The goal of cyclability is to generate estimates of broadly defined cyclability. There are at least three definitions of how conducive to cycling different places and segments of roads and travel networks are:
- Level of Traffic Stress (LTS)
- Bikeability
levels, which rates infrastructure based on the level of training
needed to feel comfortable:
- Level 1 teaches basic bike-handling skills in a controlled traffic-free environment.
- Level 2 teaches trainees to cycle planned routes on minor roads, offering a real cycling experience.
- Level 3 ensures trainees are able to manage a variety of traffic conditions and is delivered on busier roads with advanced features and layouts
- CycleStreets’s Quietness rating from 1 (very unpleasant for cycling) to 100 (the quietest)
For this practical we’ll be using a number of R packages, which can be installed as follows:
# core packages:
install.packages("remotes")
core_packages = c(
"tidyverse",
"sf",
"tmap"
)
remotes::install_cran(core_packages)Other packages that we use which you may want to install include:
other_packages = c(
"r5r"
)
remotes::install_cran(other_packages)
#> Installing 1 packages: r5r
#> Installing package into '/home/rosa/R/x86_64-pc-linux-gnu-library/4.1'
#> (as 'lib' is unspecified)
remotes::install_github("cyclestreets/cyclestreets-r")
#> Skipping install of 'cyclestreets' from a github remote, the SHA1 (68b345cd) has not changed since last install.
#> Use `force = TRUE` to force installationFor the practical we assume you have the following packages loaded:
library(tidyverse)
library(sf)To get going with the data a good starting point is to download the the data in this repo. Do this by clicking on https://github.com/ITSLeeds/cyclability/archive/refs/heads/main.zip and then download and unzip the folder. Assuming you have unzipped the folder in your Downloads folder you should be able to open the project with the following command in RStudio:
rstudioapi::openProject("~/Downloads/cyclability-main/")After you have opened the project you should be able to run the following commands. Try reproducing the next code chunk to generate a plot to see if it works. If not you can open an issue at https://github.com/ITSLeeds/cyclability/issues
-
15 minutes: get up-to-speed with best practices on data science projects including using software for reproducible and collaborative research such as Git and RStudio.
-
15 minutes Download and reproduce a reproducible script that estimates active travel friendliness based on open access OSM data.
Hack topics:
- Compare CycleStreets vs R5 estimates of cyclability
- Testing and developing methods for geographic joins between route network datasets
- Identifying issues with quietness estimates based on real data data
(no code option)
- Think of a route you know well and calculate a route along it with CycleStreets: https://www.cyclestreets.net/
- Issues with the route quietness estimation based on real world knowledge
- ITS Leeds website improvements: https://www.cyclestreets.net/
- Any others Work to improve the script
To get estimates of LTS on the network we used the R5 routing engine via
the r5r package. See code/r5r_setup.R for details.
The resulting estimates of LTS in Leeds can be read-in and visualised as follows:
leeds_lts = readRDS("r5r/r5r_lts_osmtags.Rds")
leeds_lts
#> Simple feature collection with 10134 features and 19 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: -1.575111 ymin: 53.79672 xmax: -1.53612 ymax: 53.81871
#> Geodetic CRS: WGS 84
#> First 10 features:
#> edge_index osm_id from_vertex to_vertex street_class length walk car
#> 1 0 1709456 0 1 TERTIARY 14.750 TRUE TRUE
#> 2 1 1709456 1 0 TERTIARY 14.750 TRUE TRUE
#> 3 2 1709456 1 2 TERTIARY 44.502 TRUE TRUE
#> 4 3 1709456 2 1 TERTIARY 44.502 TRUE TRUE
#> 5 4 1709456 2 3 TERTIARY 19.366 TRUE TRUE
#> 6 5 1709456 3 2 TERTIARY 19.366 TRUE TRUE
#> 7 6 1709456 3 4 TERTIARY 45.043 TRUE TRUE
#> 8 7 1709456 4 3 TERTIARY 45.043 TRUE TRUE
#> 9 8 1709460 5 6 OTHER 69.640 TRUE TRUE
#> 10 9 1709460 6 5 OTHER 69.640 TRUE TRUE
#> car_speed bicycle bicycle_lts name highway waterway aerialway
#> 1 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 2 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 3 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 4 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 5 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 6 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 7 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 8 48.276 TRUE 3 Hanover Way tertiary <NA> <NA>
#> 9 32.184 TRUE 3 Hyde Place residential <NA> <NA>
#> 10 32.184 TRUE 3 Hyde Place residential <NA> <NA>
#> barrier man_made z_order other_tags
#> 1 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 2 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 3 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 4 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 5 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 6 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 7 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 8 <NA> <NA> 4 "lit"=>"yes","maxspeed"=>"30 mph"
#> 9 <NA> <NA> 3 "maxspeed"=>"20 mph"
#> 10 <NA> <NA> 3 "maxspeed"=>"20 mph"
#> geometry
#> 1 LINESTRING (-1.55754 53.799...
