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Modeling offenses among motorcyclists involved in crashes in Spain

Overview of attention for article published in Accident Analysis & Prevention, July 2013
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1 tweeter

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Title
Modeling offenses among motorcyclists involved in crashes in Spain
Published in
Accident Analysis & Prevention, July 2013
DOI 10.1016/j.aap.2013.03.014
Pubmed ID
Authors

Patricia Perez-Fuster, Maria F. Rodrigo, Maria Luisa Ballestar, Jaime Sanmartin

Abstract

In relative terms, Spanish motorcyclists are more likely to be involved in crashes than other drivers and this tendency is constantly increasing. The objective of this study is to identify the factors that are related to being an offender in motorcycle accidents. A binary logit model is used to differentiate between offender and non-offender motorcyclists. A motorcyclist was considered to be offender when s/he had committed at least one traffic offense at the moment previous to the crash. The analysis is based on the official accident database of the Spanish general directorate of traffic (DGT) for the 2003-2008 time period. A number of explanatory variables including motorcyclist characteristics and environmental factors have been evaluated. The results suggest that inexperienced, older females, not using helmets, absent-minded and non-fatigued riders are more likely to be offenders. Moreover, riding during the night, on weekends, for leisure purposes and along roads in perfect condition, mainly on curves, predict offenses among motorcyclists. The findings of this study are expected to be useful in developing traffic policy decisions in order to improve motorcyclist safety.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
United States 1 2%
Unknown 39 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 27%
Student > Master 6 15%
Professor 4 10%
Unspecified 4 10%
Student > Ph. D. Student 3 7%
Other 13 32%
Readers by discipline Count As %
Engineering 18 44%
Unspecified 6 15%
Psychology 6 15%
Social Sciences 3 7%
Medicine and Dentistry 2 5%
Other 6 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 20 October 2013.
All research outputs
#10,719,853
of 12,088,747 outputs
Outputs from Accident Analysis & Prevention
#2,208
of 3,008 outputs
Outputs of similar age
#109,766
of 131,770 outputs
Outputs of similar age from Accident Analysis & Prevention
#32
of 62 outputs
Altmetric has tracked 12,088,747 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,008 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 131,770 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.