Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data

Robin Lovelace, Rosa Félix, and Dustin Carlino (2022). Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data. Findings. https://doi.org/10.32866/001c.33873

Abstract

Origin-destination (OD) datasets are often represented as “desire lines” between zone centroids. This paper presents a “jittering” approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits “large” OD pairs into “sub-OD” pairs. Reproducible findings, based on the open source odjitter Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by “unjittered” data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.

Type: Journal Article Venue: Findings Year: 2022

DOI BibTeX

Abstract

Origin-destination (OD) datasets are often represented as “desire lines” between zone centroids. This paper presents a “jittering” approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits “large” OD pairs into “sub-OD” pairs. Reproducible findings, based on the open source odjitter Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by “unjittered” data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.

Citation

Robin Lovelace, Rosa Félix, and Dustin Carlino (2022). Jittering: A Computationally Efficient Method for Generating Realistic Route Networks from Origin-Destination Data. Findings. https://doi.org/10.32866/001c.33873

BibTeX

@article{lovelace_jittering_2022b,
  title = {Jittering: {{A Computationally Efficient Method}} for {{Generating Realistic Route Networks}} from {{Origin-Destination Data}}},
  shorttitle = {Jittering},
  author = {Lovelace, Robin and F{\'e}lix, Rosa and Carlino, Dustin},
  year = {2022},
  month = apr,
  journal = {Findings},
  pages = {33873},
  publisher = {{Findings Press}},
  doi = {10.32866/001c.33873},
  urldate = {2022-05-05},
  abstract = {Origin-destination (OD) datasets are often represented as `desire lines' between zone centroids. This paper presents a `jittering' approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits `large' OD pairs into `sub-OD' pairs. Reproducible findings, based on the open source \_odjitter\_ Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by `unjittered' data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.},
  copyright = {Creative Commons Attribution-ShareAlike 4.0 International Licence (CC-BY-SA)},
  langid = {english},
  file = {/home/robin/Zotero/storage/MW7B8GVG/Lovelace et al. - 2022 - Jittering A Computationally Efficient Method for .pdf;/home/robin/Zotero/storage/QZU3J666/33873-jittering-a-computationally-efficient-method-for-generating-realistic-route-networks-from.html}
}

Notes