
start_hour = row.start_hour
start_minute = row.start_minute
if not (0 <= start_hour <= 23) or not (0 <= start_minute <= 59):
msg = f"1-4-3-2. plan에 잘못된 형식의 start_time이 존재합니다: {start_hour, start_minute}"
self.issues.append(msg)
# 1-4-3-3. 현시시간 검사
# self.plan.loc[0, 'dura_A1'] = -2 # 에러 발생을 위한 코드
durations = self.plan[[f'dura_{alph}{j}' for alph in ['A','B'] for j in range(1, 9)]]
valid_indices = ((durations >= 0) & (durations <= 200)).all(axis=1)
invalid_inter_nos = sorted(self.plan[~ valid_indices].inter_no.unique())
if invalid_inter_nos:
msg = f"1-4-3-3. 음수이거나 200보다 큰 현시시간이 존재합니다. : {invalid_inter_nos}"
# 1-4-3-4. 주기 일관성 검사
# self.plan.loc[0, 'cycle'] = 50 # 에러 발생을 위한 코드
inconsistent_cycle = self.plan.groupby(['inter_no', 'start_hour', 'start_minute'])['cycle'].nunique().gt(1)
if inconsistent_cycle.any():
inc_inter_no, start_hour, start_minute = inconsistent_cycle[inconsistent_cycle].index[0]
msg = f"1-4-3-4. inter_no:{inc_inter_no}, start_hour:{start_minute}, start_hour:{start_minute}일 때, cycle이 유일하게 결정되지 않습니다."
self.issues.append(msg)
# 1-4-3-5. 현시시간 / 주기 검사
# self.plan.loc[0, 'duration'] = 10 # 에러 발생을 위한 코드
right_duration = True
for (inter_no, start_hour, start_minute), group in self.plan.groupby(['inter_no', 'start_hour', 'start_minute']):
A_sum = group[[f'dura_A{j}' for j in range(1, 9)]].iloc[0].sum()
B_sum = group[[f'dura_B{j}' for j in range(1, 9)]].iloc[0].sum()
# A_sum = group[group['ring_type']=='A']['duration'].sum()
# B_sum = group[group['ring_type']=='B']['duration'].sum()
cycle = group['cycle'].unique()[0]
if not (A_sum == B_sum == cycle):
right_duration = False
inc_inter_no = inter_no
if not right_duration:
msg = f"1-4-4-5. inter_no:{inc_inter_no}, A링현시시간의 합과 B링현시시간의 합이 일치하지 않거나, 현시시간의 합과 주기가 일치하지 않습니다."
self.issues.append(msg)
# 2. 중간산출물 만들기
def get_intermediates(self):
self.get_matches()
# self.get_movements()
self.get_node2num_cycles()
# 2-1 매칭테이블들 생성
def get_matches(self):
self.make_match1()
self.make_match2()
self.make_match3()
self.make_match4()
self.make_match5()
self.make_match6()
self.make_matching()
# 2-1-1
def make_match1(self):
'''
신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다.
'../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다.
return : 통합된 이동류정보
- 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보
match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다.
'''
# [이동류번호] 불러오기 (약 1분의 소요시간)
path_move = os.path.join(self.path_root, 'Data', 'tables', 'move')
csv_moves = os.listdir(path_move)
moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')]
self.match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True)
self.match1.to_csv(os.path.join(self.path_root, 'Intermediates', 'match1.csv'))
# 2-1-2
def make_match2(self):
'''
match1을 계층화함.
- match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
'''
# 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no')
matchA = self.match1[['inter_no', 'phas_A', 'move_A']].copy()
matchA.columns = ['inter_no', 'phase_no', 'move_no']
matchA['ring_type'] = 'A'
matchB = self.match1[['inter_no', 'phas_B', 'move_B']].copy()
matchB.columns = ['inter_no', 'phase_no', 'move_no']
matchB['ring_type'] = 'B'
self.match2 = pd.concat([matchA, matchB]).drop_duplicates()
self.match2 = self.match2[['inter_no', 'phase_no', 'ring_type', 'move_no']]
self.match2 = self.match2.sort_values(by=list(self.match2.columns))
# 2-1-3
def make_match3(self):
'''
각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함.
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
nema :
- 컬럼 : move_no, inc_dir, out_dir
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# nema 정보 불러오기 및 병합
self.match3 = pd.merge(self.match2, self.nema, how='left', on='move_no').drop_duplicates()
# 2-1-4
def make_match4(self):
'''
방위각 정보를 매칭시켜 추가함.
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
angle_original :
- 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8)
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# 계층화
angles = []
for i, row in self.angle.iterrows():
angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]
new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()})
angles.append(new)
angles = pd.concat(angles)
angles = angles.dropna().reset_index(drop=True)
# 병합
six_chars = angles.angle_code.apply(lambda x:len(x)==6)
angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3])
angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:])
angles = angles.drop('angle_code', axis=1)
self.match4 = pd.merge(self.match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'],
right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates()
# 2-1-5
def make_match5(self):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로).
