Jittering: A computationally efficient method for generating realistic route networks from origin-destination data

Lovelace, Robin; Félix, Rosa; Carlino, Dustin

doi:10.31219/osf.io/qux6g

Cited by 3 publications

(7 citation statements)

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“…Building on previous work (Lovelace et al, 2022b), we have explored the relative importance of jittering and routing options for generating accurate route network level estimates of movement, down to the street level. In corroboration with previous research, we found that jittering leads to more spatially diverse geographic representations of travel between zones and estimates of flow down to the link level (Lovelace et al, 2022a).…”

Section: Discussionmentioning

confidence: 99%

“…The overall result was the finding that OD jittering methods first developed by Lovelace et al (2022b) are not enough on their own to generate accurate route networks. Jittering leads to more spatially diffuse route networks than networks generated from the common approach of routing from and to zone centroids.…”

Section: • Estimates Of Quietness Of Links On the Network Computedmentioning

confidence: 99%

“…A common approach to converting OD datasets to geographic entities, for example represented using the simple features standard (Open Geospatial Consortium Inc, 2011) and saved in file formats such as GeoPackage and GeoJSON, is to represent each OD record as a straight line between zone centroids. This approach to representing OD datasets on the map has been since at least the 1950s (Boyce and Williams, 2015) and -despite the development of various methods to add value to OD datasets by * Corresponding author sampling start and end points and 'connectors' withing each zone (Lovelace et al, 2022b), discussed below -centroidbased geographic representations of OD data are still dominant (Rae, 2009;Tennekes and Chen, 2021). Before explaining the methods, it is worth defining terms:…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, methods of 'jittering' OD data have been developed (Lovelace et al, 2022b). While the results from analysis of route networks generated from jittered OD data in that paper were promising, the input datasets were small and technique was not evaluated with reference to ground truth data.…”

Section: Introductionmentioning

confidence: 99%

“…This raised the question "Are the jittered results measurably better when compared with counter datasets on the network?" (Lovelace et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Lovelace

Félix

Carlino

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Origin-Destination (OD) datasets provide vital information on how people travel between areas in many cities, regions and countries worldwide. OD datasets are usually represented geographically with straight lines or routes between zone centroids. For active travel, this geographic representation has substantial limitations, especially when zone origins and centroids are large: only using a single centroid origin/destination for each large zone results in sparse route networks covering only a small fraction of likely walking and cycling routes. This paper implements and explores the use of jittering and different routing options to overcome this limitation, thereby adding value to aggregate OD data to support investment in sustainable transport infrastructure. The route network results — generated from on an open dataset representing cycling trips in Lisbon, Portugal — were compared with a ground-truth dataset from 67 count locations distributed throughout the city. This approach enabled exploration of which jittering parameters and routing options lead to the most accurate route network results approximating the real geographic distribution of cycling trips in the study area. We found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. We conclude that a combined approach involving 1) jittering with intermediate levels of disaggregation and 2) careful selection of routing options can lead to much more realistic route networks than using established OD processing techniques. The methods can be deployed to support evidence-based investment in strategic cycling and other sustainable transport networks in cities worldwide.

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Section: Discussionmentioning

confidence: 99%

Section: • Estimates Of Quietness Of Links On the Network Computedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This raised the question "Are the jittered results measurably better when compared with counter datasets on the network?" (Lovelace et al, 2022b).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Lovelace

Félix

Carlino

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation

Bilal,

Zaatour,

Otano

et al. 2023

Preprint

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The COVID-19 pandemic has dramatically highlighted the importance of developing simulation systems for quickly characterizing and providing spatio-temporal forecasts of infection spread dynamics that take specific accounts of the population and spatial heterogeneities that govern pathogen transmission in real-world communities. Developing such computational systems must also overcome the cold start problem related to the inevitable scarce early data and extant knowledge regarding a novel pathogen’s transmissibility and virulence, while addressing changing population behavior and policy options as a pandemic evolves. Here, we describe how we have coupled advances in the construction of digital or virtual models of real-world cities with an agile, modular, agent-based model of viral transmission and data from navigation and social media interactions, to overcome these challenges in order to provide a new simulation tool, CitySEIRCast, that can model viral spread at the sub-national level. Our data pipelines and workflows are designed purposefully to be flexible and scalable so that we can implement the system on hybrid cloud/cluster systems and be agile enough to address different population settings and indeed, diseases. Our simulation results demonstrate that CitySEIRCast can provide the timely high resolution spatio-temporal epidemic predictions required for supporting situational awareness of the state of a pandemic as well as for facilitating assessments of vulnerable sub-populations and locations and evaluations of the impacts of implemented interventions, inclusive of the effects of population behavioral response to fluctuations in case incidence. This work arose in response to requests from county agencies to support their work on COVID-19 monitoring, risk assessment, and planning, and using the described workflows, we were able to provide uninterrupted bi-weekly simulations to guide their efforts for over a year from late 2021 to 2023. We discuss future work that can significantly improve the scalability and real-time application of this digital city-based epidemic modelling system, such that validated predictions and forecasts of the paths that may followed by a contagion both over time and space can be used to anticipate the spread dynamics, risky groups and regions, and options for responding effectively to a complex epidemic.INDEX TERMSAgent-based Modeling, city-scale digital twins, disease transmission, epidemiology, geospatial modeling, healthcare interventions, lockdowns, microsimulation, model validation, synthetic populations, risk groups, vaccinations, visual analytics.

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Exploring jittering and routing options for converting origin-destination data into route networks: towards accurate estimates of movement at the street level

Lovelace¹,

Félix²,

Carlino³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Origin-Destination (OD) datasets provide vital information on how people travel between areas in many cities, regions and countries worldwide. OD datasets are usually represented geographically with straight lines or routes between zone centroids. For active travel, this geographic representation has substantial limitations, especially when zone origins and centroids are large: only using a single centroid origin/destination for each large zone results in sparse route networks covering only a small fraction of likely walking and cycling routes. This paper implements and explores the use of jittering and different routing options to overcome this limitation, thereby adding value to aggregate OD data to support investment in sustainable transport infrastructure. The route network results --- generated from on an open dataset representing cycling trips in Lisbon, Portugal --- were compared with a ground-truth dataset from 67 count locations distributed throughout the city. This approach enabled exploration of which jittering parameters and routing options lead to the most accurate route network results approximating the real geographic distribution of cycling trips in the study area. We found that jittering and disaggregating OD data, combined with routing using low level of traffic stress (quieter) preferences resulted in the most accurate route networks. We conclude that a combined approach involving 1) jittering with intermediate levels of disaggregation and 2) careful selection of routing options can lead to much more realistic route networks than using established OD processing techniques. The methods can be deployed to support evidence-based investment in strategic cycling and other sustainable transport networks in cities worldwide.

show abstract

Jittering: A computationally efficient method for generating realistic route networks from origin-destination data

Cited by 3 publications

References 9 publications

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

Exploring Jittering and Routing Options for Converting Origin-Destination Data Into Route Networks: Towards Accurate Estimates of Movement at the Street Level

CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation

Exploring jittering and routing options for converting origin-destination data into route networks: towards accurate estimates of movement at the street level

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