An aggregate accident model based on pooled, regional time-series data∗

Fridstrøm, Lasse; Ingebrigtsen, Siv

doi:10.1016/0001-4575(91)90057-c

Cited by 110 publications

(51 citation statements)

References 20 publications

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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%

“…Other researchers have looked at month-to-month changes in accident levels (Van den Bossche et al, 2005Bossche et al, , 2004Keeler, 1994;Fridstrøm and Ingebrigtsen, 1991). However, there are only few studies that have looked at changes in crash counts at a more disaggregate level.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, serial correlation between successive daily crash counts, i.e. autocorrelation, is reported as an important challenge for all accident models (Levine et al, 1995;Fridstrøm et al, 1995Fridstrøm et al, , 1991. For instance, Miaou and Lord (2003), Shankar data.…”

Section: Introductionmentioning

confidence: 99%

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

Brijs

Karlis

Wets

2008

Accident Analysis & Prevention

191

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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Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Brijs

Karlis

Wets

2008

Accident Analysis & Prevention

191

134

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“…There are many possible reasons for this high incidence of tra c-related death, including poor infrastructure for public transportation, increasing number of cars and motorcycles that would not meet safety standards elsewhere, and poor education of people about tra c rules. Socioeconomic/educational/cultural factors (e.g., social willingness to comply with the law) have signi cant pos-itive e ects on tra c-related fatalities [4][5][6]. Drivers with higher levels of education have been found to be associated with less severe crashes [7].…”

Section: Introductionmentioning

confidence: 99%

The effects of drivers’ behavior on driver-injury severities in Iran: An application of the mixed-logit model

2016

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Abstract. It has long been established that adverse drivers' behaviors, including behavior relating to distracted driving, propensities for risk-taking, and disregard for tra c rules, have been critical determinants with regard to the likelihood of a vehicle crash and its resulting injury severity. Socio-economic/educational/cultural factors have signi cant positive e ects on tra c-related fatalities. Using crash data from Iran over a six-year period from January 1, 2006 to December 31, 2011, this paper estimates a mixed logit model to investigate the unsafe driving behavior e ects on driver-injury severities in a vehicle collision on interprovincial routes. The methodological approach allows the parameters to vary across observations as opposed to a xed parameter model. The results showed random e ects of female drivers, separated bi-directional route and clear weather condition for no injury severity function, following too closely for minor injury function, and average annual daily tra c for severe injury function. Several factors were found to signi cantly increase the likelihood of severe injury crashes including disregarding tra c rules, exceeding the speed limit, improper overtaking/passing, and driving in an erratic and negligent manner. The results of this paper will further help decision-makers to better understand the contributing factors on crash injury severity and implement treatments to reduce the crash severity and improve transportation safety.

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“…Van den Bossche and Wets (2003), Starnes and Longthorne (2003), Tunaru and Jarrett (1998) and Yannis et al (2007) along with the references mentioned above (Knowles et al 2007;Broughton and Knowles 2010;Sonkin et al 2006;Fridstrom and Ingebrigtsen 1991) all contain examples of the use of exposure information, based on survey data but assumed to be a fixed quantity, to model accident risk.…”

Section: Introductionmentioning

confidence: 99%

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

Lloyd

Forster

2014

Computational Statistics & Data Analysis

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Several thousand people die as a result of a road accident each year in Great Britain and the trend in the number of fatal accidents is monitored closely to understand increases and reductions in the number of deaths. Results from analysis of these data directly influence Government road safety policy and ensure the introduction of effective safety interventions across the country.Overall accident numbers are important, but when disaggregating into various characteristics, accident risk (defined as the number of accidents relative to an exposure measure) is a better comparator. The exposure measure used most commonly for accident rate analysis is traffic flow which can be disaggregated into vehicle types, road type, and year. Here we want to assess the accident risk across different car types and car ages, and therefore alternative exposure sources are required. We disaggregate exposure to a further extent than possible with currently available data in order to take the increased variability within these new factors into account.Exposure data sources are mainly based on sample surveys and therefore have some associated uncertainty, however previous accident risk analysis has not, in general, taken this into account. For an explicit way to include this uncertainty we use a Bayesian analysis to combine three sources of exposure using a log-Normal model with model priors representing our uncertainty in each data source.Using further Bayesian models, we propagate this uncertainty through to accident rates and accident severity, determining important factors and interrelationships between factors to identify key features affecting accident trends, and we make the first exploration of the effect of the recent recession on road accidents.i ii

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An aggregate accident model based on pooled, regional time-series data∗

Cited by 110 publications

References 20 publications

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

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

The effects of drivers’ behavior on driver-injury severities in Iran: An application of the mixed-logit model

Modelling trends in road accident frequency— Bayesian inference for rates with uncertain exposure

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