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
- match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) net
- 성남시 정자동 부근의 샘플 네트워크
(2) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(3) inter_info
- 교차로 정보. 여기에서는 위도와 경도가 쓰임.
- 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no
진입엣지id, 진출엣지id를 얻는 과정 :
- match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함.
* 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음
- 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장.
* inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge
- inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄.
- 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄.
- 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함.
'''
# parent node만 가져옴.
inter_node1 = self.inter_node[self.inter_node.inter_type == 'parent'].drop('inter_type', axis=1)
inter_info1 = self.inter_info[['inter_no', 'inter_lat', 'inter_lon']]
inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'],
right_on=['inter_no']).drop_duplicates()
self.inter2node = dict(zip(inter['inter_no'], inter['node_id']))
self.match5 = self.match4.copy()
# 진입진출ID 매칭
for index, row in self.match5.iterrows():
node_id = self.inter2node[row.inter_no]
node = self.net.getNode(node_id)
# 교차로의 모든 (from / to) edges
inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges
out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges
# 교차로의 모든 (from / to) directions
inc_dirs = []
for inc_edge in inc_edges:
start = inc_edge.getShape()[-2]
end = inc_edge.getShape()[-1]
inc_dir = np.array(end) - np.array(start)
inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5
inc_dirs.append(inc_dir)
out_dirs = []
for out_edge in out_edges:
start = out_edge.getShape()[0]
end = out_edge.getShape()[1]
out_dir = np.array(end) - np.array(start)
out_dir = out_dir / (out_dir ** 2).sum() ** 0.5
out_dirs.append(out_dir)
# 진입각, 진출각 불러오기
if not pd.isna(row.inc_angle):
inc_angle = int(row.inc_angle)
out_angle = int(row.out_angle)
# 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환
inc_angle = (-90 - inc_angle) % 360
inc_angle = inc_angle * np.pi / 180.
inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)])
out_angle = (90 - out_angle) % 360
out_angle = out_angle * np.pi / 180.
out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)])
# 매칭 엣지 반환
inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()
out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax()
inc_edge_id = inc_edges[inc_index].getID()
out_edge_id = out_edges[out_index].getID()
self.match5.at[index, 'inc_edge'] = inc_edge_id
self.match5.at[index, 'out_edge'] = out_edge_id
self.match5['node_id'] = self.match5['inter_no'].map(self.inter2node)
self.match5 = self.match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True)
# 2-1-6
def make_match6(self):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로).
- match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(2) uturn (유턴정보)
- 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 유턴교차로id
- direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향)
- condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나
- inc_edge, out_edge : 유턴에 대한 진입진출엣지
(3) coord (연동교차로정보)
- 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 연동교차로id
- 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지
설명 :
- match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음.
여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함.
유턴교차로 :
- directions를 정북기준 시계방향의 8방위로 정함.
- 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음.
- 예) 진입방향(direction)이 '북'일 때,
- 직진 : (북, 남)
* 남 : directions[(ind + 4) % len(directions)]
- 좌회전 : (북, 동)
* 동 : directions[(ind + 2) % len(directions)]
- 보행 : (서, 동)
* 서 : directions[(ind - 2) % len(directions)]
- uturn의 각 행을 순회하면서 아래 과정을 반복함
- match5에서 parent_id에 해당하는 행들을 가져옴(cmatch).
- condition 별로 진입방향, 진출방향A, 진출방향B 정함.
- 상술한 directions를 활용하여 정함.
- (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함.
- ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
- 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함.
연동교차로 :
- 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음.
- 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음.
- 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음.
match6 :
- 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄.
'''
self.node2inter = dict(zip(self.inter_node['node_id'], self.inter_node['inter_no']))
child_ids = self.inter_node[self.inter_node.inter_type=='child'].node_id.unique()
ch2pa = {} # child to parent
for child_id in child_ids:
parent_no = self.inter_node[self.inter_node.node_id==child_id].inter_no.iloc[0]
sub_inter_node = self.inter_node[self.inter_node.inter_no==parent_no]
ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id
directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향
# 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여
cmatches = []
for _, row in self.uturn.iterrows():
child_id = row.child_id
parent_id = row.parent_id
direction = row.direction
condition = row.condition
inc_edge_id = row.inc_edge
out_edge_id = row.out_edge
# match5에서 parent_id에 해당하는 행들을 가져옴
cmatch = self.match5.copy()[self.match5.node_id==parent_id] # match dataframe for a child node
cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True)
cmatch['node_id'] = child_id
cmatch[['inc_edge', 'out_edge']] = np.nan
# condition 별로 inc_dire, out_dire_A, out_dire_B를 정함
ind = directions.index(direction)
if condition == "좌회전시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)]
elif condition == "직진시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)]
elif condition == "보행신호시":
inc_dire = directions[(ind + 2) % len(directions)]
out_dire_A = directions[(ind - 2) % len(directions)]
out_dire_B = directions[(ind - 2) % len(directions)]
# (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함
if condition == '보행신호시':
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다.
cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
else: # '직진시', '좌회전시'
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 유턴신호의 이동류번호를 19로 부여한다.
