Effect of Roadside Features on Single-Vehicle Roadway Departure Crashes on Rural Two-Lane Roads

2012 ◽  
Vol 2309 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Yichuan Peng ◽  
Srinivas Reddy Geedipally ◽  
Dominique Lord
Keyword(s):  
Negative Binomial ◽  
Motor Vehicle ◽  
Large Body ◽  
Crash Frequency ◽  
Side Slope ◽  
Single Vehicle ◽  
Lateral Clearance ◽  
Slope Condition ◽  
The Impact ◽  
The Relationship

One of the most important tasks in traffic safety is investigating the relationship between motor vehicle crashes and the geometric characteristics of roadways. A large body of previous work provides meaningful results on the impact of geometric design on crash frequency. However, little attention has been paid to the relationship between roadway departure crashes and relevant roadside features such as lateral clearance, side slope condition, and driveway density. The lack of roadside data for use in estimating rigorous statistical models has been a major obstacle to roadside safety research for many years. This study investigated the relationship between single-vehicle roadway departure crashes and roadside features. Two types of models were developed: a negative binomial model of crash frequency and a multinomial logit model of crash severity. The study used field data collected in four districts in Texas. The results showed that shoulder width, lateral clearance, and side slope condition had a significant effect on roadway departure crashes. Crash frequency and severity increased when lateral clearance or shoulder width decreased and when the side slope condition became worse. Driveway density was not found to have a significant influence on crash frequency or severity.

Case Study using Probe Vehicle Speeds to Assess Roadway Safety in Georgia

2020 ◽  
Vol 2674 (11) ◽  
pp. 554-562
Author(s):  
David J. Ederer ◽  
Michael O. Rodgers ◽  
Michael P. Hunter ◽  
Kari E. Watkins
Keyword(s):  
Performance Monitoring ◽  
Negative Binomial ◽  
Performance Metrics ◽  
Large Body ◽  
Vehicle Speed ◽  
Crash Frequency ◽  
Data Set ◽  
Probe Vehicle ◽  
Performance Metric ◽  
The Relationship

Speed is a primary risk factor for road crashes and injuries. Previous research has attempted to ascertain the relationship between individual vehicle speeds, aggregated speeds, and crash frequency on roadways. Although there is a large body of research linking vehicle speeds to safety outcomes, there is not a widely applied performance metric for safety based on regularly reported speeds. With the increasingly widespread availability of probe vehicle speed data, there is an opportunity to develop network-level safety performance metrics. This analysis examined the relationship between percentile speeds and crashes on a principal arterial in Metropolitan Atlanta. This study used data from the National Performance Metric Research Data Set (NPMRDS), the Georgia Electronic Accident Reporting System, and the Highway Performance Monitoring System. Negative binomial regression models were used to analyze the relationship between speed percentiles, and speed differences to crash frequency on roadway sections. Results suggested that differences in speed percentiles, a measure of speed dispersion, are related to the frequency of crashes. Based on the models, the difference in the 85th percentile and median speed is proposed as a performance metric. This difference is easily measured using NPMRDS probe vehicle speeds, and provides a practical performance metric for assessing safety on roadways.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

Modelling patterns of parasite aggregation in natural populations: trichostrongylid nematode–ruminant interactions as a case study

Parasitology ◽  
1995 ◽  
Vol 111 (S1) ◽  
pp. S135-S151 ◽  
Author(s):  
B. T. Grenfell ◽  
K. Wilson ◽  
V. S. Isham ◽  
H. E. G. Boyd ◽  
K. Dietz
Keyword(s):  
Population Biology ◽  
Negative Binomial ◽  
Natural Populations ◽  
Parasite Load ◽  
Host Immunity ◽  
Moment Closure ◽  
The Mean ◽  
Parasite Aggregation ◽  
The Impact ◽  
The Relationship

