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A random parameters probit model of urban and rural intersection crashes

Overview of attention for article published in Accident Analysis & Prevention, November 2015
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1 tweeter

Citations

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Readers on

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54 Mendeley
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Title
A random parameters probit model of urban and rural intersection crashes
Published in
Accident Analysis & Prevention, November 2015
DOI 10.1016/j.aap.2015.07.013
Pubmed ID
Authors

Richard Tay

Abstract

Intersections are hazardous locations and many studies have been conducted to identify the factors contributing to the frequency and severity of intersection crashes. However, little attention has been devoted to investigating the differences between crashes at urban and rural intersections, which have different road, traffic and environmental characteristics. By applying a random parameters probit model to the data from the Canadian Province of Alberta between 2008 and 2012, we find that urban intersection crashes are more likely to be associated with hit and run behaviours, roads with higher traffic volume, wet surfaces, four lanes and skewed intersections, and crashes on weekdays and off-peak hours, whereas rural crashes are likely to be associated with increases in fatalities and injuries, roads with higher speed limits, special road features, exit and entrance terminals, gravel, curvature and two lanes, crashes during weekends, peak hours and night-time, run-off-road crashes, and police visit to crash scene. Hence, road safety professionals in urban and rural areas should consider these differences when designing and implementing counter-measures to improve intersection safety, especially their safety audits and reviews, enforcement activities and education campaigns, to target the more vulnerable times and locations in the different areas.

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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 53 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Student > Master 9 17%
Researcher 7 13%
Student > Doctoral Student 4 7%
Lecturer 4 7%
Other 11 20%
Unknown 5 9%
Readers by discipline Count As %
Engineering 36 67%
Business, Management and Accounting 3 6%
Social Sciences 2 4%
Psychology 2 4%
Economics, Econometrics and Finance 1 2%
Other 4 7%
Unknown 6 11%

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 26 May 2016.
All research outputs
#10,875,690
of 12,271,314 outputs
Outputs from Accident Analysis & Prevention
#2,234
of 3,028 outputs
Outputs of similar age
#200,746
of 243,834 outputs
Outputs of similar age from Accident Analysis & Prevention
#51
of 91 outputs
Altmetric has tracked 12,271,314 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 3,028 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 91 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.