Injury severity analysis of accidents involving young male drivers in Great Britain

Gray, R. C.; Quddus, Mohammed; Evans, Alison

doi:10.1016/j.jsr.2008.07.003

Cited by 117 publications

(65 citation statements)

References 13 publications

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“…However, this finding is not consistent with the finding of Gray et al (2008) who found that crashes in Great Britain are more severe on Fridays, Saturdays and Sundays (relative to Mondays). In order to investigate this, a categorical variable of seven categories representing seven days of the week was also examined (the results are not shown for brevity).…”

Section: Figure 4 Is About Herecontrasting

confidence: 99%

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Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

2010

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Abstract:There is an ongoing debate among transport planners and safety policy makers as to whether there is any association between the level of traffic congestion and road safety. One can expect that the increased level of traffic congestion aids road safety and this is because average traffic speed is relatively low in a congested condition relative to an un-congested condition which may result in less severe crashes. The relationship between congestion and safety may not be so straightforward however as there are a number of other factors such as, traffic flow, driver characteristics, road geometry and vehicle design affecting crash severity. Previous studies have employed count data models (either Poisson or negative binomials and their extensions) while developing a relationship between the frequency of traffic crashes and traffic flow or density (as a proxy for traffic congestion). The use of aggregated crash counts at a road segment level or at an area level with the proxy for congestion may obscure the actual relationship. The objective of this study is to explore the relationship between the severity of road crashes and the level of traffic congestion using disaggregated crash records and a measure of traffic congestion while controlling for other contributory factors. Ordered response models such as ordered logit models, heterogeneous choice models and generalised ordered logit (partially constrained) models suitable for both ordinal dependent variables and disaggregate crash data are used. Data on crashes, traffic characteristics (e.g., congestion, flow, speed) and road geometry (e.g., curvature and gradient) were collected from the M25 London orbital motorway between 2003 and 2006. Our results suggest that the level of traffic congestion does not affect the severity of road crashes on the M25 motorway. The impact of traffic flow on the severity of crashes however shows an interesting result. All other factors included in the models also provide results consistent with existing studies.

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Section: Figure 4 Is About Herecontrasting

confidence: 99%

“…Less severe injury crashes are predicted during darkness. This finding is also not consistent with that of Gray et al (2008) while analysing the crash severity in London.…”

Section: Figure 4 Is About Herecontrasting

confidence: 92%

Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

2010

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“…Gray et al (2008) identified that more severe injuries are predicted during darkness. Abel-Aty (2003) and Helai et al (2008) Table 4) in the model are less important, with percentages between 55.9% and 9.9%.…”

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confidence: 99%

Extracting decision rules from police accident reports through decision trees

Oña

López

Abellán³

2013

Accident Analysis & Prevention

108

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Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build Decision Trees (DTs), which have not been applied to the Road Safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules.

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“…The study found increased risk of fatal injury for older drivers if the point of impact on the vehicle is on the front passenger side or the passenger side behind the driver. Gray et al, (2008) studied the effect of factors determining injury severity for young drivers in London, using an ordered-response model structure. Among other things, the study found inconsistent results in the effect of age using a crash sample only from London and a crash sample from the entire of Great Britain.…”

Section: Injury Severity Studiesmentioning

confidence: 99%

“…Our study indicates that such broad categories may mask variations within finer age groups, and reinforces the notion that the over-representation of 16-17 year old drivers in traffic crashes (see, for example, NHTSA, 2007 andPreusser andLeaf, 2003) is not simply due to lack of experience, but also because of aggressive driving acts. Of course, whether 16-17 year olds drive aggressively because they fundamentally underestimate the risk of being involved in a crash (due to a sense of invincibility from harm or due to optimism bias or simply as a way of insulating themselves from personal concerns; see Jasanoff, 1998;Arnett et al, 2002, McNight andMcNight, 2003), or because of an exaggerated sense of how good their driving skills are (William et al, 1995), or because of peer pressure related to bravado and braggadocio (Gray et al, 2008) is still a very open question for research.…”

Section: Driver Characteristicsmentioning

confidence: 99%

Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes

Paleti

Eluru

Bhat

2010

Accident Analysis & Prevention

210

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Injury severity analysis of accidents involving young male drivers in Great Britain

Cited by 117 publications

References 13 publications

Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

Road Traffic Congestion and Crash Severity: Econometric Analysis Using Ordered Response Models

Extracting decision rules from police accident reports through decision trees

Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes

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