Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis

Lee, Joyoung; Mannering, Fred

doi:10.1016/s0001-4575(01)00009-4

Cited by 401 publications

(206 citation statements)

References 26 publications

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“…In this model, a base category is selected out of the different outcomes and other categories are estimated with respect to the base category. Many researchers have used multinomial logit models for accident severity analysis [1][2][3][4][5][6][7][8][9][10]. If the three severity levels are represented by 0, 1, and 2 with 0 as the reference or base category then the model structure for a three-level data structure (occupant-based data) is given by Eq.…”

Section: Multilevel Logistic Regression Modelsmentioning

confidence: 99%

“…This type of data is mostly modeled using logistic regression models. Most of the models are extensions of the multinomial logit models based on the assumption of independent severity classes [1][2][3][4][5][6][7][8][9][10][11]. Although different modeling methodologies are available from literature to examine collision severity as related to various influencing factors, little is known on the relative merits of these alternatives.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Multilevel Logistic Regression Modelsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Specifically, following previous studies (Lee and Mannering, 2002, Dissanayake and Ratnayake, 2006, Milton et al, 2008, Mujalli and de Oña, 2011, Anastasopoulos and Mannering, 2011, the dependent variable is the injury severity level of the most severely injured individual (could be a driver or a passenger) in each crash. 6 Injury severity is recorded in five ordinal categories: (1) no injury (52.7% of the sample), (2) possible injury (27.7%), (3) non-incapacitating injury (17.2%), (4) incapacitating injury (1.7%), and (5) fatal injury (0.7%).…”

Section: Sample Formationmentioning

confidence: 99%

A spatial generalized ordered response model to examine highway crash injury severity

Castro

Paleti

Bhat

2013

Accident Analysis & Prevention

112

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This paper proposes a flexible econometric structure for injury severity analysis at the level of individual crashes that recognizes the ordinal nature of injury severity categories, allows unobserved heterogeneity in the effects of contributing factors, as well as accommodates spatial dependencies in the injury severity levels experienced in crashes that occur close to one another in space. The modeling framework is applied to analyze the injury severity sustained in crashes occurring on highway road segments in Austin, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files from 2009 and includes a variety of crash characteristics, highway design attributes, driver and vehicle characteristics, and environmental factors. The results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects. The most important determinants of injury severity on highways, according to our results, are (1) whether any vehicle occupant is ejected, (2) whether collision type is head-on, (3) whether any vehicle involved in the crash overturned, (4) whether any vehicle occupant is unrestrained by a seat-belt, and (5) whether a commercial truck is involved.

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“…These models represent an alternative way to handle data characterized by a significant amount of zeros or more zeros than the one would expect in a traditional Poisson or NB (Carson and Mannering, 2001;Lee and Mannering, 2002;Qin et al, 2005). However, Lord et al (2005Lord et al ( , 2007 have questioned the validity of these models in most safety modelling circumstances.…”

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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Impact of roadside features on the frequency and severity of run-off-roadway accidents: an empirical analysis

Cited by 401 publications

References 26 publications

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

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

A spatial generalized ordered response model to examine highway crash injury severity

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

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