Contributing factors to vehicle to vehicle crash frequency and severity under rainfall

Jung, Soyoung; Jang, Kitae; Yoon, Yoonjin; Kang, Sanghyeok

doi:10.1016/j.jsr.2014.01.001

Cited by 51 publications

(15 citation statements)

References 22 publications

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“…The studies related to road characteristics include the curve radius/ curvature (Coutton-Jean et al, 2009;Bella et al, 2014;Boer, 1996), edge lines (Coutton-Jean et al, 2009), lane width (Robertshaw and Wilkie, 2008;Coutton-Jean et al, 2009), curve length (Zuriaga et al, 2010;Hu and Donnell, 2010) and pavement condition (Buddhavarapu et al, 2013;Zador et al, 1987). The research related to environmental conditions in curve segments includes weather conditions (Jung et al, 2014;Yan et al, 2014), nighttime (Bella et al, 2014;Hu and Donnell, 2010), roadside clearance (Aram, 2010;Bella, 2013), sight distance (Kondo and Ajimine, 1968), traffic volume (Aram, 2010;AASHTO, 2010) and markings and speed signs (Rutley, 1972;Comte and Jamson, 2000). The vehicle-related factors can significantly influence the relevant driving behaviors and running out of curve crashes, such as vehicle type (Liu and Subramanian, 2009;Fitzsimmons et al, 2013) and vehicle occupancy (Liu and Ye, 2011).…”

Section: Factors Associated With Curve Safetymentioning

confidence: 99%

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Yan

Wong

2015

Accident Analysis & Prevention

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Driving on curved roads has been recognized as a significant safety issue for many years. However, driver behavior and the interactions among variables that affect driver performance on curves is complicated and not well understood. Previous studies have investigated various factors that influence driver performance on right- or left-turn curves, but have paid little attention to the effects of foggy weather, driver experience and gender on driver performance on complex curves. A driving simulator experiment was conducted in this study to evaluate the relationships between driving behavior on a continuous S-curve and foggy weather, driver experience and gender. The process of negotiating a curve was divided into three stages consisting of a straight segment, the transition from the straight segment to the S-curve and the S-curve. The experimental results indicated that drivers tended to drive more cautiously in heavy fog, but the driving risk was still increased, especially in the transition stage from the straight segment to the S-curve. The non-professional (NP) drivers were less sensitive to the impending change in the road geometry, and less skilled in both longitudinal and lateral vehicle control than the professional drivers. The NP female drivers in particular were found to be the most vulnerable group in S-curve driving.

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Section: Factors Associated With Curve Safetymentioning

confidence: 99%

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Yan

Wong

2015

Accident Analysis & Prevention

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“…Besides their devastating impact on roadway users, traffic crashes lead to substantial economic losses on both personal and societal levels [1]. Due to the complexity of a traffic crash event, it is a challenging task to identify all the contributing factors, and even more challenging to quantify their level of impact on crash rates [2,3]. Some of these factors include driver behavior and human factors, traffic conditions, road geometrics, pavement surface condition, and traffic speed.…”

