Seasonal Variation in Frequencies and Rates of Highway Accidents as Function of Severity

Brown, B. M.; Baass, K G

doi:10.3141/1581-08

Cited by 33 publications

(9 citation statements)

References 3 publications

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“…Conversely, the least affecting condition found was visibility, which is consistent with the normal and lognormal PDF analysis. This finding is in line with previous studies, where temperature has found to be significant on road accidents, especially in combination with humidity or rain (Branas and Knudson 2001;Brown and Baass 1997;Fridstrom et al 1995;Fridstrom and Ingebrigtsen 1991). The data reported in Figs.…”

Section: For the Winter Seasonsupporting

confidence: 95%

Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Al‐Harbi

Yassin

Shams

2012

Stoch Environ Res Risk Assess

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Meteorological conditions have become one of the major factors that influence the frequency and severity of motor vehicle collisions in urban environments. In Kuwait, more than 60,000 accidents occur each year, and about 500 people are killed annually on the roads. This paper is intended to investigate the impact of meteorological conditions on traffic accidents in Kuwait. Stochastic models are developed to analyze and examine the influence of meteorological conditions on the level of road accidents. Normal and lognormal probability densities and their associated cumulative density functions are used to model the meteorological conditions in four different seasons. The results indicate that the most influential meteorological condition that causes accidents is temperature during the fall, spring, and winter seasons. In the summer, wind speed is identified as the most influential factor that accounts for the increased road accidents, with temperature as the second highest meteorological condition affecting accidents. Wind speed and humidity are also found to have significant influence on accident level, following temperature in the fall and winter seasons, respectively. Correlation analyses were also applied and supported the findings obtained using stochastic analyses. The results of this study may help local authorities to reduce the number of accidents and help save people lives.

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Section: For the Winter Seasonsupporting

confidence: 95%

Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Al‐Harbi

Yassin

Shams

2012

Stoch Environ Res Risk Assess

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“…Temperature has found to be important, especially in combination with snowfall or rain (e.g., Branas and Knudson, 2001;Brown and Baass, 1997;Fridstrøm et al, 1995;Fridstrøm and Ingebrigtsen, 1991). We use the daily mean temperature in 0.1 degrees Celsius, the minimum temperature in 0.1 degrees Celsius and the maximum temperature in 0.1 degrees Celsius.…”

Section: Data Descriptionmentioning

confidence: 99%

Studying the effect of weather conditions on daily crash counts using a discrete time-series model

Brijs

Karlis

Wets

2008

Accident Analysis & Prevention

198

134

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In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an Integer Autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

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“…Andrey et al noted that injury rate increased more than 20% in Ottawa, Canada (1). Brown and Baass found fewer crashes involving injuries during winter in the province of Quebec, Canada (2). The impact of severe weather on fatal crashes is even harder to quantify, owing to the lower number of events involved and other confounding factors.…”

mentioning

confidence: 98%

Effects of Adverse Weather on Traffic Crashes

Lin

Nixon

2008

Transportation Research Record: Journal of the Transportation R

217

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identify relevant studies by a systematic search and then use effect size standardizing on each study result. In addition, this approach corrects sampling error and other artifacts, and it can present an estimate of the total effect with minimized subjectivity (5). Further, since different studies might be influenced by methodologies, time span, and regions, hierarchy meta-analysis has been applied using these factors as grouping variables. Separate analyses are conducted for each group.The process used in the meta-analysis is outlined in Figure 1. After careful review of the included articles, two separate meta-analyses were conducted separately for comparison studies and regression studies.For regression studies, measures of weather conditions were taken as the continuous variable. Effect size and percent change in crash rate were both applied to standardize the research results. However, due to space limitations of this paper, those results are not presented; they may be found in Qiu (6).For the comparison studies, measures of weather conditions were considered as a binary variable (such as snow versus nonsnow). In addition to the overall meta-analysis carried out for each weather category (snow, rain, etc.), hierarchy meta-analyses were conducted separately for the comparison studies stratified by decades (time span) and by countries. METHOD Literature SearchA literature search for relevant studies published from 1970 to 2005 was conducted for both peer-reviewed literature and unpublished technical reports and theses. Search strategies used ensured this study contained enough primary studies for meta-analysis, because meta-analysis based on a large number of studies with a small sample size has been shown to be more accurate than that based on a small number of studies with large sample sizes (5).Following the search, 376 papers and reports were selected for further examination, of which 108 were determined to be pertinent. From those reports, 34 that provided 78 result records were selected for meta-analysis. Study Inclusion Criteria and CodingPrevious studies that explored the association between weather conditions and traffic safety have investigated diverse variables. These studies have also applied a variety of methods and were based on Adverse weather obviously has an impact on vehicle crash rates on roads and highways. However, it would be valuable to quantify the extent to which weather conditions affect the crash rate. To do that, a meta-analysis has been conducted to generalize research findings on this subject and attempt to quantify the impact of weather on traffic crashes. Studies between 1967 and 2005 that examined the interaction of weather and traffic safety were reviewed. Thirty-four papers and 78 records that meet the predetermined criteria were included in the analysis. Crash rates from each study were normalized with respect to effect size for meta-analysis generalization. Results indicate that the crash rate usually increases during precipitation. Snow has a greater effect than rain does ...

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Seasonal Variation in Frequencies and Rates of Highway Accidents as Function of Severity

Cited by 33 publications

References 3 publications

Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Stochastic modeling of the impact of meteorological conditions on road traffic accidents

Studying the effect of weather conditions on daily crash counts using a discrete time-series model

Effects of Adverse Weather on Traffic Crashes

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