SUMMARYThe characteristically aggregated frequency distribution of macroparasites in their hosts is a key feature of host–parasite population biology. We begin with a brief review of the theoretical literature concerning parasite aggregation. Though this work has illustrated much about both the sources and impact of parasite aggregation, there is still no definitive analysis of both these aspects. We then go on to illustrate the use of one approach to this problem – the construction of Moment Closure Equations (MCEs), which can be used to represent both the mean and second moments (variances and covariances) of the distribution of different parasite stages and phenomenological measures of host immunity. We apply these models to one of the best documented interactions involving free-living animal hosts – the interaction between trichostrongylid nematodes and ruminants. The analysis compares patterns of variability in experimental infections of Teladorsagia circumcincta in sheep with the equivalent wildlife situation – the epidemiology of T. circumcincta in a feral population of Soay sheep on St Kilda, Outer Hebrides. We focus on the relationship between mean parasite load and aggregation (inversely measured by the negative binomial parameter, k) for cohorts of hosts. The analysis and empirical data indicate that k tracks the increase and subsequent decline in the mean burden with host age. We discuss this result in terms of the degree of heterogeneity in the impact of host immunity or parasite-induced mortality required to shorten the tail of the parasite distribution (and therefore increase k) in older animals. The model is also used to analyse the relationship between estimated worm and egg counts (since only the latter are often available for wildlife hosts). Finally, we use these results to review directions for future work on the nature and impact of parasite aggregation.

scholarly journals Initial impact of COVID-19’s stay-at-home order on motor vehicle traffic and crash patterns in Connecticut: an interrupted time series analysis

2020 ◽  
pp. injuryprev-2020-043945
Author(s):  
Mitchell L Doucette ◽  
Andrew Tucker ◽  
Marisa E Auguste ◽  
Amy Watkins ◽  
Christa Green ◽  
...  
Keyword(s):  
Time Series ◽  
Motor Vehicle ◽  
Interrupted Time Series ◽  
Data Repository ◽  
Interrupted Time Series Analysis ◽  
Crash Rates ◽  
Single Vehicle ◽  
The Mean ◽  
The Impact ◽  
At Home

IntroductionUnderstanding how the COVID-19 pandemic has impacted our health and safety is imperative. This study sought to examine the impact of COVID-19’s stay-at-home order on daily vehicle miles travelled (VMT) and MVCs in Connecticut.MethodsUsing an interrupted time series design, we analysed daily VMT and MVCs stratified by crash severity and number of vehicles involved from 1 January to 30 April 2017, 2018, 2019 and 2020. MVC data were collected from the Connecticut Crash Data Repository; daily VMT estimates were obtained from StreetLight Insight’s database. We used segmented Poisson regression models, controlling for daily temperature and daily precipitation.ResultsThe mean daily VMT significantly decreased 43% in the post stay-at-home period in 2020. While the mean daily counts of crashes decreased in 2020 after the stay-at-home order was enacted, several types of crash rates increased after accounting for the VMT reductions. Single vehicle crash rates significantly increased 2.29 times, and specifically single vehicle fatal crash rates significantly increased 4.10 times when comparing the pre-stay-at-home and post-stay-at-home periods.DiscussionDespite a decrease in the number of MVCs and VMT, the crash rate of single vehicles increased post stay-at-home order enactment in Connecticut after accounting for reductions in VMT.

Safety Effects of Horizontal Curve Reliability Index

2020 ◽  
Vol 2674 (9) ◽  
pp. 627-636
Author(s):  
Scott Himes ◽  
Eric Donnell
Keyword(s):  
Reliability Index ◽  
Horizontal Curve ◽  
Mass Model ◽  
Crash Frequency ◽  
Horizontal Curves ◽  
Run Off ◽  
Single Vehicle ◽  
Green Book ◽  
Total Curve ◽  
The Relationship