Section: Introductionmentioning

confidence: 99%

Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa

et al. 2018

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Safety performance is a crucial component of highway network performance evaluation. Besides their devastating impact on roadway users, traffic crashes lead to substantial economic losses on both personal and societal levels. Due to the complexity of crash events and the unique conditions in each country and state, empirical local calibration for the correlation between attributes of interest and the safety performance is always recommended. Limited studies have established a procedure to analyze the impact of pavement condition on traffic safety in a risk analysis scheme. This study presents a thorough analysis of some roadway departure crashes which occurred in Iowa between 2006 and 2016. All crash records were mapped onto one-mile segments with known traffic volume (i.e., AADT), posted speed limits (SL), skid numbers (SN), ride qualities (IRI), and rut depths (RD) in a geographic information system (GIS) database. The crash records were correlated to the pavement surface condition (i.e., SN, IRI, and RD) using negative binomial regression models. Moreover, a novel risk analysis framework is introduced to perform crash risk assessment and evaluate the possible consequences for a given combination of events. The analysis shows a significant impact of pavement skid resistance on roadway-departure crashes under all accident conditions and severities. Risk analysis will facilitate coordination between the pavement management system and safety management system in the future, which will help with optimizing the overall highway network performance.Several studies have investigated the relationship between pavement surface friction and crash risk, and could quantify significant correlations between them. However, these correlations can be delusive and challenging to derive due to the highly varied and uncontrolled conditions during a crash event [7,8]. One of the most extensive efforts in this research area is the National Corporative Research Program (NCHRP) project 01-43 "Guide for Pavement Friction". The project report was published in 2010 [9]. In the report, quantitative correlations between wet crash rates and pavement friction values could be found in more than 15 studies in the literature. The study focused on describing and illustrating the importance of friction in highway safety, as well as the principles of pavement friction characterization. Furthermore, the study presented valuable information on (a) the management of friction on existing highway pavements, and (b) the design of new highway surfaces with adequate friction. This information focuses on techniques for monitoring friction and crashes, and determining the need for remedial action. However, two components, friction and safety management, are still disconnected and not synchronized efficiently.Pavement friction develops between the tire and the pavement due to the hysteresis and adhesion mechanisms at the tire-pavement interface [9,10]. The two mechanisms, strongly depend on the contact surface area between the tire and the pavement surface, wh...

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“…Earlier studies have identified an increased risk of crash injuries during the rain (Andrey & Yagar, 1993; Jung, Jang, Yoon, & Kang, 2014; Qiu & Nixon, 2008; A. Theofilatos & Yannis, 2014), snow (El-Basyouny, Barua, & Islam, 2014; Fridstrøm, Ifver, Ingebrigtsen, Kulmala, & Thomsen, 1995; A.…”

Section: Introductionmentioning

confidence: 99%

Road environment characteristics and fatal crash injury during the rush hour period in the U.S.: Model testing and nested analytical study

Adeyemi¹,

Paul

Delmelle

et al. 2022

Preprint

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Background: A substantial proportion of crash injuries occur during the rush hour period. This study aims to assess the relationship between county level road environmental characteristics and fatal road crash counts during the rush hour period. Method: We merged eight-year (2010 to 2017) data from the Fatality Analysis Reporting System. We limited the data to crashes during the rush hour period (6 to 9 am; 3 to 7 pm). The outcome variable was the counts of fatal crashes per county. The predictor variables were road design (intersection, driveway, ramp, workzone), road type (interstate, highways, roads/streets), and inclement weather factors (rain, fog, snow). A nested spatial negative binomial regression model was used to estimate the rate ratio of fatal crash injury during the rush hour period, with estimated county population sizes used as the offset variable. Small area estimates, adjusted crash fatality rates, clusters, and outliers were visualized using choropleths maps. Results: The median prevalence of rush hour related fatal crashes was 7.3 per 100,000 population. Case specific fatality rates from interstates, highways, roads, streets, intersections, rain, fog, and snow were higher than the median fatality rates. Also, the median crash fatality rates were significantly higher in rural counties as compared to urban counties. During the rush hour period, fatal crash injury rates were significantly elevated on interstates, highways, roads and streets, intersections, driveways, and work zones. Further, rain and fog were significantly associated with elevated fatal crash rates during the rush-hour period. Spatial clusters of fatal crash injuries were found in counties located in Idaho, Montana, Nevada, California, Wyoming, Utah, and across a few states in the Southeast. Conclusion: Certain built, and natural road environment factors may influence crash injury rates during the rush hour period.

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Contributing factors to vehicle to vehicle crash frequency and severity under rainfall

Cited by 51 publications

References 22 publications

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Effects of fog, driver experience and gender on driving behavior on S-curved road segments

Impact of Pavement Surface Condition on Roadway Departure Crash Risk in Iowa

Road environment characteristics and fatal crash injury during the rush hour period in the U.S.: Model testing and nested analytical study

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