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19
cmatches.append(cmatch)
# 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여
self.coord['inter_no'] = self.coord['parent_id'].map(self.node2inter)
self.coord = self.coord.rename(columns={'child_id':'node_id'})
self.coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan
self.coord['move_no'] = 20
self.coord = self.coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']]
# display(coord)
cmatches = pd.concat(cmatches)
self.match6 = pd.concat([self.match5, cmatches, self.coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])
self.match6.to_csv(os.path.join(self.path_root, 'Intermediates', 'match6.csv'))
# 2-1-7
def make_matching(self):
'''
이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다.
모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다.
- matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id
설명 :
- 필요한 리스트, 딕셔너리 등을 정의
(1) 가능한 (진입방향, 진출방향) 목록 [리스트]
(2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리]
(3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리]
(4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리]
(5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리]
- matching은 빈 리스트로 지정.
- 모든 노드id에 대하여 다음 과정을 반복
- 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복
- (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄
- 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append
'''
self.match7 = self.match6.copy()
self.match7 = self.match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']]
parent_ids = sorted(self.inter_node[self.inter_node.inter_type=='parent'].node_id.unique())
child_ids = sorted(self.inter_node[self.inter_node.inter_type=='child'].node_id.unique())
# (1) 가능한 (진입방향, 진출방향) 목록
flows = self.nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist()
# (2) 각 교차로별 방향 목록 : pdires (possible directions)
pdires = {}
for node_id in parent_ids:
dires = self.match7[self.match7.node_id == node_id][['inc_dir','out_dir']].values.flatten()
dires = {dire for dire in dires if type(dire)==str}
pdires[node_id] = dires
# (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id)
inc2id = {}
for node_id in parent_ids:
for inc_dir in pdires[node_id]:
df = self.match7[(self.match7.node_id==node_id) & (self.match7.inc_dir==inc_dir)]
inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0]
# (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id)
out2id = {}
for node_id in parent_ids:
for out_dir in pdires[node_id]:
df = self.match7[(self.match7.node_id==node_id) & (self.match7.out_dir==out_dir)]
out2id[(node_id, out_dir)] = df.out_edge.iloc[0]
# (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows)
pflow = {}
for node_id in parent_ids:
pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])]
# (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching
# node2inter = dict(zip(self.match7['node_id'], self.match7['inter_no']))
dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow
self.matching = []
for node_id in parent_ids:
inter_no = self.node2inter[node_id]
# 좌회전과 직진(1 ~ 16)
for (inc_dir, out_dir) in pflow[node_id]:
move_no = self.nema[(self.nema.inc_dir==inc_dir) & (self.nema.out_dir==out_dir)].move_no.iloc[0]
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
self.matching.append(new_row)
# 보행신호(17), 전적색(18)
new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18],
'inc_dir':[None]*2, 'out_dir':[None]*2,
'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2})
self.matching.append(new_row)
# 신호우회전(21)
for d in range(len(dires_right)-1):
inc_dir = dires_right[d]
out_dir = dires_right[d+1]
if {inc_dir, out_dir}.issubset(pdires[node_id]):
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
self.matching.append(new_row)
self.matching.append(self.match7[self.match7.node_id.isin(child_ids)])
self.matching = pd.concat(self.matching)
self.matching = self.matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)
self.matching['move_no'] = self.matching['move_no'].astype(int)
self.matching.to_csv(os.path.join(self.path_root, 'Intermediates', 'matching.csv'))
# 2-2
def get_movements(self):
movements_path = os.path.join(self.path_root, 'Intermediates', 'movement')
movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')]
movements = pd.concat(movements_list)
movements = movements.drop(columns=['start_unix'])
movements = movements.drop_duplicates()
movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])
movements = movements.reset_index(drop=True)
movements.to_csv(os.path.join(self.path_root, 'Intermediates', 'movements.csv'))
return movements
