Motor Vehicle Collisions and Their Demographics: A 5‐Year Retrospective Study of the Hamilton‐Wentworth Niagara Region*

Lemieux, Carolyne E.; Fernandes, John; Rao, C J

doi:10.1111/j.1556-4029.2008.00723.x

Cited by 8 publications

(10 citation statements)

References 47 publications

(75 reference statements)

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“…The lack of association between day of the week and fatal collisions on Ontario’s highways is not consistent with previous research by Lemieux who reported that MVCs in the Hamilton-Wentworth Niagara Region of Ontario that resulted in fatalities occurred during Fridays and Sundays, on city and rural roads as well as highways [12]. Other studies compared fatality rates in different days of the week; however, they compared weekdays vs. weekend days rather than each day individually [5,17,22].…”

Section: Discussioncontrasting

confidence: 55%

“…Further, Turner and McClure revealed that men scored significantly higher than females in the ‘Driver Aggression’ and ‘Risk Acceptance’ scales [19]. Lemieux reported an increased rate of fatalities amongst male drivers compared to females [12]. However, this finding is not completely consistent.…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, Singleton et al found that females were 60% more likely than males to be seriously injured in MVCs, and Bedard et al explained that females in the United States were 54% more likely to be involved in a fatal accident than their male counterparts [10,11]. Moreover, Lemieux found that when looking at MVCs in the Hamilton region of Ontario, weather did not play a significant role in the collisions that resulted in one or more fatalities [12]. …”

Section: Introductionmentioning

confidence: 99%

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Risk of death in crashes on Ontario’s highways

2012

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BackgroundMotor vehicle collisions (MVCs) that result in one or more fatalities on the 400-series Highways represent a serious public health problem in Ontario, and were estimated to have cost $11 billion in 2004. To date, no studies have examined risk factors for fatal MVCs on Ontario’s 400 series highways.The investigate how demographic and environmental risk factors are associated with fatal MVCs on Ontario’s 400-Series Highways.MethodsData were provided from the Ontario Ministry of Transport database, and included driver demographics, vehicle information, environmental descriptors, structural descriptors, as well as collision information (date and time), and severity of the collision. Multivariate analysis was used to identify factors significantly associated with the odds of dying in a collision.ResultsThere were 53,526 vehicles involved in collisions from 2001 to 2006 included in our analysis. Results from the multivariate analysis suggest that collisions with older age and male drivers were associated with an increased risk of involving a fatality. Highway 405 and an undivided 2-way design proved to be the most fatal structural configurations. Collisions in the summer, Fridays, between 12 am-4 am, and in drifting snow conditions during the wintertime were also shown to have a significantly increased risk of fatality.ConclusionOur results suggest that interventions to reduce deaths as a result of MVCs should focus on both driver-related and road-related modifications.

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Section: Discussioncontrasting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk of death in crashes on Ontario’s highways

2012

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“…This crash type is understood to become less frequent with age relative to minor crashes. Certainly younger drivers tend to have more severe crashes than older drivers [ 159 , 160 ]. Accordingly, the association between severe crashes and traffic offences could diminish as they become relatively less common.…”

Section: Discussionmentioning

confidence: 99%

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data

Barraclough

Wåhlberg²,

Freeman

et al. 2016

PLoS ONE

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BackgroundTraffic offences have been considered an important predictor of crash involvement, and have often been used as a proxy safety variable for crashes. However the association between crashes and offences has never been meta-analysed and the population effect size never established. Research is yet to determine the extent to which this relationship may be spuriously inflated through systematic measurement error, with obvious implications for researchers endeavouring to accurately identify salient factors predictive of crashes.Methodology and Principal FindingsStudies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. Potential impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were considered and discussed. After weighting for sample size, an average correlation of r = .18 was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. Consistent with common method variance effects, a within country analysis found stronger effect sizes in self-reported data even controlling for crash mean.SignificanceThe effectiveness of traffic offences as a proxy for crashes may be limited. Inclusion of elements such as independently validated crash and offence histories or accurate measures of exposure to the road would facilitate a better understanding of the factors that influence crash involvement.

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“…This crash type is understood to become less frequent with age (and driving experience) relative to minor crashes. Certainly younger drivers tend to have more severe crashes than older drivers (Blows, Ameratunga, Ivers, Lo, & Norton, 2005;Lemieux, Fernandes, & Rao, 2008). Accordingly, the association between severe crashes and traffic offences could diminish as they become relatively less common.…”

Section: The Relationship Between Crashes and Traffic Offencesmentioning

confidence: 99%

Common method variance and other sources of bias in road traffic research

Barraclough¹

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Common Method Variance and other sources of bias in road traffic research i KeywordsRoad crashes, traffic offences, common method variance, social desirability, method bias, meta-analysis, self-report data, road safety. AbstractReducing the number of fatalities and injuries due to vehicle crashes is a major focus for road safety researchers throughout the world. Research on driver behaviours, attitudes and associated crashes is often conducted using self-report surveys. When the predictor and criterion variables are drawn from a single source (such as a cross-sectional survey) as is generally the case, it has been generally acknowledged that a range of methodological issues associated with this approach have the potential to reduce the efficacy of predictive measures. Common method variance (CMV) is bias that occurs when both the predictors and the predicted variables are gathered and analysed in the same manner from the same source. CMV effects have the potential to distort findings by artificially strengthening or weakening the observed relationships between items of interest. This is of particular concern to road safety researchers, given the distinct possibility that the small associations often observed within road safety studies may in turn be inflated due to CMV biases. There is a significant need to examine the exact nature and extent of CMV effects within self-reported road safety data and to explore how best to reduce this measurement error.The current body of research was conducted with three primary aims in mind:1. Examine the existence of CMV effects.2. Explore the level of influence that CMV effects have on self-report data, including whether these effects inflate or deflate observed relationships between variable.3. Test research methods to control for the influence of CMV effects on selfreport data.Study 1 involved a meta-analysis that quantified the association between crashes and traffic offences in light of potential moderators. In reference to the exploration for CMV effects, this study examined the extent to which the relationship between self-reported traffic offences and self-reported crashes is stronger than that recorded using only official data sources. Studies yielding a correlation between crashes and traffic offences were collated and a meta-analysis of 144 effects drawn from 99 road safety studies conducted. The impact of factors such as age, time period, crash and offence rates, crash severity and data type, sourced from either self-report surveys or archival records, were examined. After weighting for sample size, an average correlation of r = .18 between crashes and traffic offences was observed over the mean time period of 3.2 years. Evidence emerged suggesting the strength of this correlation is decreasing over time. Stronger correlations between crashes and offences were generally found in studies involving younger drivers. In relation to the existence of CMV effects, a within-country analysis found stronger effect sizes in self-reported data after controlling for crash mean ...

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Motor Vehicle Collisions and Their Demographics: A 5‐Year Retrospective Study of the Hamilton‐Wentworth Niagara Region*

Cited by 8 publications

References 47 publications

Risk of death in crashes on Ontario’s highways

Risk of death in crashes on Ontario’s highways

Predicting Crashes Using Traffic Offences. A Meta-Analysis that Examines Potential Bias between Self-Report and Archival Data

Common method variance and other sources of bias in road traffic research

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