Recent advancements in analytical processes have used probabilistic approaches to examine the efficacy of the point mass model (and other Green Book models) to develop reliability-based approaches for geometric design. However, there has been minimal research establishing the link between reliability measures and substantive safety (expressed through crash frequency). The objective of this paper is to use empirical data supporting the calculation of reliability index for existing horizontal curves and to estimate the relationship between reliability index and crash frequency. Other horizontal curve-related characteristics that may have an impact on crash frequency on horizontal curves for rural two-lane highways and rural freeway facilities are controlled for in the evaluation. The safety analysis showed that the wet pavement reliability index was significantly associated with crash frequency for total curve-related crashes, single-vehicle run-off-road crashes, rollover crashes, truck-related crashes, and weather-related crashes. The relationship was strongest for the reliability index in its continuous form, meaning that the effect is continuous across the range of wet pavement reliability that was observed.

Effect of Median Design on Rural Freeway Safety

2008 ◽  
Vol 2060 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Andrew P. Tarko ◽  
Natalie M. Villwock ◽  
Nicolas Blond
Keyword(s):  
Opposite Direction ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Well Being ◽  
Crash Severity ◽  
Before And After ◽  
Single Vehicle ◽  
Median Width ◽  
The Impact

Although median barriers are an absolute means of preventing drivers from crossing road medians and colliding with vehicles moving in the opposite direction, they may cause additional crashes. This perhaps complex safety effect of median barriers has not been investigated well. Being able to predict the safety impact of most types of median barriers on rural freeways is becoming more desirable because some state departments of transportation plan to expand many of their four-lane rural freeways to six lanes to accommodate increases in traffic volume. Realistic crash prediction models sensitive to the median design would provide the needed guidance useful in designing adequate median treatments on widened freeways. The impact of median designs on crash frequency was investigated in this study through negative binomial regression and before-and-after studies based on data collected in eight participating states. The impact on crash severity was investigated with a logit model. The separate effects of changes in median geometry were quantified for single-vehicle, multiple-vehicle same direction, and multiple-vehicle opposite direction crashes. The results were significantly different and indicated that reducing the median width without adding barriers (the remaining median width is still reasonably wide) increases the severity of crashes, particularly opposite direction crashes. Further, reducing the median and installing concrete barriers eliminates opposite direction crashes but doubles the frequency of single-vehicle crashes and tends to lessen the frequency of same direction crashes. The crash severity also tends to increase.

The relationship between citations, downloads and alternative metrics in rheumatology publications: a bibliometric study

Rheumatology ◽  
2019 ◽  
Vol 59 (2) ◽  
pp. 277-280 ◽  
Author(s):  
Winnie M Y Chen ◽  
Marwan Bukhari ◽  
Francesca Cockshull ◽  
James Galloway
Keyword(s):  
Negative Binomial ◽  
Positive Association ◽  
Negative Binomial Regression ◽  
Significant Positive Association ◽  
H Index ◽  
Binomial Regression ◽  
Number Of Citations ◽  
The Impact ◽  
The Relationship ◽  
Alternative Metrics

Abstract Objective Scientific journals and authors are frequently judged on ‘impact’. Commonly used traditional metrics are the Impact Factor and H-index. However, both take several years to formulate and have many limitations. Recently, Altmetric—a metric that measures impact in a non-traditional way—has gained popularity. This project aims to describe the relationships between subject matter, citations, downloads and Altmetric within rheumatology. Methods Data from publications in Rheumatology were used. Articles published from 2010 to 2015 were reviewed. Data were analysed using Stata 14.2 (StataCorp, College Station, TX, USA). Correlation between citations, downloads and Altmetric were quantified using linear regression, comparing across disease topics. Relationship between downloads and months since publications were described using negative binomial regression, clustering on individual articles. Results A total of 1460 Basic Science and Clinical Science articles were identified, with the number of citations, downloads and Altmetric scores. There were no correlations between disease topic and downloads (R2 = 0.016, P = 0.03), citations (R2 = 0.011, P = 0.29) or Altmetric (R2 = 0.025, P = 0.02). A statistically significant positive association was seen between the number of citations and downloads (R2 = 0.29, P < 0.001). No correlations were seen between Altmetric and downloads (R2 = 0.028, P < 0.001) or citations (R2 = 0.004, P = 0.445). Conclusion Disease area did not correlate with any of the metrics compared. Correlations were apparent with clear links between downloads and citations. Altmetric identified different articles as high impact compared with citation or download metrics. In conclusion: tweeting about your research does not appear to influence citations.