# 2-3 node2num_cycles : A dictionary that maps a node_id to the number of cycles
def get_node2num_cycles(self):
# node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))
self.node_ids = sorted(self.inter_node.node_id.unique())
Aplan = self.plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']]
grouped = Aplan.groupby('inter_no')
df = grouped.agg({'cycle': 'min'}).reset_index()
df = df.rename(columns={'cycle': 'min_cycle'})
df['num_cycle'] = 300 // df['min_cycle'] + 2
inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle']))
node2numcycles = {node_id : inter2num_cycles[self.node2inter[node_id]] for node_id in self.node_ids}
with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file:
json.dump(node2numcycles, file, indent=4)
return node2numcycles
# 3. 이슈사항 저장
def write_issues(self):
path_issues = os.path.join(self.path_root, "Results", "issues_intermediates.txt")
with open(path_issues, "w", encoding="utf-8") as file:
for item in self.issues:
file.write(item + "\n")
if self.issues:
print("데이터 처리 중 발생한 특이사항은 다음과 같습니다. :")
for review in self.issues:
print(review)
def main(self):
# 1. 데이터 불러오기
self.load_data()
# 2. 중간산출물 만들기
self.get_intermediates()
# 3. 이슈사항 저장
self.write_issues()
if __name__ == '__main__':
self = DailyPreprocess()
self.main()

+ 0
- 19
Script/preprocess_5min.ipynb View File

@ -41,7 +41,6 @@
"metadata": {},
"outputs": [],
"source": [
"# 5초 단위로 이동류번호 저장 및 신호이력에서 유닉스시각 가져와서 표시, 한시간동안의 데이터만 보관\n",
"midnight = int(datetime(2024, 1, 5, 0, 0, 0).timestamp())\n",
"next_day = int(datetime(2024, 1, 6, 0, 0, 0).timestamp())\n",
"fsecs = range(midnight, next_day, 5) # fsecs : unix time by Five SECondS\n",
@ -581,24 +580,6 @@
" return movement_updated"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def make_movements():\n",
" movements_path = '../Intermediates/movement/'\n",
" movements_list = [pd.read_csv(movements_path + file, index_col=0) for file in tqdm(os.listdir(movements_path))]\n",
" movements = pd.concat(movements_list)\n",
" movements = movements.drop(columns=['start_unix'])\n",
" movements = movements.drop_duplicates()\n",
" movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])\n",
" movements = movements.reset_index(drop=True)\n",
" movements.to_csv('../Intermediates/movements.csv')\n",
" return movements"
]
},
{
"cell_type": "code",
"execution_count": 13,

+ 580
- 419
Script/preprocess_daily.py
File diff suppressed because it is too large
View File


+ 431
- 0
Script/preprocess_daily_0.py View File

@ -0,0 +1,431 @@
import pandas as pd
import numpy as np
import os
import json
import sumolib
from tqdm import tqdm
def check_inter_info(inter_info):
print(inter_info)
print('check')
def make_match1(path_root):
'''
신호 DB에는 매 초마다 이동류정보가 업데이트 된다. 그리고 이 이동류정보를 매 5초마다 불러와서 사용하게 된다.
'../Data/tables/move/'에는 5초마다의 이동류정보가 저장되어 있다.
return : 통합된 이동류정보
- 모든 inter_no(교차로번호)에 대한 A, B링 현시별 이동류정보
match1을 만드는 데 시간이 소요되므로 한 번 만들어서 저장해두고 저장해둔 것을 쓴다.
'''
# [이동류번호] 불러오기 (약 1분의 소요시간)
path_move = os.path.join(path_root, 'Data', 'tables', 'move')
csv_moves = os.listdir(path_move)
moves = [pd.read_csv(os.path.join(path_move, csv_move), index_col=0) for csv_move in tqdm(csv_moves, desc='이동류정보 불러오는 중 : match1')]
match1 = pd.concat(moves).drop_duplicates().sort_values(by=['inter_no','phas_A','phas_B']).reset_index(drop=True)
match1.to_csv(os.path.join(path_root, 'Intermediates', 'match1.csv'))
return match1
def make_match2(match1):
'''
match1을 계층화함.
- match1의 컬럼 : inter_no, phas_A, phas_B, move_A, move_B
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
'''
# 계층화 (inter_no, phas_A, phas_B, move_A, move_B) -> ('inter_no', 'phase_no', 'ring_type', 'move_no')
matchA = match1[['inter_no', 'phas_A', 'move_A']].copy()
matchA.columns = ['inter_no', 'phase_no', 'move_no']
matchA['ring_type'] = 'A'
matchB = match1[['inter_no', 'phas_B', 'move_B']].copy()
matchB.columns = ['inter_no', 'phase_no', 'move_no']
matchB['ring_type'] = 'B'
match2 = pd.concat([matchA, matchB]).drop_duplicates()
match2 = match2[['inter_no', 'phase_no', 'ring_type', 'move_no']]
match2 = match2.sort_values(by=list(match2.columns))
return match2
def make_match3(match2, nema):
'''
각 movement들에 방향(진입방향, 진출방향)을 매칭시켜 추가함.
- match2의 컬럼 : inter_no, phase_no, ring_type, move_no
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
nema :
- 컬럼 : move_no, inc_dir, out_dir
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# nema 정보 불러오기 및 병합
match3 = pd.merge(match2, nema, how='left', on='move_no').drop_duplicates()
return match3
def make_match4(match3, angle):
'''
방위각 정보를 매칭시켜 추가함.
