A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level

Usman, Taimur; Fu, Liping; Miranda-Moreno, Luis F.

doi:10.1016/j.aap.2012.02.005

Cited by 58 publications

(58 citation statements)

References 30 publications

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“…For instance, several researchers have assessed the effect of weather elements, i.e., snow fall (Andrey, 2010;Eisenberg and Warner, 2005;Hermans et al, 2006;Khattak and Knapp, 2001), rain fall (Abdel-Aty and Pemmanaboina, 2006;Usman et al, 2012;, wind speed (Jung et al, 2011;Usman et al, 2012), temperature (Bergel et al, 2013;Brijs et al, 2008;El-Basyouny and Kwon, 2012 crash occurrence (Ahmed et al, 2012;Brijs et al, 2008;Usman et al, 2012;, whereas other researchers investigated these effects on crash severity (Andrey, 2010;Bergel et al, 2013;Eisenberg, 2004;El-Basyouny and Kwon, 2012). While the above studies are useful in understanding the effect of adverse weather, it is not always possible to identify suitable countermeasures based on the results of these studies.…”

Section: Introductionmentioning

confidence: 96%

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

El-Basyouny

Barua

Islam

2014

Accident Analysis & Prevention

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Section: Introductionmentioning

confidence: 96%

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

El-Basyouny

Barua

Islam

2014

Accident Analysis & Prevention

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“…This research makes use of a collision database prepared in our previous effort [23,28,29]. This dataset is unique in several aspects, including reliable observations on traffic and environmental conditions when the collision occurred, and extensive spatial and temporal coverage, as described in the following section.…”

Section: Data Descriptionmentioning

confidence: 99%

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

Usman

Miranda-Moreno

2016

J. Mod. Transport.

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This paper describes an empirical study aiming at identifying the main differences between different logistic regression models and collision data aggregation methods that are commonly applied in road safety literature for modeling collision severity. In particular, the research compares three popular multilevel logistic models (i.e., sequential binary logit models, ordered logit models, and multinomial logit models) as well as three data aggregation methods (i.e., occupant based, vehicle based, and collision based). Six years of collision data (2001)(2002)(2003)(2004)(2005)(2006) from 31 highway routes from across the province of Ontario, Canada were used for this analysis. It was found that a multilevel multinomial logit model has the best fit to the data than the other two models while the results obtained from occupant-based data are more reliable than those from vehicle-and collision-based data. More importantly, while generally consistent in terms of factors that were found to be significant between different models and data aggregation methods, the effect size of each factor differ substantially, which could have significant implications for evaluating the effects of different safety-related policies and countermeasures.

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“…Accordingly, elasticity for RSI (1.93) was found relatively high in comparison to the weather related factors (ranges from 0.02 to 0.5). A more extensive discussion on the findings can be found in Usman et al (2012).…”

Section: Collision Modelmentioning

confidence: 99%

“…For example, the Ministry of Transportation Ontario (MTO) implements a condition description system including 7 major categories and 486 subcategories for reporting possible road conditions in the winter season (Usman et al 2012). While the subcategories are useful for maintenance operations, only the main categories are used for reporting purposes and disseminated to the public through its own information portal system.…”

Section: Winter Road Surface Condition Classification and Reporting Pmentioning

confidence: 99%

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A risk-based approach to winter road surface condition classification

Thakali

Kwon

et al. 2017

Can. J. Civ. Eng.

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This paper presents a risk-based approach for classifying the road surface conditions of a highway network under winter weather events. A relative risk index (RRI) is developed to capture the effect of adverse weather conditions on the collision risk of a highway in reference to the normal driving conditions. Based on this index, multiple risk factors related to adverse winter weather conditions can be considered either jointly or separately. The index can also be used to aggregate different types of road conditions observed on any given route into a single class for risk-consistent condition classification and reporting. Two example applications are shown to illustrate the advantages of the proposed approach.Key words: road surface condition classification, relative risk index, collision model. Résumé :Cet article présente une approche fondée sur le risque afin de classifier les conditions de la surface de roulement d'un réseau d'autoroutes en fonction d'événements météorologiques d'hiver. On a développé un indice relatif du risque afin de déterminer l'effet des mauvaises conditions météorologiques sur le risque de collision sur une autoroute par rapport aux conditions de conduite normale. En se fondant sur cet indice, on peut considérer les facteurs de risques multiples liés aux conditions météorologiques défavorables d'hiver, soit conjointement ou séparément. On peut aussi utiliser l'indice afin de grouper différents types de conditions routières observées sur n'importe quelle route donnée en une seule classe, et ce, pour la classification et la production de rapport des conditions cohérentes avec le risque. On montre deux exemples d'applications afin d'illustrer les avantages de l'approche proposée. [Traduit par la Rédaction] Mots-clés : classification de la surface de roulement selon la condition, indice relatif du risque, modèle de collision.

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A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level

Cited by 58 publications

References 30 publications

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

Investigation of time and weather effects on crash types using full Bayesian multivariate Poisson lognormal models

Injury severity analysis: comparison of multilevel logistic regression models and effects of collision data aggregation

A risk-based approach to winter road surface condition classification

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