Explaining variation in safety performance of roundabouts

Daniëls, Stijn; Brijs, Tom; Nuyts, Erik; Wets, Geert

doi:10.1016/j.aap.2009.08.019

Cited by 101 publications

(76 citation statements)

References 17 publications

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“…Generally, it was found that roundabouts are able to reduce injury crashes considerably, although not for all user groups (Daniels et al, 2008;2009;Elvik, 2003;Persaud et al, 2001). In a previous analysis by the same authors, crash prediction models were fit for all injury crashes at roundabouts (Daniels et al, 2010.). The results showed that vulnerable road users (moped riders, motorcyclists, bicyclists and pedestrians) are more frequently than expected involved in crashes at roundabouts.…”

Section: Introductionmentioning

confidence: 89%

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Externality of risk and crash severity at roundabouts

Daniëls

Brijs

Nuyts³

et al. 2010

Accident Analysis & Prevention

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The severity of 1491 crashes on 148 roundabouts in Flanders-Belgium was examined in order to investigate which factors might explain the severity of crashes or injuries and to relate these factors to the existing knowledge about contributing factors for injury severity in traffic.Logistic regression and hierarchical binomial logistic regression techniques were used.A clear externality of risk appeared to be present in the sense that vulnerable road user groups (pedestrians, bicyclists, moped riders and motorcyclists) are more severely affected than others. Fatalities or serious injuries in multiple-vehicle crashes for drivers of four-wheel vehicles are much rarer.Injury severity increases with higher age. Crashes at night and crashes outside built-up areas are more severe. Single-vehicle crashes seem to have more severe outcomes than multiplevehicle crashes. However, systematic differences in the reporting rate of crashes are likely to exist and may have affected the stated results. Correlations with important, but unobserved variables like the impact speeds in the crashes might exist as well and could provide an alternative explanation for some results.3

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Section: Introductionmentioning

confidence: 89%

“…The dataset departed from a previously composed dataset of 90 roundabouts (Daniels et al, 2010), that was extended. Each roundabout in the sample was visited and photographed, traffic counts were executed and geometric data were collected on the spot.…”

Section: Data Collectionmentioning

confidence: 99%

Externality of risk and crash severity at roundabouts

Daniëls

Brijs

Nuyts³

et al. 2010

Accident Analysis & Prevention

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“…The most radical measures refers to segregate lanes, which has been proved to be an efficient solution to protect riders from conflicts with cars and trucks, the greatest benefit of this solution being got when the PTW traffic overcome a certain level (according to Radin Umar, Mackay and Hills [25]: 15,000 vehicles a day with a proportion between 20% and 30% of PTWs in the traffic). But less massive solutions can also be promoted to avoid critical interactions with cars in town, such as replacing intersections, known to be detrimental to PTWs, with roundabouts, but considering the necessity to adapt them to avoid PTWs single vehicle crashes when negotiating them [26].…”

Section: Discussionmentioning

confidence: 99%

Powered two-wheelers in urban environment: A detailed accident analysis

Elslande¹,

Fournier²,

Parraud³

2015

Int. J. SAFE

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The powered two-wheelers (PTWs; moped, motorcycles and scooters) have long been considered as a marginal population, but they developed significantly these last decades in most parts of the world, necessitating a new view concerning them. This is particularly the case in urban areas where their use has increased, notably linked to the possibility they offer to escape the problems of traffic congestion, which attracts an increasingly vast and varied population. In addition, PTWs can also be a good compromise to face in inner-city parking problems. As such, the motorized two wheelers are considered by certain authors as an essential contribution to the urban traffic of tomorrow. However, this view is currently contradicted by the poor level of safety linked to the integration of such vehicles within traffic. As a matter of fact, most epidemiological studies show the overall excess risk to which PTWs are confronted in the traffic system, this risk coming notably from the extreme vulnerability of PTW riders in case of a crash, and relying on some specific accident patterns. This negative effect must be neutralized for PTW to become a true urban mobility tool. The purpose of the present paper is to identify the causes and mechanisms of PTW traffic crashes occurred in town, notably comparing them with those produced in countryside areas. The data rely on a detailed analysis of a sample of 1308 accident police records. The results put emphasis on the difficulties met by PTW users on the road or in the street, and also the difficulties met by car drivers confronted to them. A better knowledge of the specificities and the mechanisms of PTW involvement in injury accidents is viewed as offering a potential improvement of their safety through an adapted urban infrastructure development.

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“…Although rare, this phenomenon can arise when the sample size is very small, leading to erroneous parameter estimates [24] [25]. To address the limitations of NB models, Poisson-lognormal models have been proposed, in which the error term is Poisson-lognormal rather than gamma-distributed to better handle the under-dispersed crash counts [21] [26] [27]. Another widely used type of crash prediction model is the zero-inflated Poisson and zero-inflated negative binomial models, which have been introduced mainly to deal with the over-dispersion problem caused by excessive zeroes (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To address the limitations of the NB models, the Poisson-lognormal model was introduced, in which the error term is Poisson-lognormal rather than gammadistributed so as to better handle under-dispersed data counts [21] [26] [27]. The Poisson-lognormal model is similar to the negative binomial model, however, the EXP (ε i ) term used in the model is lognormal-rather than gamma-distributed.…”

Section: Poisson-lognormal Regression Modelmentioning

confidence: 99%

Road Crash Prediction Models: Different Statistical Modeling Approaches

Abdulhafedh¹

2017

JTTs

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Road crash prediction models are very useful tools in highway safety, given their potential for determining both the crash frequency occurrence and the degree severity of crashes. Crash frequency refers to the prediction of the number of crashes that would occur on a specific road segment or intersection in a time period, while crash severity models generally explore the relationship between crash severity injury and the contributing factors such as driver behavior, vehicle characteristics, roadway geometry, and road-environment conditions. Effective interventions to reduce crash toll include design of safer infrastructure and incorporation of road safety features into land-use and transportation planning; improvement of vehicle safety features; improvement of post-crash care for victims of road crashes; and improvement of driver behavior, such as setting and enforcing laws relating to key risk factors, and raising public awareness. Despite the great efforts that transportation agencies put into preventive measures, the annual number of traffic crashes has not yet significantly decreased. For instance, 35,092 traffic fatalities were recorded in the US in 2015, an increase of 7.2% as compared to the previous year. With such a trend, this paper presents an overview of road crash prediction models used by transportation agencies and researchers to gain a better understanding of the techniques used in predicting road accidents and the risk factors that contribute to crash occurrence.

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Explaining variation in safety performance of roundabouts

Cited by 101 publications

References 17 publications

Externality of risk and crash severity at roundabouts

Externality of risk and crash severity at roundabouts

Powered two-wheelers in urban environment: A detailed accident analysis

Road Crash Prediction Models: Different Statistical Modeling Approaches

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