Background

• We need to build dense walking/cycling/wheeling networks
• But where?

Source: Cycle Routing Uptake and Scenario Estimation (CRUSE) tool

Tools of the trade

Modelling framework and long-standing limitations

What is Jittering?

Source: Lovelace, R., Félix, R., & Carlino, D. (2022, January 13). Jittering: A computationally efficient method for generating realistic route networks from origin-destination data. Transport Findings, in Press https://doi.org/10.31219/osf.io/qux6g

Current default: centroid-based desire lines (+routes+rnets)

Jittering in action: minimal reproducible example

Jittering a larger dataset

Adding value and detail to existing OD data. Source: Lovelace, Félix and Carlino (2022)

Resulting route network

Validating the approach: MKI

Data from Edinburgh. Source: GISRUK 2022 conference paper.

Model experiments: jittering parameters

Results From Edinburgh

Changing jittering and routing params

The 'jittering only' approach is assumes perfect routing, not true

Model/data discrepancies may be more due to routing than jittering/OD parameters

Enter Lisbon!

Network level results

Summary of results

Next steps

Exploring the parameter space: different origin and destination points + weights, routing 'engines', disaggregation.

Appendix: Reproducible code I: Rust implementation

See reproducible repo + manuscript here: https://github.com/Robinlovelace/odnet

System command line implementation (compile Rust code):

cargo install --git https://github.com/dabreegster/odjitter
odjitter jitter --od-csv-path od_iz_ed.csv \
  --zones-path iz_zones11_ed.geojson \
  --subpoints-path road_network_ed.geojson \
  --max-per-od 10 --output-path output_max50.geojson

Reproducible code II: R implementation

(See code in slides.Rmd in robinlovelace/foss4g22 for code to get data)

remotes::install_github("dabreegster/odjitter", subdir = "r")

od = read_csv("od_iz_ed.csv")
zones = sf::read_sf("iz_zones11_ed.geojson")

od_jittered = odjitter::jitter(
  od = od,
  zones = zones,
  subpoints = sf::read_sf("road_network_ed.geojson")
  )
od_jittered2 = odjitter::jitter(
  od = od,
  zones = zones,
  subpoints = sf::read_sf("road_network_ed.geojson"),
  disaggregation_key = "all",
  disaggregation_threshold = 10
  )

Results of reprex 1

od_sf = od::od_to_sf(
  od,
  zones
)
nrow(od_sf)

## [1] 10394

nrow(od_jittered)

## [1] 10394

nrow(od_jittered2)

## [1] 22432

Plot of unjittered data

library(dplyr)
plot(od_sf$geometry,
     lwd = od_sf$all / 500)

Results of reprex 2

plot(od_jittered$geometry,
     lwd = od_jittered$all / 500)

Results of reprex 3

plot(od_jittered2$geometry,
     lwd = od_jittered2$all / 50)

Alternative validation datasets: OA-WPZ data

There are 17,848,366 OA to WPZ records, 170k OAs, 54k WPZ

For 5km buffer around London, 1.5 million OD pairs with destinations

Reproducible example

u = "https://github.com/ITSLeeds/od/releases/download/v0.3.1/od_intra_top_sf.geojson"
desire_lines_oa_wpz_1k = sf::read_sf(u)
oas_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/oas_in_buffer.geojson")
wpz_in_buffer = sf::read_sf("https://github.com/ITSLeeds/od/releases/download/v0.3.1/wpz_in_buffer.geojson")
library(tmap)
tmap_mode("view")

## tmap mode set to interactive viewing

m = tm_shape(desire_lines_oa_wpz_1k) +
  tm_lines() +
  tm_shape(oas_in_buffer) + tm_dots(col = "darkgreen") +
  tm_shape(wpz_in_buffer) + tm_dots(col = "darkred")