The following goes through a few scenarios, but is not a complete exploration of the space. The interactive web applet is provided so that values for any segment and different assumed uncertainty can be used as input.

Scenario 1

(Note: I changed the Scenario letters to numbers and low, medium, high to A, B, and C – to avoid implying a magnitude for the SDs because it is unknown what should count as “low” and “high”. The code is not updated to reflect this change, but plot labels are.) Scenario 1 is based on data for segment "“CR 846: Jones Mining Rd. to Wild Turkey Dr” found in Row 7 in the Traffic_Analysis_by_Segments file.

preds_A <- dplyr::filter(A_out, Year==2060)
summary_A <- tapply(preds_A$PVM_sim, preds_A$name, summary)
sd_A <- tapply(preds_A$PVM_sim, preds_A$name, sd)
summary_A
## $`1 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.777   1.857   1.880   1.879   1.899   1.995 
## 
## $`1 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.474   1.776   1.876   1.892   1.995   2.559 
## 
## $`1 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.123   1.678   1.871   1.903   2.100   3.781
sd_A
##      1 - A      1 - B      1 - C 
## 0.03203428 0.16320138 0.32368358

Scenario 2

Scenario 2 is based on data for segment “CCR 846: 0.5 mile south of CR 833” found in Row 20 in the Traffic_Analysis_by_Segments file.

preds_B <- dplyr::filter(B_out, Year==2060)
summary_B <- tapply(preds_B$PVM_sim, preds_B$name, summary)
sd_B <- tapply(preds_B$PVM_sim, preds_B$name, sd)
summary_B
## $`2 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1617  0.2130  0.2258  0.2258  0.2381  0.2938 
## 
## $`2 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1176  0.1980  0.2248  0.2280  0.2556  0.3847 
## 
## $`2 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.04373 0.18604 0.22464 0.22943 0.26872 0.49720
sd_B
##      2 - A      2 - B      2 - C 
## 0.02013700 0.04242561 0.06442108

Scenario C

Scenario C is based on data for segment “I-75: Collier-Dade County line to SR29” found in Row 75 in the Traffic_Analysis_by_Segments file. I-75: Collier-Dade County line to SR29

preds_C <- dplyr::filter(C_out, Year==2060)
summary_C <- tapply(preds_C$PVM_sim, preds_C$name, summary)
sd_C <- tapply(preds_C$PVM_sim, preds_C$name, sd)
summary_C
## $`3 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4940  0.6048  0.6325  0.6323  0.6605  0.7872 
## 
## $`3 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3743  0.5766  0.6277  0.6318  0.6819  0.9952 
## 
## $`3 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3028  0.5492  0.6382  0.6548  0.7376  1.5060
sd_C
##      3 - A      3 - B      3 - C 
## 0.04358866 0.08590980 0.15468720

Scenario D

Scenario D is based on data for segment “SR 29 at Sunniland Mine” found in Row 28 in the Traffic_Analysis_by_Segments file.

SR 29 at Sunniland Mine 63077 CC-MS2 4,104 2 0.4 21,717 96.5% 65.8% 2.12 1.39 1.69

preds_D <- dplyr::filter(D_out, Year==2060)
summary_D <- tapply(preds_D$PVM_sim, preds_D$name, summary)
sd_D <- tapply(preds_D$PVM_sim, preds_D$name, sd)
summary_D
## $`4 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.689   2.041   2.117   2.123   2.198   2.487 
## 
## $`4 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.403   1.988   2.126   2.129   2.269   2.790 
## 
## $`4 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6724  1.8295  2.1073  2.1378  2.4110  3.7515
sd_D
##     4 - A     4 - B     4 - C 
## 0.1145243 0.2173356 0.4489942

Combine all Scenarios

## $`2 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1617  0.2130  0.2258  0.2258  0.2381  0.2938 
## 
## $`2 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1176  0.1980  0.2248  0.2280  0.2556  0.3847 
## 
## $`2 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.04373 0.18604 0.22464 0.22943 0.26872 0.49720 
## 
## $`3 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.4940  0.6048  0.6325  0.6323  0.6605  0.7872 
## 
## $`3 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3743  0.5766  0.6277  0.6318  0.6819  0.9952 
## 
## $`3 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.3028  0.5492  0.6382  0.6548  0.7376  1.5060 
## 
## $`1 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.777   1.857   1.880   1.879   1.899   1.995 
## 
## $`1 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.474   1.776   1.876   1.892   1.995   2.559 
## 
## $`1 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.123   1.678   1.871   1.903   2.100   3.781 
## 
## $`4 - A`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.689   2.041   2.117   2.123   2.198   2.487 
## 
## $`4 - B`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.403   1.988   2.126   2.129   2.269   2.790 
## 
## $`4 - C`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.6724  1.8295  2.1073  2.1378  2.4110  3.7515
##      2 - A      2 - B      2 - C      3 - A      3 - B      3 - C 
## 0.02013700 0.04242561 0.06442108 0.04358866 0.08590980 0.15468720 
##      1 - A      1 - B      1 - C      4 - A      4 - B      4 - C 
## 0.03203428 0.16320138 0.32368358 0.11452433 0.21733564 0.44899421
## $`2 - A`
##      2.5%     97.5% 
## 0.1865366 0.2689103 
## 
## $`2 - B`
##      2.5%     97.5% 
## 0.1507745 0.3191645 
## 
## $`2 - C`
##      2.5%     97.5% 
## 0.1129177 0.3617271 
## 
## $`3 - A`
##      2.5%     97.5% 
## 0.5419829 0.7208339 
## 
## $`3 - B`
##      2.5%     97.5% 
## 0.4742719 0.8181183 
## 
## $`3 - C`
##      2.5%     97.5% 
## 0.4066337 0.9930504 
## 
## $`1 - A`
##     2.5%    97.5% 
## 1.818887 1.942873 
## 
## $`1 - B`
##     2.5%    97.5% 
## 1.587692 2.228188 
## 
## $`1 - C`
##     2.5%    97.5% 
## 1.341097 2.585276 
## 
## $`4 - A`
##     2.5%    97.5% 
## 1.914703 2.363387 
## 
## $`4 - B`
##     2.5%    97.5% 
## 1.698443 2.597586 
## 
## $`4 - C`
##     2.5%    97.5% 
## 1.346673 3.121243
## [1] "Scenario A: PVM baseline = 0.8    AADT 2017 = 7786    AADT 2060 = 18285"
## [1] "Scenario B: PVM baseline = 0.2    AADT 2017 = 1100    AADT 2060 = 1241"
## [1] "Scenario C: PVM baseline = 0.4    AADT 2017 = 24000    AADT 2060 = 37833"
## [1] "Scenario D: PVM baseline = 0.4    AADT 2017 = 4104    AADT 2060 = 21717"
##   AADT_baseline SD_AADT_baseline AADT_projected SD_AADT_projected
## 1          7786              100          18285               200
## 2          1100               50           1241               100
## 3         24000             1000          37833              2000
## 4          4104              100          21717              1000
##   PVM_baseline
## 1          0.8
## 2          0.2
## 3          0.4
## 4          0.4
AADT_baseline SD_AADT_baseline AADT_projected SD_AADT_projected PVM_baseline
7786 100 18285 200 0.8
1100 50 1241 100 0.2
24000 1000 37833 2000 0.4
4104 100 21717 1000 0.4