A critical review of macro models for road accidents

Hakim, Simon; Shefer, Daniel; Hakkert, A S; Hocherman, Irit

doi:10.1016/0001-4575(91)90058-d

Cited by 111 publications

(69 citation statements)

References 36 publications

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“…Key references for this approach are e.g. [13,14]. In practice, truly comprehensive analyses are often infeasible, and, therefore, many authors tend to focus on particular effects or relations, such as comparing crash rates between different regions or assessing trends in the rates in relation to selected explanatory macro-level variables, such as the size and composition of the car stock [15,16].…”

Section: Approaches In Analysis Of Road Traffic Sensitivity To Weathementioning

confidence: 99%

Weather Conditions, Weather Information and Car Crashes

Perrels

Votsis

Nurmi

et al. 2015

IJGI

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Road traffic safety is the result of a complex interaction of factors, and causes behind road vehicle crashes require different measures to reduce their impacts. This study assesses how strongly the variation in daily winter crash rates associates with weather conditions in Finland. This is done by illustrating trends and spatiotemporal variation in the crash rates, by showing how a GIS application can evidence the association between temporary rises in regional crash rates and the occurrence of bad weather, and with a regression model on crash rate sensitivity to adverse weather conditions. The analysis indicates that a base rate of crashes depending on non-weather factors exists, and some combinations of extreme weather conditions are able to substantially push up crash rates on days with bad weather. Some spatial causation factors, such as variation of geophysical characteristics causing systematic differences in the distributions of weather variables, exist. Yet, even in winter, non-spatial factors are normally more significant. GIS data can support optimal deployment of rescue services and enhance in-depth quantitative analysis by helping to identify the most appropriate spatial and temporal resolutions. However, the supportive role of GIS should not be inferred as existence of highly significant spatial causation.

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Section: Approaches In Analysis Of Road Traffic Sensitivity To Weathementioning

confidence: 99%

Weather Conditions, Weather Information and Car Crashes

Perrels

Votsis

Nurmi

et al. 2015

IJGI

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show abstract

“…Although the ordinary least square regression model (OLS) is applied in the road safety literature (Hakim et al, 1991;Zlatopher, 1988), some other authors have questioned its suitability (Jovanis and Chang, 1986;Miaou et al, 1992;Miaou and Lum, 1993). This method assumes that the dependent variable is continuous and normally distributed with a constant variance.…”

Section: Models To Evaluate the Influence Of Certain Variables On Roamentioning

confidence: 99%

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Rangel

Vassallo

Herraiz

2013

Accident Analysis & Prevention

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The goal of this paper is to evaluate whether the incentives incorporated in toll highway concession contracts in order to encourage private operators to adopt measures to reduce accidents are actually effective at improving safety. To this end, we implemented negative binomial regression models using information about highway characteristics and accident data from toll highway concessions in Spain from 2007 to 2009. Our results show that even though road safety is highly influenced by variables that are not managed by the contractor, such as the annual average daily traffic (AADT), the percentage of heavy vehicles on the highway, number of lanes, number of intersections and average speed; the implementation of these incentives has a positive influence on the reduction of accidents and injuries. Consequently, this measure seems to be an effective way of improving safety performance in road networks.

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“…A larger blood alcohol concentration (BAC) implies a higher probability of getting involved in an accident [12]. Moreover, consumption of alcohol has been seen to increase the frequency of fatal accidents [6]. The percentage of road users driving under the influence of alcohol (i.e.…”

Section: Selection Of Road Safety Indicatorsmentioning

confidence: 99%

“…The relationship between numerous explanatory factors on the one handbehavioural, economic, climatologic, infrastructural, legislative, etc.-and the number of accidents and casualties on the other hand, has been studied amongst others by Hakim et al [6], Scuffham [7], Eisenberg [8], Van den Bossche et al [9] and Hermans et al [10]. The most contributing road safety dimensions are the road user behaviour (speed, alcohol, seatbelt use), the vehicle safety (composition and age of the vehicle fleet) and environmental factors (expenditure on roads, gross domestic product, urban population, hospital care) [11,12].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty assessment of the road safety index

Hermans

Bossche²,

Wets

2009

Reliability Engineering & System Safety

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a b s t r a c tComposite indicators aggregate domain-specific information in one index, on the basis of which countries can be assigned a relative ranking. Recently, the road safety community got convinced of the policy supporting role of indicators in terms of benchmarking, target setting and selection of measures. However, combining the information of a set of relevant risk indicators in one index presenting the whole picture turns out to be very challenging. In particular, the rank of a country can be largely influenced by the methodological choices made during the composite indicator building process. Decisions concerning the selection of indicators, the normalisation of the indicator values, the weighting of indicators and the way of aggregating can influence the final ranking. In this research, it is shown that the road safety ranking of countries differs significantly according to the selected weighting method, the expert choice and the set of indicators. From these three input factors, the selection of the set of indicators is most influential. A well considered selection of indicators will therefore establish the largest reduction in ranking uncertainty. With a set of appropriate indicators, the proposed framework reveals the major sources of uncertainty in the creation of a composite road safety indicator.

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A critical review of macro models for road accidents

Cited by 111 publications

References 36 publications

Weather Conditions, Weather Information and Car Crashes

Weather Conditions, Weather Information and Car Crashes

The influence of economic incentives linked to road safety indicators on accidents: The case of toll concessions in Spain

Uncertainty assessment of the road safety index

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