scholarly journals Urban Road Crashes and Weather Conditions: Untangling the Effects

2019 ◽  
Vol 11 (11) ◽  
pp. 3176 ◽  
Author(s):  
António Lobo ◽  
Sara Ferreira ◽  
Isabel Iglesias ◽  
António Couto
Keyword(s):  
Negative Binomial ◽  
Weather Conditions ◽  
Crash Risk ◽  
Traffic Crash ◽  
Road Crashes ◽  
Road Users ◽  
Poisson Models ◽  
Adverse Weather ◽  
Single Vehicle ◽  
The Impact

Most previous studies show that inclement weather increases the risk of road users being involved in a traffic crash. However, some authors have demonstrated a little or even an opposite effect, observed both on crash frequency and severity. In urban roads, where a greater number of conflict points and heavier traffic represent a higher exposure to risk, the potential increase of crash risk caused by adverse weather deserves a special attention. This study investigates the impact of meteorological conditions on the frequency of road crashes in urban environment, using the city of Porto, Portugal as a case study. The weather effects were analyzed for different types of crashes: single-vehicle, multi-vehicle, property-damage-only, and injury crashes. The methodology is based on negative binomial and Poisson models with random parameters, considering the influence of daily precipitation and mean temperature, as well as the lagged effects of the precipitation accumulated during the previous month. The results show that rainy days are more prone to the occurrence of road crashes, although the past precipitation may attenuate such effect. Temperatures below 10 °C are associated with higher crash frequencies, complying with the impacts of precipitation in the context of the Portuguese climate characteristics.

Impact of Congestion and Traffic Flow on Crash Frequency and Severity: Application of Smartphone-Collected GPS Travel Data

2017 ◽  
Vol 2659 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Joshua Stipancic ◽  
Luis Miranda-Moreno ◽  
Nicolas Saunier
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Quebec City ◽  
Crash Severity ◽  
Crash Frequency ◽  
Crash Data ◽  
Instrumented Vehicles ◽  
Minor Injuries ◽  
The Impact ◽  
The Relationship

Mobility and safety are the two greatest priorities within any transportation system. Ideally, traffic flow enhancement and crash reductions could occur simultaneously, although their relationship is likely complex. The impact of traffic congestion and flow on road safety requires more empirical evidence to determine the direction and magnitude of the relationship. The study of this relationship is an ideal application for instrumented vehicles and surrogate safety measures (SSMs). The purpose of this paper is to correlate quantitative measures of congestion and flow derived from smartphone-collected GPS data with collision frequency and severity at the network scale. GPS travel data were collected in Quebec City, Quebec, Canada, and the sample for this study contained data for more than 4,000 drivers and 20,000 trips. The extracted SSMs, the congestion index (CI), average speed ( V), and the coefficient of variation of speed (CVS) were compared with crash data collected over an 11-year period from 2000 to 2010 with the use of Spearman’s correlation coefficient and pairwise Kolmogorov–Smirnov tests. The correlations with crash frequency were weak to moderate. CI was shown to be positively correlated with crash frequency, and the relationship to crash severity was found to be nonmonotonous. Higher congestion levels were related to crashes with major injuries, whereas low congestion levels were related to crashes with minor injuries and fatalities. Surprisingly, V was found to be negatively correlated with crash frequency and had no conclusive statistical relationship to crash severity. CVS was positively correlated with crash frequency and statistically related to increased crash severity. Future work will focus on the development of a network screening model that incorporates these SSMs.

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