Validation Phase II

[Robinlovelace]

Validation of Phase 2 outputs by comparison to counter data.

[joeytalbot]

The first task as part of the validation process is to tackle utility trips #354

[Robinlovelace]

Heads-up @joeytalbot just looking at this now with Angus and it seems that the uptake numbers on the utility trips for Go Dutch are inconsistently high compared with for commute trips. Illustration, showing 30x fold increase for utility vs more like 6x increase for commuting.

That's Dalry road: https://nptscot.github.io/#/rnet/#16.83/55.940968/-3.221568

[Robinlovelace]

Some debugging of this:

``` r library(sf) ``` Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE ``` r library(tidyverse) ``` ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.1.4 ✔ readr 2.1.5 ✔ forcats 1.0.0 ✔ stringr 1.5.1 ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ✔ lubridate 1.9.3 ✔ tidyr 1.3.1 ✔ purrr 1.0.2 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ purrr::%||%() masks base::%||%() ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() ℹ Use the conflicted package () to force all conflicts to become errors ``` r library(targets) ``` ``` r setwd("../..") tar_load(parameters) tar_load(uptake_utility_fastest) tar_load(uptake_commute_fastest) names(uptake_utility_fastest) ``` [1] "route_number" "distances" [3] "quietness" "gradient_smooth" [5] "geo_code1" "geo_code2" [7] "car" "foot" [9] "bicycle" "all" [11] "dist_euclidean" "public_transport" [13] "taxi" "dist_euclidean_jittered" [15] "purpose" "endDZ" [17] "startDZ" "geometry" [19] "route_hilliness" "length_route" [21] "pcycle_go_dutch" "pcycle_ebike" [23] "bicycle_go_dutch" "bicycle_ebike" [25] "mode_ratio_baseline" "mode_ratio_go_dutch" [27] "mode_ratio_ebike" "car_go_dutch" [29] "public_transport_go_dutch" "foot_go_dutch" [31] "taxi_go_dutch" "car_ebike" [33] "public_transport_ebike" "foot_ebike" [35] "taxi_ebike" ``` r # [1] "route_number" "distances" # [3] "quietness" "gradient_smooth" # [5] "geo_code1" "geo_code2" # [7] "car" "foot" # [9] "bicycle" "all" # [11] "dist_euclidean" "public_transport" # [13] "taxi" "dist_euclidean_jittered" # [15] "purpose" "endDZ" # [17] "startDZ" "geometry" # [19] "route_hilliness" "length_route" # [21] "pcycle_go_dutch" "pcycle_ebike" # [23] "bicycle_go_dutch" "bicycle_ebike" # [25] "mode_ratio_baseline" "mode_ratio_go_dutch" # [27] "mode_ratio_ebike" "car_go_dutch" # [29] "public_transport_go_dutch" "foot_go_dutch" # [31] "taxi_go_dutch" "car_ebike" # [33] "public_transport_ebike" "foot_ebike" # [35] "taxi_ebike" head(uptake_utility_fastest) ``` Simple feature collection with 6 features and 34 fields Geometry type: LINESTRING Dimension: XY Bounding box: xmin: -4.18703 ymin: 55.75881 xmax: -4.16931 ymax: 55.76907 Geodetic CRS: WGS 84 # A tibble: 6 × 35 # Groups: route_number [6] route_number distances quietness gradient_smooth geo_code1 geo_code2 car 1 1 2046 32 0.00831 S02002422 65013 65.6 2 2 1425 28 0.0119 S02002422 65013 65.6 3 3 1502 29 0.0120 S02002422 65013 65.6 4 4 1540 27 0.0110 S02002422 65013 65.6 5 5 1604 31 0.0106 S02002422 65013 65.6 6 6 1565 30 0.0102 S02002422 65013 65.6 # ℹ 28 more variables: foot , bicycle , all , # dist_euclidean , public_transport , taxi , # dist_euclidean_jittered , purpose , endDZ , startDZ , # geometry , route_hilliness , length_route , # pcycle_go_dutch , pcycle_ebike , bicycle_go_dutch , # bicycle_ebike , mode_ratio_baseline , mode_ratio_go_dutch , # mode_ratio_ebike , car_go_dutch , … ``` r edinburgh = zonebuilder::zb_zone("edinburgh") ``` Loading required namespace: tmaptools ``` r edinburgh_1km = edinburgh |> slice(1) uptake_sample = uptake_utility_fastest[edinburgh_1km,] uptake_sample |> select(1) |> plot() ```  ``` r uptake_1 = uptake_sample |> ungroup() |> slice(1) plot(uptake_1) ``` Warning: plotting the first 9 out of 34 attributes; use max.plot = 34 to plot all  ``` r sf::st_length(uptake_1) ``` 3690.236 [m] ``` r summary(sf::st_length(uptake_sample)) ``` Min. 1st Qu. Median Mean 3rd Qu. Max. 651.6 3245.7 4350.3 4195.9 5286.1 7893.4 ``` r # for all utility routes: uptake_utility_fastest |> st_length() |> summary() ``` Min. 1st Qu. Median Mean 3rd Qu. Max. 438.4 2736.8 4148.1 4073.0 5365.3 10008.4 ``` r pcycle_utility = pct::uptake_pct_godutch_2020( distance = sf::st_length(uptake_utility_fastest) |> as.numeric(), gradient = uptake_utility_fastest$route_hilliness ) summary(pcycle_utility) ``` Min. 1st Qu. Median Mean 3rd Qu. Max. 0.01483 0.35382 0.40619 0.40013 0.45353 0.55865 Same for all commutes: ``` r uptake_commute_fastest |> st_length() |> summary() ``` Min. 1st Qu. Median Mean 3rd Qu. Max. 1020 4633 8168 9006 12747 82859 ``` r pcycle_commute = pct::uptake_pct_godutch_2020( distance = sf::st_length(uptake_commute_fastest) |> as.numeric(), gradient = uptake_commute_fastest$route_hilliness ) summary(pcycle_commute) ``` Min. 1st Qu. Median Mean 3rd Qu. Max. 0.01229 0.11610 0.25601 0.25650 0.38838 0.55861