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Assessing methods for generating route networks from origin-destionation data

Jittering, routing, and visualisation

GISRUK 2022, Liverpool

Robin Lovelace, University of Leeds
+Rosa Félix, Dustin Carlino, Roger Beecham

2022-4-06

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Background

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Tools of the trade

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Tools of the trade

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  • Turing Fellowship
  • LIDA internship on open transport infrastructure data
  • Links with DfT, MHCLG, TfNH, international partners

Tools of the trade

  • Evidence-based policies in government: Data Science Fellowship at N. 10

  • Future areas of development: Reproducible Bayesian modelling of proportions (Dirichlet regression), Machine Learning, Decarbonisation Agenda

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  • Turing Fellowship
  • LIDA internship on open transport infrastructure data
  • Links with DfT, MHCLG, TfNH, international partners

Modelling framework

Modular

Future proof

Scalable

Vector/

Raster/

Source: Morgan and Lovelace (2020) Implementation: stplanr

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Modelling framework

Modular

Future proof

Scalable

Vector/

Raster/

Source: Morgan and Lovelace (2020) Implementation: stplanr

Approach: OD -> Desire Line -> Route -> Route Networks

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I am an all-round data scientist with experience with Python, Julia, and command line tools such as Docker and shell scripting for scalable data science applications.

I have particular expertise in R and geocomputation with R in particular.

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

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

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Jittering in action: minimal reproducible example

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Jittering a larger dataset

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

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Resulting route network

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Validating the approach: this paper!

Problem: we need a validation dataset

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Validating the approach: this paper!

Problem: we need a validation dataset

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Model experiments: jittering parameters

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Results!

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Code!

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

R implementation

remotes::install_github("dabreegster/odjitter", subdir = "r")
od = read_csv("od_iz_ed.csv")
od_jittered = odjitter::jitter(
  od = od,
  zones = sf::read_sf("iz_zones11_ed.geojson"),
  subpoints = sf::read_sf("road_network_ed.geojson")
  )
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Next steps

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

Selection of input data: open options

  • Traffic count data
  • Urban Observatory type data (Newcastle, Birmingham, Manchester)
  • Faceboook and Google open mobility data
  • 'OSM2od' - spatial interaction model
  • Modelled data
    • jittering: spatial disaggregation
    • temporal disaggregation

Non-open data data

  • National Travel Survey
  • Mobile Telephone Data
  • Large GPS type data (biobank, Google timeline, Straval)
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Discussion: scalability vs resolution

Source: UKRI CREDS project repo

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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

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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")

See here for map

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From OD data to policy tools

Lead Developer of the DfT's PCT (Lovelace et al. 2017) : transformational impact on planning in the UK (source: REF Impact Case Study)

COVID response: RAPID tool (Lovelace et al. 2020)

ActDev tool for informing planning process

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Open source software, community building

  • New high performance libraries

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Making UK data come to life

Source: Lovelace, Tennekes, Carlino (under review)

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

References

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

References

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. https://doi.org/10.5198/jtlu.2016.862

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

References

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. https://doi.org/10.5198/jtlu.2016.862

Morgan, M., Lovelace, R., 2020. Travel flow aggregation: nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment & Planning B: Planning & Design. https://doi.org/10.1177/2399808320942779

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

References

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. https://doi.org/10.5198/jtlu.2016.862

Morgan, M., Lovelace, R., 2020. Travel flow aggregation: nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment & Planning B: Planning & Design. https://doi.org/10.1177/2399808320942779

Lovelace, R., Tennekes, M., Carlino, D., 2021. ClockBoard: a zoning system for urban analysis. https://doi.org/10.31219/osf.io/vncgw

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Thanks, look forward to working with you 🖧 + 📈 + ✨ = 🚀!

References

Lovelace, R., Goodman, A., Aldred, R., Berkoff, N., Abbas, A., Woodcock, J., 2017. The Propensity to Cycle Tool: An open source online system for sustainable transport planning. Journal of Transport and Land Use 10. https://doi.org/10.5198/jtlu.2016.862

Morgan, M., Lovelace, R., 2020. Travel flow aggregation: nationally scalable methods for interactive and online visualisation of transport behaviour at the road network level. Environment & Planning B: Planning & Design. https://doi.org/10.1177/2399808320942779

Lovelace, R., Tennekes, M., Carlino, D., 2021. ClockBoard: a zoning system for urban analysis. https://doi.org/10.31219/osf.io/vncgw

Lovelace, Robin, Rosa Félix, and Dustin Carlino. “Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data.” OSF Preprints, January 13, 2022. https://doi.org/10.31219/osf.io/qux6g.

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