Quantifying the safety effects of horizontal curves on two-way, two-lane rural roads

Gooch, Jeffery P.; Gayah, Vikash V.; Donnell, Eric T.

doi:10.1016/j.aap.2016.03.024

Cited by 77 publications

(55 citation statements)

References 27 publications

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“…The visual inspection aimed to check if the characteristics specified in the dataset actually matched the real conditions, and if there were surrounding elements that may act as confounding factors for the analysis. In fact, ROR crashes at curves may also be influenced by the characteristics of other nearby elements [11][12][13]. At the end of this process, 17 sites were discharged because it was not possible to precisely localize the crash in the case of very close subsequent curves; five due to road works; 12 due to main intersections or significant cross-sectional modifications; nine were partially or entirely placed in sub-urban/urban environments; five were actually not two-lane rural road curves; one was a hairpin turn; and one was close to a tunnel.…”

Section: Selection Of Road Sites Based On Their Run-off-road Crash Himentioning

confidence: 99%

“…Finally, safety indicators were computed for the curves included in the selected road sites. The selected indicators were two crash modification factors (CMFs) for horizontal curves on two-way two-lane rural roads: the first included in the Highway Safety Manual (HSM) [19] (based on [34]), considering the absence of spiral transition curves in Equation (15), and the second provided in [12] (adapted from the original source in Equation (16), considering the presence of curves and the CCR measure). These can be reported as follows:…”

Section: Safety Measures Of Selected Road Sitesmentioning

confidence: 99%

“…In detail, while the first two factors are essentially physically-based and interact between them (design speeds, side friction, and the radii of curvature are typically related), the third factor is more complex. In recent research [11,12], it was shown how crash rates at curves may be influenced by the design characteristics of nearby road elements. For example, drivers, especially if unfamiliar with the road [13], may not expect a sharp curve after a very long tangent.…”

mentioning

confidence: 99%

“…In the case of curve C with a speed less than the previous curve speed, a single deceleration phase (DR depending on curve C, Equation (12)) is considered. In the opposite case, a single acceleration phase In both cases 1 and 2, the assumptions about the acceleration/deceleration rates computed through Equations (11) and (12) are reasonable: there is enough space for the driver to accelerate/decelerate through these rates. However, in the second case, since there is not enough space on the included tangent for two acceleration/deceleration phases (from the previous curve to the tangent, and from the tangent to the curve C), a single acceleration/deceleration phase is considered, depending on the difference between the subsequent curve speeds ( Figure 2).…”

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confidence: 99%

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Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

Intini¹,

Berloco²,

Ranieri³

et al. 2019

Infrastructures

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1) Run-off-road (ROR) crashes are a crucial issue worldwide, resulting in a disproportionate number of traffic deaths. In safety research, macro-level analysis on large datasets is usually conducted by linking explanatory variables to ROR crash frequency/severity. Micro-analysis approaches, like the one used in this study, are instead less frequent. (2) A comprehensive Italian Fatal + Injury (FI) crash dataset was filtered to identify two-way two-lane rural road curves on the national road network on which more than one ROR FI crash (i.e., at least two crashes) in the observation period of four years had occurred. The typical features of the ROR FI crashes and the recurrent geometric (characteristics of tangents and curves) and operational features (inferred speeds, acceleration/decelerations) of the crash sites were reconstructed. (3) The main contributory factors in ROR FI crashes are: wet pavements, speeding, and distraction. Sites with a relevant history of ROR FI crashes present recurrent safety issues such as inadequate horizontal curve coordination, an insufficient tangent length for decelerating, and inferred operating speeds comparable/higher than the inferred design speeds. (4) Based on findings, some practical suggestions for road safety management and maintenance are proposed through specific indicators and countermeasures (speed, perception, and friction related).Infrastructures 2020, 5, 3 2 of 25 Among the main causes of ROR crashes, speed, distraction, and fatigue play an important role (see [4]). Moreover, when specifically focusing on curves, the occurrence of ROR crashes may be influenced, among the other factors, by these road design aspects:

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Section: Selection Of Road Sites Based On Their Run-off-road Crash Himentioning

confidence: 99%

Section: Safety Measures Of Selected Road Sitesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

Intini¹,

Berloco²,

Ranieri³

et al. 2019

Infrastructures

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“…For example, according to a crash causation survey released by the US National Highway Traffic Safety Administration (NHTSA) in 2015 [2,3], drivers are to be criticised for 94% of crash cases. Apparently, the 94% of such accidents are also related to other causes from common experience, such as road alignment [4][5][6], traffic sign [7][8][9], and weather condition [10][11][12]. Therefore, the existing road accident statistics cannot fully reveal the causes, and traffic…”

Section: Introductionmentioning

confidence: 97%

A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

Zou

Yue

2017

Journal of Advanced Transportation

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Based on an overall consideration of factors affecting road safety evaluations, the Bayesian network theory based on probability risk analysis was applied to the causation analysis of road accidents. By taking Adelaide Central Business District (CBD) in South Australia as a case, the Bayesian network structure was established by integrating K2 algorithm with experts' knowledge, and Expectation-Maximization algorithm that could process missing data was adopted to conduct the parameter learning in Netica, thereby establishing the Bayesian network model for the causation analysis of road accidents. Then Netica was used to carry out posterior probability reasoning, the most probable explanation, and inferential analysis. The results showed that the Bayesian network model could effectively explore the complex logical relation in road accidents and express the uncertain relation among related variables. The model not only can quantitatively predict the probability of an accident in certain road traffic condition but also can find the key reasons and the most unfavorable state combination which leads to the occurrence of an accident. The results of the study can provide theoretical support for urban road management authorities to thoroughly analyse the induction factors of road accidents and then establish basis in improving the safety performance of the urban road traffic system.

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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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Quantifying the safety effects of horizontal curves on two-way, two-lane rural roads

Cited by 77 publications

References 27 publications

Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

A Bayesian Network Approach to Causation Analysis of Road Accidents Using Netica

Effectiveness of road safety interventions: An evidence and gap map

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