Factors Affecting Median Cable Barrier Crash Frequency: New Insights

Chimba, Deo; Emaasit, Daniel; Allen, Steve; Hurst, Brian; Nelson, Marcie

doi:10.1080/19439962.2013.812168

Cited by 14 publications

(3 citation statements)

References 20 publications

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“…Statistical analysis of historical crashes has been another approach used in the past to evaluate traffic barriers. A majority of past statistical studies considered before-after evaluations to identify the performance of new traffic barrier segments in comparison to before period [16][17][18][19]. Villwock et al studied the effect of installing new median cable barriers and found that new cable barriers increased the frequency of median crashes [19].…”

Section: Introductionmentioning

confidence: 99%

“…However, the severity of these crashes was identified to be less than before installing the new cable barriers. A study conducted by Chimba et al estimated that a reduction of 82% and 76% in fatal and incapacitating injury crashes, respectively, were observed after the installation of new median cable barriers [18]. Some studies also considered statewide crash datasets for evaluating all traffic barrier crashes reported in a region (state) in a specific period [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates

Moomen¹,

Molan

Ksaibati

2023

Sustainability

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This paper investigated factors influencing injury severity of crashes involving median traffic barriers, including the impact of barrier characteristics and their geometric features in Wyoming. Combining field data of inventoried median barriers with crash data on Wyoming interstates highways, a random parameters multinomial logit (mixed logit) model of injury severity was estimated. This methodological approach allowed for the possibility of estimated model parameters to vary randomly across crash observations to account for heterogeneity with respect to driver characteristics, roadway attributes, and vehicle characteristics. The estimation results indicated concrete barriers installed on front side-slopes and box beam barriers were associated with severe injury crashes. It was also found that median barrier crashes involving sports utility vehicles, pickups, and improperly restraint vehicle occupants are complex and vary significantly across observations. Other statistically significant variables found to increase the likelihood of severe injury crashes were rural interstate roads, concrete barriers installed on a front side-slope, box beam barriers with lateral offset less than 2 feet, and rollover crashes. These parameters were fixed across observations. The findings of this research point to the need to further investigate the impacts of sport utility vehicles, pickups, and rollover crashes on median barrier crash injury severity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates

Moomen¹,

Molan

Ksaibati

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…A few research studies have investigated such correlations [9][10][11]. However, the majority of the developed deterioration models in the literature did not take into consideration such interrelations and investigated the condition of each asset independent from its neighbors [12][13][14][15][16][17][18]. For example, Abaza et al [12] forecasted pavement condition only based on historical condition data of pavements.…”

Section: Introductionmentioning

confidence: 99%

Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach

et al. 2022

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Transportation agencies constantly strive to tackle the challenge of limited budgets and continuously deteriorating highway infrastructure. They look for optimal solutions to make intelligent maintenance and repair investments. Condition prediction of highway assets and, in turn, prediction of their maintenance needs are key elements of effective maintenance optimization and prioritization. This paper proposes a novel risk-based framework that expands the potential of available data by considering the probabilistic susceptibility of assets in the prediction process. It combines a risk score generator with machine learning to forecast the hotspots of multiple defects while considering the interrelations between defects. With this, we developed a scalable algorithm, Multi-asset Defect Hotspot Predictor (MDHP), and then demonstrated its performance in a real-world case. In the case study, MDHP predicted the hotspots of three defects on paved ditches, considering the interrelation between paved ditches and five nearby assets. The results demonstrate an acceptable accuracy in predicting hotspots while highlighting the interrelation between adjacent assets and their contribution to future defects. Overall, this study offers a scalable approach with contribution in data-driven multi-asset maintenance planning with potential benefits to a broader range of linear infrastructures such as sewers, water networks, and railroads.

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Effectiveness of road safety interventions: An evidence and gap map

Goel,

Tiwari,

Varghese

et al. 2024

Campbell Systematic Reviews

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BackgroundRoad Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low‐ and middle‐income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high‐income countries have successfully reduced RTI by using a public health approach and implementing evidence‐based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high‐income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.ObjectivesThe objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.Search MethodsThe EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.Selection CriteriaThe interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post‐crash pre‐hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non‐fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.Data Collection and AnalysisThe EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case‐control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.Main ResultsThe EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post‐crash pre‐hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non‐fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety‐six percent of the studies were reported from high‐income countries (HIC), 4.5% from upper‐middle‐income countries, and only 1.4% from lower‐middle and low‐income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.Authors' ConclusionsThe EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence‐synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

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Factors Affecting Median Cable Barrier Crash Frequency: New Insights

Cited by 14 publications

References 20 publications

A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates

A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates

Multi-Asset Defect Hotspot Prediction for Highway Maintenance Management: A Risk-Based Machine Learning Approach

Effectiveness of road safety interventions: An evidence and gap map

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