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  1. install.packages("jsonlite")
  2. scen <- jsonlite::read_json("scenario.json")
  3.  
  4. plot(1:length(scen$simulation$vehicle0$values[[1]]), scen$simulation$vehicle0$values[[1]], type="n", xlab="time steps", ylab="cell position of vehicle")
  5. lines(1:length(scen$simulation$vehicle0$values[[1]]), scen$simulation$vehicle0$values[[1]], type = "l", col = "black")
  6.    
  7. {
  8. "configuration" : {
  9.    "cellsize_in_meter" : 7.5,
  10.    "timestep_in_minutes" : 0.1,
  11.    "simulationtime_in_minutes" : 30.0
  12. },
  13. "simulation" : {
  14.  "vehicle0" : {
  15.   "max" : 1331.0,
  16.   "mean" : 598.7886309513347,
  17.   "min" : 3.0,
  18.   "count" : 300,
  19.   "standarddeviation" : 394.2135720068928,
  20.   "sum" : 179636.58928540035,
  21.   "variance" : 155404.34035443363,
  22.   "populationvariance" : 154886.32588658552,
  23.   "quadraticmean" : 716.5431950993318,
  24.   "geometricmean" : 409.8459606287298,
  25.   "25-percentile" : 248.25,
  26.   "50-percentile" : 562.0,
  27.   "75-percentile" : 938.75,
  28.   "kurtosis" : -1.1913990481648709,
  29.   "skewness" : 0.2111748583848508,
  30.   "values" : [ [ 38.58928540035259, 38.0, 38.0, 38.0, 39.0, 41.0, 44.0, 48.0, 52.0, 57.0, 61.0, 65.0, 70.0, 76.0, 83.0, 91.0, 100.0, 109.0, 119.0, 130.0, 142.0, 155.0, 168.0, 182.0, 197.0, 213.0, 230.0, 247.0, 265.0, 284.0, 304.0, 325.0, 346.0, 368.0, 389.0, 411.0, 432.0, 454.0, 476.0, 498.0, 517.0, 537.0, 558.0, 580.0, 601.0, 623.0, 644.0, 666.0, 687.0, 709.0, 730.0, 752.0, 773.0, 795.0, 816.0, 836.0, 857.0, 878.0, 900.0, 922.0, 944.0, 966.0, 988.0, 1010.0, 1030.0, 1051.0, 1073.0, 1094.0, 1116.0, 1137.0, 1159.0, 1180.0, 1200.0, 1221.0, 1242.0, 1264.0, 1285.0, 1305.0, 1324.0, 10.0, 28.0, 47.0, 66.0, 86.0, 107.0, 129.0, 150.0, 172.0, 193.0, 215.0, 236.0, 258.0, 279.0, 301.0, 322.0, 344.0, 365.0, 387.0, 408.0, 428.0, 447.0, 466.0, 486.0, 507.0, 529.0, 550.0, 570.0, 590.0, 611.0, 633.0, 654.0, 674.0, 695.0, 716.0, 738.0, 757.0, 777.0, 798.0, 820.0, 843.0, 865.0, 886.0, 908.0, 929.0, 951.0, 972.0, 994.0, 1015.0, 1037.0, 1058.0, 1080.0, 1101.0, 1123.0, 1144.0, 1166.0, 1187.0, 1207.0, 1228.0, 1247.0, 1267.0, 1288.0, 1310.0, 1331.0, 20.0, 41.0, 63.0, 84.0, 106.0, 127.0, 149.0, 168.0, 188.0, 209.0, 230.0, 252.0, 273.0, 295.0, 317.0, 339.0, 358.0, 376.0, 395.0, 414.0, 434.0, 455.0, 477.0, 498.0, 520.0, 541.0, 563.0, 584.0, 606.0, 627.0, 649.0, 670.0, 692.0, 713.0, 735.0, 756.0, 778.0, 800.0, 822.0, 844.0, 866.0, 888.0, 908.0, 929.0, 951.0, 972.0, 994.0, 1015.0, 1037.0, 1058.0, 1080.0, 1099.0, 1119.0, 1140.0, 1161.0, 1183.0, 1204.0, 1226.0, 1248.0, 1270.0, 1292.0, 1314.0, 3.0, 25.0, 47.0, 70.0, 92.0, 113.0, 135.0, 156.0, 176.0, 196.0, 215.0, 235.0, 256.0, 277.0, 299.0, 320.0, 342.0, 364.0, 386.0, 408.0, 430.0, 452.0, 474.0, 496.0, 518.0, 540.0, 561.0, 583.0, 604.0, 626.0, 647.0, 669.0, 690.0, 712.0, 733.0, 755.0, 776.0, 798.0, 819.0, 841.0, 862.0, 882.0, 903.0, 922.0, 942.0, 963.0, 985.0, 1006.0, 1028.0, 1049.0, 1071.0, 1092.0, 1114.0, 1135.0, 1157.0, 1178.0, 1200.0, 1219.0, 1239.0, 1260.0, 1279.0, 1299.0, 1318.0, 4.0, 24.0, 43.0, 63.0, 83.0, 104.0, 126.0, 147.0, 169.0, 190.0, 212.0, 233.0, 255.0, 276.0, 298.0, 317.0, 337.0, 358.0, 380.0, 402.0, 424.0, 446.0, 465.0, 485.0, 506.0, 528.0, 549.0, 571.0, 592.0, 614.0, 635.0, 657.0 ] ]
  31.   }
  32.  }
  33. }
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