- match3의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
angle_original :
- 컬럼 : inter_no, angle_Aj, angle_Bj (j : 1 ~ 8)
- 모든 종류의 이동류번호에 대하여 진입방향과 진출방향을 매칭시키는 테이블
- 이동류번호 : 1 ~ 16, 17, 18, 21
- 진입, 진출방향(8방위) : 동, 서, 남, 북, 북동, 북서, 남동, 남서
'''
# 계층화
angles = []
for i, row in angle.iterrows():
angle_codes = row[[f'angle_{alph}{j}' for alph in ['A', 'B'] for j in range(1,9)]]
new = pd.DataFrame({'inter_no':[row.inter_no] * 16, 'phase_no':list(range(1, 9))*2, 'ring_type':['A'] * 8 + ['B'] * 8, 'angle_code':angle_codes.to_list()})
angles.append(new)
angles = pd.concat(angles)
angles = angles.dropna().reset_index(drop=True)
# 병합
six_chars = angles.angle_code.apply(lambda x:len(x)==6)
angles.loc[six_chars,'inc_angle'] = angles.angle_code.apply(lambda x:x[:3])
angles.loc[six_chars,'out_angle'] = angles.angle_code.apply(lambda x:x[3:])
angles = angles.drop('angle_code', axis=1)
match4 = pd.merge(match3, angles, how='left', left_on=['inter_no', 'phase_no', 'ring_type'],
right_on=['inter_no', 'phase_no', 'ring_type']).drop_duplicates()
return match4
def make_match5(match4, net, inter_node, inter_info):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (주교차로).
- match4의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle
- match5의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) net
- 성남시 정자동 부근의 샘플 네트워크
(2) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(3) inter_info
- 교차로 정보. 여기에서는 위도와 경도가 쓰임.
- 컬럼 : inter_no, inter_name, inter_lat, inter_lon, group_no, main_phase_no
진입엣지id, 진출엣지id를 얻는 과정 :
- match5 = match4.copy()의 각 열을 순회하면서 아래 과정을 반복함.
* 진입에 대해서만 서술하겠지만 진출도 마찬가지로 설명될 수 있음
- 해당 행의 교차로정보로부터 노드ID를 얻어내고, 해당 노드에 대한 모든 진출엣지id를 inc_edges에 저장.
* inc_edge(진입엣지) : incoming edge, out_edge(진출엣지) : outgoing_edge
- inc_edges의 모든 진입엣지에 대하여 진입방향(inc_dires, 2차원 단위벡터)을 얻어냄.
- 해당 행의 진입각으로부터 그에 대응되는 진입각방향(단위벡터)를 얻어냄.
- 주어진 진입각방향에 대하여 내적이 가장 작은 진입방향에 대한 진입엣지를 inc_edge_id로 지정함.
'''
# parent node만 가져옴.
inter_node1 = inter_node[inter_node.inter_type == 'parent'].drop('inter_type', axis=1)
inter_info1 = inter_info[['inter_no', 'inter_lat', 'inter_lon']]
inter = pd.merge(inter_node1, inter_info1, how='left', left_on=['inter_no'],
right_on=['inter_no']).drop_duplicates()
inter2node = dict(zip(inter['inter_no'], inter['node_id']))
match5 = match4.copy()
# 진입진출ID 매칭
for index, row in match5.iterrows():
node_id = inter2node[row.inter_no]
node = net.getNode(node_id)
# 교차로의 모든 (from / to) edges
inc_edges = [edge for edge in node.getIncoming() if edge.getFunction() == ''] # incoming edges
out_edges = [edge for edge in node.getOutgoing() if edge.getFunction() == ''] # outgoing edges
# 교차로의 모든 (from / to) directions
inc_dirs = []
for inc_edge in inc_edges:
start = inc_edge.getShape()[-2]
end = inc_edge.getShape()[-1]
inc_dir = np.array(end) - np.array(start)
inc_dir = inc_dir / (inc_dir ** 2).sum() ** 0.5
inc_dirs.append(inc_dir)
out_dirs = []
for out_edge in out_edges:
start = out_edge.getShape()[0]
end = out_edge.getShape()[1]
out_dir = np.array(end) - np.array(start)
out_dir = out_dir / (out_dir ** 2).sum() ** 0.5
out_dirs.append(out_dir)
# 진입각, 진출각 불러오기
if not pd.isna(row.inc_angle):
inc_angle = int(row.inc_angle)
out_angle = int(row.out_angle)
# 방위각을 일반각으로 가공, 라디안 변환, 단위벡터로 변환
inc_angle = (-90 - inc_angle) % 360
inc_angle = inc_angle * np.pi / 180.
inc_dir_true = np.array([np.cos(inc_angle), np.sin(inc_angle)])
out_angle = (90 - out_angle) % 360
out_angle = out_angle * np.pi / 180.
out_dir_true = np.array([np.cos(out_angle), np.sin(out_angle)])
# 매칭 엣지 반환
inc_index = np.array([np.dot(inc_dir, inc_dir_true) for inc_dir in inc_dirs]).argmax()
out_index = np.array([np.dot(out_dir, out_dir_true) for out_dir in out_dirs]).argmax()
inc_edge_id = inc_edges[inc_index].getID()
out_edge_id = out_edges[out_index].getID()
match5.at[index, 'inc_edge'] = inc_edge_id
match5.at[index, 'out_edge'] = out_edge_id
match5['node_id'] = match5['inter_no'].map(inter2node)
match5 = match5.sort_values(by=['inter_no','phase_no','ring_type']).reset_index(drop=True)
return match5
def make_match6(match5, inter_node, uturn, coord, path_root):
'''
진입엣지id, 진출엣지id, 노드id를 추가함 (부교차로).
