Addressing unobserved heterogeneity in the analysis of bicycle crash injuries in Scotland: A correlated random parameters ordered probit approach with heterogeneity in means

Fountas, Grigorios; Fonzone, Achille; Olowosegun, Adebola; McTigue, Clare

doi:10.1016/j.amar.2021.100181

Cited by 59 publications

(16 citation statements)

References 61 publications

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“…To estimate the random parameter binary logit models, the simulated maximum likelihood estimation technique was used [22,30]. Halton sequence draws [31] were deployed in order to conduct the numerical integrations that are required for model estimation [32].…”

Section: Methodological Approachmentioning

confidence: 99%

“…Even though a dry road surface implies the immediate absence of precipitation and the likely presence of mild weather, a portion of drivers, typically those who are familiar with the school commute, may be prompted to undertake more risk-taking manoeuvres due the lack of apparent road hazards. The heterogeneity underpinning the impact of the road surface conditions on injury severities and the potential relationship with risk compensating behaviour has been also discussed in previous research [22,44].…”

Section: School Travel-related Accidents In Urban Areasmentioning

confidence: 98%

“…Serious injuries refer to severe injuries that led to the hospitalization of the associated person or required serious medical treatment (e.g., concussions, fractures, or internal injuries) [21]. As noted in [22,23], the STATS19 database does not record no-injury accidents, which has been highlighted as a limitation of its reporting mechanism [24].…”

Section: Datamentioning

confidence: 99%

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Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

Fountas

Olowosegun

Basbas

2022

Safety

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School travel has been a significant source of safety concerns for children, parents, and public authorities. It will continue to be a source of concerns as long as severe accidents continue to emerge during pupils’ commute to school. This study provides an empirical analysis of the factors influencing the injury severities of the accidents that occurred on trips to or from school in Scotland. Using 9-year data from the STATS19 public database, random parameter binary logit models with allowances for heterogeneity in the means were estimated in order to investigate injury severities in urban and rural areas. The results suggested that factors such as the road type, lighting conditions, vehicle type, and age of the driver or casualty constitute the common determinants of injury severities in both urban and rural areas. Single carriageways and vehicles running on heavy oil engines were found to induce opposite effects in urban and rural areas, whereas the involvement of a passenger car in the accident decomposed various layers of unobserved heterogeneity for both area types. The findings of this study can inform future policy interventions with a focus on traffic calming in the proximity of schools.

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Section: Methodological Approachmentioning

confidence: 99%

Section: School Travel-related Accidents In Urban Areasmentioning

confidence: 98%

Section: Datamentioning

confidence: 99%

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Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

Fountas

Olowosegun

Basbas

2022

Safety

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“…The occurrence of rainfall reduces the likelihood of collisions of all severity levels (26). Sunny weather increased the likelihood of serious injury and fatal bicycle-motor vehicle crashes by 78% (12). Dry weather was more likely to result in injury crashes (minor, serious, and fatal) (5).…”

Section: Environment Characteristics Weathermentioning

confidence: 99%

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Wang

Jin

Chen

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Exploring the heterogeneity of factors influencing the severity of electric bicycle crashes and electric motorcycle crashes can help target accident prevention policies to improve traffic safety. Therefore, this paper establishes a mean heterogeneity random parameter logit model using crash data from 2016 to 2020 in Guangxi to explore the different factors influencing the severity of crashes involving electric motorcycles and electric bicycles. The results show that the key influences on crash severity differ in electric motorcycle crashes and electric bicycle crashes. At the same time, some common factors affect the two types of crashes to different degrees. In addition, the complex interaction effects of unobserved heterogeneity were considered to explore the random parameters of the two types of crashes. The effect of unobserved heterogeneity on the distribution characteristics of the random parameters was then determined. For example, in electric motorcycle crashes, street lighting at night has a random parameter characteristic. The likelihood of serious crashes decreased when both street lighting at night and vehicle left turn were involved, and decreased when both street lighting at night and no signal control were involved. In electric bicycle crashes, large trucks have a random parameter characteristic. The likelihood of serious crashes increased when both large trucks and motor vehicle lights not turned on were involved, and increased when both large trucks and visibility less than or equal to 200 meters were involved. The results provide a basis for improving the road safety of electric motorcycles and electric bicycles.

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“…To overcome these limitations, this study proposes an integrated modelling framework (i.e., the correlated random parameter ordered probit approach with heterogeneity in the means). Even though a few studies recently applied a similar modelling framework for the statistical analysis of accident injury severities (e.g., Fountas et al, 2021;Se et al, 2021;Ahmed et al, 2021), this approach has not been used to analyse pedestrian-vehicles accidents, to the best of the authors' knowledge.…”

Section: Previous Research On Injury Severities At Junctions and Cros...mentioning

confidence: 99%

Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: The case of Scotland

Olowosegun

Babajide

Akintola

et al. 2022

Accident Analysis & Prevention

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Addressing unobserved heterogeneity in the analysis of bicycle crash injuries in Scotland: A correlated random parameters ordered probit approach with heterogeneity in means

Cited by 59 publications

References 61 publications

Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

Assessing School Travel Safety in Scotland: An Empirical Analysis of Injury Severities for Accidents in the School Commute

Factors Associated With the Severity of Motor Vehicle Crashes Involving Electric Motorcycles and Electric Bicycles: A Random Parameters Logit Approach With Heterogeneity in Means

Analysis of pedestrian accident injury-severities at road junctions and crossings using an advanced random parameter modelling framework: The case of Scotland

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