Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference

Gong, Linfeng; Fan, Wei

doi:10.1016/j.aap.2017.02.014

Cited by 76 publications

(24 citation statements)

References 37 publications

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“…However, driving on a dry roadway surface lessened the likelihood (coefficient of −0.46) more than driving on a wet surface (coefficient of −0.39) for young drivers. Similar results were found in [31]. This indicated that even though young drivers travel carefully on wet surfaces, other factors such as tire conditions can contribute to crash probability and injury severity.…”

Section: Roadway Characteristicssupporting

confidence: 82%

Age-Related Differences in Motor-Vehicle Crash Severity in California

Adebisi

Masaki

Sobanjo

2019

Safety

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With ongoing changes in the age distribution of drivers in the United States, it is important to obtain insights on how to make the roadways equally safe for drivers across different age groups. In light of this, the objective of this study is to examine various crash characteristics and make recommendations on how to potentially improve roadway safety for all age groups. Using the Highway Safety Information System (HSIS) data, this study investigates the factors influencing motor-vehicle crash injury severity for young (aged 16–25), middle-aged (aged 26–64), and older drivers (above 64) in the state of California. A multinomial logit model was used to separately model crashes involving each age group and to evaluate the weight of different predictor variables on driver injury severity. The predictor variables were classified into four—driver, roadway, accident and environmental characteristics. Results suggest that there are close relationships between severity determinants for young and middle-aged drivers. However, older drivers tend to be most cautious among all age groups under all environmental and roadway conditions. Young drivers are more likely to explore their driving skills due to newness to driving. Middle-aged drivers are familiar with driving and tend to demonstrate less cautious behaviors, especially male drivers. Another insight obtained from this study is that older driver behavior is less dynamic compared to other age groups; their driving pattern is usually regular regardless of the surrounding conditions.

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Section: Roadway Characteristicssupporting

confidence: 82%

Age-Related Differences in Motor-Vehicle Crash Severity in California

Adebisi

Masaki

Sobanjo

2019

Safety

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“…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%

“…However, this percentage is strictly in line with that estimated from the benchmark dataset for all crashes. Moreover, the small dataset does not allow for investigation into combinations of these factors, but previous research has shown that the ages of different drivers may also be associated with different risk factors for ROR crashes [15].…”

Section: Period Of the Daymentioning

confidence: 99%

“…Crash modification factors (CMFs), which were chosen as indicators of the predicted safety characteristics, were computed for both the crash curves and adjacent curves. These are synthetic parameters that may express the crash risk of given curves, based on both their length and radius of curvature (see Equations (15) and (16)). Descriptive statistics on their computed values are reported in Table 4.…”

Section: Predicted Safety Characteristicsmentioning

confidence: 99%

“…Research in this field usually includes these above-mentioned factors while investigating ROR crashes. In particular, statistical techniques (e.g., [1,14,15]) are used to capture the most relevant factors that may influence both the occurrence and severity of ROR crashes. However, as previously explained, the most relevant factors may be inter-dependent, both design-and human-related, and may be site-specific.…”

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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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A generalized ordered logit analysis of risk factors associated with driver injury severity

Aidoo

Ackaah

2019

J Public Health (Berl.)

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Modeling single-vehicle run-off-road crash severity in rural areas: Accounting for unobserved heterogeneity and age difference

Cited by 76 publications

References 37 publications

Age-Related Differences in Motor-Vehicle Crash Severity in California

Age-Related Differences in Motor-Vehicle Crash Severity in California

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

A generalized ordered logit analysis of risk factors associated with driver injury severity

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