- match6의 컬럼 : inter_no, phase_no, ring_type, move_no, inc_dir, out_dir, inc_angle, out_angle, inc_edge, out_edge, node_id
사용된 데이터 :
(1) inter_node
- 교차로번호와 노드id를 매칭시키는 테이블.
- parent/child 정보도 포함되어 있음
- 컬럼 : inter_no, node_id, inter_type
(2) uturn (유턴정보)
- 컬럼 : parent_id, child_id, direction, condition, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 유턴교차로id
- direction : 주교차로에 대한 유턴노드의 상대적인 위치(방향)
- condition : 좌회전시, 직진시, 직좌시, 보행신호시 중 하나
- inc_edge, out_edge : 유턴에 대한 진입진출엣지
(3) coord (연동교차로정보)
- 컬럼 : parent_id, child_id, phase_no, ring_type, inc_edge, out_edge
- parent_id, child_id : 주교차로id, 연동교차로id
- 나머지 컬럼 : 각 (현시, 링)별 진입진출엣지
설명 :
- match5는 주교차로에 대해서만 진입엣지id, 진출엣지id, 노드id를 추가했었음.
여기에서 uturn, coord를 사용해서 부교차로들(유턴교차로, 연동교차로)에 대해서도 해당 값들을 부여함.
유턴교차로 :
- directions를 정북기준 시계방향의 8방위로 정함.
- 이를 통해 진입방향이 주어진 경우에 좌회전, 직진, 보행 등에 대한 (진입방향, 진출방향)을 얻어낼 수 있음.
- 예) 진입방향(direction)이 '북'일 때,
- 직진 : (북, 남)
* 남 : directions[(ind + 4) % len(directions)]
- 좌회전 : (북, 동)
* 동 : directions[(ind + 2) % len(directions)]
- 보행 : (서, 동)
* 서 : directions[(ind - 2) % len(directions)]
- uturn의 각 행을 순회하면서 아래 과정을 반복함
- match5에서 parent_id에 해당하는 행들을 가져옴(cmatch).
- condition 별로 진입방향, 진출방향A, 진출방향B 정함.
- 상술한 directions를 활용하여 정함.
- (진입방향, 진출방향A, 진출방향B)을 고려하여 (현시, 링) 별로 진입엣지id, 진출엣지id를 정함.
- ex) cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
- 순회하면서 만든 cmatch를 cmatchs라는 리스트에 저장함.
연동교차로 :
- 연동교차로의 경우 coord에 (현시, 링)별 진입엣지ID, 진출엣지ID가 명시되어 있음.
- 'inc_dir', 'out_dir', 'inc_angle','out_angle'와 같은 열들은 np.nan을 지정해놓음.
- 이 열들은, 사실상 다음 스텝부터는 사용되지 않는 열들이기 때문에 np.nan으로 지정해놓아도 문제없음.
match6 :
- 이렇게 얻은 match5, cmatchs, coord를 모두 pd.concat하여 match6을 얻어냄.
'''
node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))
child_ids = inter_node[inter_node.inter_type=='child'].node_id.unique()
ch2pa = {} # child to parent
for child_id in child_ids:
parent_no = inter_node[inter_node.node_id==child_id].inter_no.iloc[0]
sub_inter_node = inter_node[inter_node.inter_no==parent_no]
ch2pa[child_id] = sub_inter_node[sub_inter_node.inter_type=='parent'].iloc[0].node_id
directions = ['북', '북동', '동', '남동', '남', '남서', '서', '북서'] # 정북기준 시계방향으로 8방향
# 각 uturn node에 대하여 (inc_edge_id, out_edge_id) 부여
cmatches = []
for _, row in uturn.iterrows():
child_id = row.child_id
parent_id = row.parent_id
direction = row.direction
condition = row.condition
inc_edge_id = row.inc_edge
out_edge_id = row.out_edge
# match5에서 parent_id에 해당하는 행들을 가져옴
cmatch = match5.copy()[match5.node_id==parent_id] # match dataframe for a child node
cmatch = cmatch.sort_values(by=['phase_no', 'ring_type']).reset_index(drop=True)
cmatch['node_id'] = child_id
cmatch[['inc_edge', 'out_edge']] = np.nan
# condition 별로 inc_dire, out_dire_A, out_dire_B를 정함
ind = directions.index(direction)
if condition == "좌회전시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 2) % len(directions)]
elif condition == "직진시":
inc_dire = direction
out_dire_A = out_dire_B = directions[(ind + 4) % len(directions)]
elif condition == "보행신호시":
inc_dire = directions[(ind + 2) % len(directions)]
out_dire_A = directions[(ind - 2) % len(directions)]
out_dire_B = directions[(ind - 2) % len(directions)]
# (inc_dire, out_dire_A, out_dire_B) 별로 inc_edge_id, out_edge_id를 정함
if condition == '보행신호시':
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 이동류번호가 17(보행신호)이면서 유턴노드방향으로 가는 신호가 없으면 (inc_edge_id, out_edge_id)를 부여한다.