#> 2 LINESTRING (-1.557522 53.80...
#> 3 LINESTRING (-1.557522 53.80...
#> 4 LINESTRING (-1.557495 53.80...
#> 5 LINESTRING (-1.557495 53.80...
#> 6 LINESTRING (-1.557487 53.80...
#> 7 LINESTRING (-1.557487 53.80...
#> 8 LINESTRING (-1.557453 53.80...
#> 9 LINESTRING (-1.558721 53.80...
#> 10 LINESTRING (-1.558404 53.80...
leeds_lts |>
select(bicycle_lts) |>
plot()
leeds_lts |>
select(bicycle_lts) |>
mapview::mapview(zcol = "bicycle_lts")To get estimates of quietness on the network we used cyclestreets
package. See code/cyclestreets_setup.R for details.
The resulting estimates of LTS in Leeds can be read-in and visualised as follows:
leeds_quietness = sf::read_sf("cyclestreets/leeds_quietness.geojson")
leeds_quietness
#> Simple feature collection with 2745 features and 9 fields
#> Geometry type: LINESTRING
#> Dimension: XY
#> Bounding box: xmin: -1.58941 ymin: 53.79281 xmax: -1.53612 ymax: 53.82387
#> Geodetic CRS: WGS 84
#> # A tibble: 2,745 × 10
#> name ridin…¹ id cycla…² quiet…³ speed…⁴ speed…⁵ pause color
#> <chr> <chr> <int> <chr> <int> <int> <int> <int> <chr>
#> 1 Hanover Way Minor … 1.71e6 Yes 40 16 26 0 #929…
#> 2 Hyde Place Reside… 1.71e6 Yes 60 15 24 0 #B06…
#> 3 Buckingham Avenue Reside… 2.96e6 Yes 40 15 24 0 #929…
#> 4 Back Buckingham M… Servic… 2.96e6 Yes 40 12 20 0 #929…
#> 5 Back Midland Road Reside… 3.98e6 Yes 60 15 24 0 #B06…
#> 6 Midland Passage Servic… 3.98e6 Yes 60 12 20 0 #B06…
#> 7 Ebberston Place Reside… 3.98e6 Yes 60 15 24 0 #B06…
#> 8 Chestnut Place Reside… 3.98e6 Yes 50 7 12 0 #7E9…
#> 9 Chestnut Place Reside… 3.98e6 Yes 50 7 12 0 #7E9…
#> 10 Welton Mount Reside… 3.98e6 Yes 60 15 24 0 #B06…
#> # … with 2,735 more rows, 1 more variable: geometry <LINESTRING [°]>, and
#> # abbreviated variable names ¹ridingSurface, ²cyclableText, ³quietness,
#> # ⁴speedMph, ⁵speedKmph
leeds_quietness |>
select(quietness) |>
plot()
leeds_quietness |>
select(quietness) |>
mapview::mapview(zcol = "quietness")# task for the hackathonThe purpose of the hackathon!