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Reconsidering the Safety in Numbers Effect for Vulnerable Road Users: An Application of Agent-Based Modeling

Overview of attention for article published in Traffic Injury Prevention, October 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

policy
1 policy source
twitter
8 X users
wikipedia
2 Wikipedia pages
reddit
1 Redditor

Readers on

mendeley
85 Mendeley
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Article details
Title
Reconsidering the Safety in Numbers Effect for Vulnerable Road Users: An Application of Agent-Based Modeling
Published in
Traffic Injury Prevention, October 2014
DOI 10.1080/15389588.2014.914626
Pubmed ID
Authors
Abstract

ABSTRACT Objective: Increasing levels of active transport provides benefits in relation to chronic disease and emissions reduction, but it may be associated with an increased risk of road trauma. The safety in numbers (SiN) effect is often regarded as a solution to this issue, however, the mechanisms underlying its influence are largely unknown. We aimed to; i) replicate the SiN effect within a simple, simulated environment, and; ii) vary bicycle density within the environment to better understand the circumstances under which SiN applies. Methods: Using an agent-based modelling approach, we constructed a virtual transport system that increased the number of bicycles from 9% to 35% of total vehicles over a period of 1000 time units while holding the number of cars in the system constant. We then repeated this experiment under conditions of progressively decreasing bicycle density. Results: We demonstrated that the SiN effect can be reproduced in a virtual environment, closely approximating the exponential relationships between cycling numbers and the relative risk of collision as shown in observational studies. The association, however, was highly contingent upon bicycle density. The relative risk of collisions between cars and bicycles with increasing bicycle numbers showed an association that is progressively linear at decreasing levels of density. Conclusions: Agent-based modeling may provide a useful tool for understanding the mechanisms underpinning the relationships previously observed between volume and risk under the assumptions of SiN. The SiN effect may apply only under circumstances in which bicycle density also increases over time. Additional mechanisms underpinning the SiN effect, independent of behavioural adjustment by drivers, are explored.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Australia 2 2%
Unknown 83 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 12 14%
Student > Ph. D. Student 11 13%
Student > Master 10 12%
Student > Doctoral Student 6 7%
Professor > Associate Professor 6 7%
Other 16 19%
Unknown 24 28%
Readers by discipline
Readers by discipline Count As %
Engineering 17 20%
Social Sciences 6 7%
Computer Science 5 6%
Medicine and Dentistry 5 6%
Psychology 4 5%
Other 15 18%
Unknown 33 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 June 2024.
All research outputs
#4,238,920
of 33,779,198 outputs
Outputs from Traffic Injury Prevention
#201
of 1,193 outputs
Outputs of similar age
#39,128
of 296,651 outputs
Outputs of similar age from Traffic Injury Prevention
#5
of 34 outputs
Altmetric has tracked 33,779,198 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,193 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done well, scoring higher than 84% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 296,651 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.