cmatch.loc[(cmatch.move_no==17) & (cmatch.out_dir!=direction), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
else: # '직진시', '좌회전시'
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), ['inc_edge', 'out_edge']] = [inc_edge_id, out_edge_id]
# 유턴신호의 이동류번호를 19로 부여한다.
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_A), 'move_no'] = 19
cmatch.loc[(cmatch.inc_dir==inc_dire) & (cmatch.out_dir==out_dire_B), 'move_no'] = 19
cmatches.append(cmatch)
# 각 coordination node에 대하여 (inc_edge_id, out_edge_id) 부여
coord['inter_no'] = coord['parent_id'].map(node2inter)
coord = coord.rename(columns={'child_id':'node_id'})
coord[['inc_dir', 'out_dir', 'inc_angle','out_angle']] = np.nan
coord['move_no'] = 20
coord = coord[['inter_no', 'phase_no', 'ring_type', 'move_no', 'inc_dir', 'out_dir', 'inc_angle','out_angle', 'inc_edge', 'out_edge', 'node_id']]
# display(coord)
cmatches = pd.concat(cmatches)
match6 = pd.concat([match5, cmatches, coord]).drop_duplicates().sort_values(by=['inter_no', 'node_id', 'phase_no', 'ring_type'])
match6.to_csv(os.path.join(path_root, 'Intermediates', 'match6.csv'))
return match6
def make_matching(match6, inter_node, nema, path_root):
'''
이동류 매칭 : 각 교차로에 대하여, 가능한 모든 이동류 (1~18, 21)에 대한 진입·진출엣지ID를 지정한다.
모든 이동류에 대해 지정하므로, 시차제시 이전과 다른 이동류가 등장하더라도 항상 진입·진출 엣지 ID를 지정할 수 있다.
- matching의 컬럼 : inter_no, move_no, inc_dir, out_dir, inc_edge, out_edge, node_id
설명 :
- 필요한 리스트, 딕셔너리 등을 정의
(1) 가능한 (진입방향, 진출방향) 목록 [리스트]
(2) 각 교차로별 방향 목록 : pdires (possible directions) [딕셔너리]
(3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id) [딕셔너리]
(4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id) [딕셔너리]
(5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows) [딕셔너리]
- matching은 빈 리스트로 지정.
- 모든 노드id에 대하여 다음 과정을 반복
- 해당 노드id에 대한 모든 가능한 (진입방향, 진출방향)에 대하여 다음 과정을 반복
- (노드id, 진입방향)으로부터 진입엣지id를 얻어냄. 마찬가지로 진출엣지id도 얻어냄
- 얻어낸 정보를 바탕으로 한 행(new_row)을 만들고 이것을 matching에 append
'''
match7 = match6.copy()
match7 = match7[['inter_no', 'move_no', 'inc_dir', 'out_dir', 'inc_edge', 'out_edge', 'node_id']]
parent_ids = sorted(inter_node[inter_node.inter_type=='parent'].node_id.unique())
child_ids = sorted(inter_node[inter_node.inter_type=='child'].node_id.unique())
# (1) 가능한 (진입방향, 진출방향) 목록
flows = nema.dropna().apply(lambda row: (row['inc_dir'], row['out_dir']), axis=1).tolist()
# (2) 각 교차로별 방향 목록 : pdires (possible directions)
pdires = {}
for node_id in parent_ids:
dires = match7[match7.node_id == node_id][['inc_dir','out_dir']].values.flatten()
dires = {dire for dire in dires if type(dire)==str}
pdires[node_id] = dires
# (3) 각 (교차로, 진입방향) 별 진입id 목록 : inc2id (incoming direction to incoming edge_id)
inc2id = {}
for node_id in parent_ids:
for inc_dir in pdires[node_id]:
df = match7[(match7.node_id==node_id) & (match7.inc_dir==inc_dir)]
inc2id[(node_id, inc_dir)] = df.inc_edge.iloc[0]
# (4) 각 (교차로, 진출방향) 별 진출id 목록 : out2id (outgoing direction to outgoing edge_id)
out2id = {}
for node_id in parent_ids:
for out_dir in pdires[node_id]:
df = match7[(match7.node_id==node_id) & (match7.out_dir==out_dir)]
out2id[(node_id, out_dir)] = df.out_edge.iloc[0]
# (5) 각 교차로별 가능한 (진입방향, 진출방향) 목록 : pflow (possible flows)
pflow = {}
for node_id in parent_ids:
pflow[node_id] = [flow for flow in flows if set(flow).issubset(pdires[node_id])]
# (6) 가능한 이동류에 대하여 진입id, 진출id 배정 : matching
node2inter = dict(zip(match7['node_id'], match7['inter_no']))
dires_right = ['북', '서', '남', '동', '북'] # ex (북, 서), (서, 남) 등은 우회전 flow
matching = []
for node_id in parent_ids:
inter_no = node2inter[node_id]
# 좌회전과 직진(1 ~ 16)
for (inc_dir, out_dir) in pflow[node_id]:
move_no = nema[(nema.inc_dir==inc_dir) & (nema.out_dir==out_dir)].move_no.iloc[0]
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[move_no],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
matching.append(new_row)
# 보행신호(17), 전적색(18)
new_row = pd.DataFrame({'inter_no':[inter_no] * 2, 'move_no':[17, 18],
'inc_dir':[None]*2, 'out_dir':[None]*2,
'inc_edge':[None]*2, 'out_edge':[None]*2, 'node_id':[node_id]*2})
matching.append(new_row)
# 신호우회전(21)
for d in range(len(dires_right)-1):
inc_dir = dires_right[d]
out_dir = dires_right[d+1]
if {inc_dir, out_dir}.issubset(pdires[node_id]):
inc_edge = inc2id[(node_id, inc_dir)]
out_edge = out2id[(node_id, out_dir)]
new_row = pd.DataFrame({'inter_no':[inter_no], 'move_no':[21],
'inc_dir':[inc_dir], 'out_dir':[out_dir],
'inc_edge':[inc_edge], 'out_edge':[out_edge], 'node_id':[node_id]})
matching.append(new_row)
matching.append(match7[match7.node_id.isin(child_ids)])
matching = pd.concat(matching)
matching = matching.dropna().sort_values(by=['inter_no', 'node_id', 'move_no']).reset_index(drop=True)
matching['move_no'] = matching['move_no'].astype(int)
matching.to_csv(os.path.join(path_root, 'Intermediates', 'matching.csv'))
return matching
def make_movements(path_root):
movements_path = os.path.join(path_root, 'Intermediates', 'movement')
movements_list = [pd.read_csv(os.path.join(movements_path, file), index_col=0) for file in tqdm(os.listdir(movements_path), desc='이동류정보 불러오는 중 : movements')]
movements = pd.concat(movements_list)
movements = movements.drop(columns=['start_unix'])
movements = movements.drop_duplicates()
movements = movements.sort_values(by=['inter_no', 'phas_A', 'phas_B'])
movements = movements.reset_index(drop=True)
movements.to_csv(os.path.join(path_root, 'Intermediates', 'movements.csv'))
return movements
# node2num_cycles : A dictionary that maps a node_id to the number of cycles
def get_node2num_cycles(plan, inter_node, path_root):
node2inter = dict(zip(inter_node['node_id'], inter_node['inter_no']))
node_ids = sorted(inter_node.node_id.unique())
Aplan = plan.copy()[['inter_no'] + [f'dura_A{j}' for j in range(1,9)] + ['cycle']]
grouped = Aplan.groupby('inter_no')
df = grouped.agg({'cycle': 'min'}).reset_index()
df = df.rename(columns={'cycle': 'min_cycle'})
df['num_cycle'] = 300 // df['min_cycle'] + 2
inter2num_cycles = dict(zip(df['inter_no'], df['num_cycle']))
node2numcycles = {node_id : inter2num_cycles[node2inter[node_id]] for node_id in node_ids}
with open(os.path.join('Intermediates','node2numcycles.json'), 'w') as file:
json.dump(node2numcycles, file, indent=4)
return node2numcycles
def main():
path_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
inter_info = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'inter_info.csv'))
check_inter_info(inter_info)
angle = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'angle.csv'), dtype = {f'angle_{alph}{j}':'str' for alph in ['A', 'B'] for j in range(1,9)})
plan = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'plan.csv'))
inter_node = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'inter_node.csv'))
uturn = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'child_uturn.csv'))
coord = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'child_coord.csv'))
nema = pd.read_csv(os.path.join(path_root, 'Data', 'tables', 'nema.csv'), encoding='cp949')
net = sumolib.net.readNet(os.path.join(path_root, 'Data', 'networks', 'sn.net.xml'))
match1 = make_match1(path_root)
match2 = make_match2(match1)
match3 = make_match3(match2, nema)
match4 = make_match4(match3, angle)
match5 = make_match5(match4, net, inter_node, inter_info)
match6 = make_match6(match5, inter_node, uturn, coord, path_root)
matching = make_matching(match6, inter_node, nema, path_root)
movements = make_movements(path_root)
node2num_cycles = get_node2num_cycles(plan, inter_node, path_root)
if __name__ == '__main__':
main()

BIN
state_300.00.xml.gz View File


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