scholarly journals Safety Analysis of Interchange Functional Areas

2018 ◽  
Vol 2672 (30) ◽  
pp. 120-130
Author(s):  
Akinfolarin Abatan ◽  
Peter T. Savolainen
Keyword(s):  
Random Effects ◽  
Traffic Control ◽  
Negative Binomial ◽  
Research Literature ◽  
Safety Performance ◽  
Random Effects Models ◽  
Traffic Crash ◽  
Design Alternatives ◽  
Traffic Conflicts ◽  
Functional Areas

Limited access facilities, such as freeways and expressways, are generally designed to the highest standards among public roads. Consequently, these facilities demonstrate crash, injury, and fatality rates that are significantly lower than other road facility types. However, these rates are generally elevated in the immediate vicinity of interchanges because of increases in traffic conflicts precipitated by weaving, merging, and diverging traffic. Given the extensive costs involved in interchange construction, it is important to discern the expected operational and safety impacts of various design alternatives. To this end, the objective of this study was to analyze safety performance within the functional areas of interchanges. The study involves the integration of traffic crash, volume, and roadway geometric data from 2010 to 2014 in the state of Iowa. Separate analyses were conducted for the freeway mainline and ramp connections. A series of safety performance functions (SPFs) were estimated for both the mainline and ramps. Random effects negative binomial models were estimated, which account for correlation in crash counts at the same location over time. The results show the frequency of crashes to vary based on traffic volume, interchange configuration, speed limit, and traffic control at the ramp terminal. The random effects models are shown to significantly outperform pooled models, which suggest there are several important location-specific factors that are not included in the analysis dataset. The SPFs from this study are also compared with several reference models from the extant research literature.

scholarly journals Within- and Between-cluster Effects in Generalized Linear Mixed Models: A Discussion of Approaches and the Xthybrid command

2017 ◽  
Vol 17 (1) ◽  
pp. 89-115 ◽  
Author(s):  
Reinhard Schunck ◽  
Francisco Perales
Keyword(s):  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Negative Binomial ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Consistent Estimation ◽  
Random Effects Models ◽  
Fixed Effects Models ◽  
Correlated Random Effects

One typically analyzes clustered data using random- or fixed-effects models. Fixed-effects models allow consistent estimation of the effects of level-one variables, even if there is unobserved heterogeneity at level two. However, these models cannot estimate the effects of level-two variables. Hybrid and correlated random-effects models are flexible modeling specifications that separate within-and between-cluster effects and allow for both consistent estimation of level-one effects and inclusion of level-two variables. In this article, we elaborate on the separation of within- and between-cluster effects in generalized linear mixed models. These models present a unifying framework for an entire class of models whose response variables follow a distribution from the exponential family (for example, linear, logit, probit, ordered probit and logit, Poisson, and negative binomial models). We introduce the user-written command xthybrid, a shell for the meglm command. xthybrid can fit a variety of hybrid and correlated random-effects models.

Safety Evaluation and Improvements for Highway Intersections

2008 ◽  
Vol 2060 (1) ◽  
pp. 46-52 ◽  
Author(s):  
Li Yuan ◽  
Jian Lu
Keyword(s):  
Traffic Control ◽  
Cost Benefit Analysis ◽  
Safety Evaluation ◽  
Cost Benefit ◽  
Safety Performance ◽  
Crash Analysis ◽  
Traffic Crash ◽  
Crash Data ◽  
Intersection Safety ◽  
Traffic Signal Timing

Intersection safety is one of the most important issues in transportation. Traffic crash analysis—the most popular method to evaluate or assess the safety performance of an intersection—has been used for a long time. However, it is based on a lot of crash data, which need to be accumulated over a long period. In addition, traffic crashes sometimes occur randomly as a result of human driving behavior. Therefore, without sufficient data and crash history, traffic crash analysis may not give an overall evaluation of an intersection's safety performance. This paper introduces an approach to evaluating highway intersection safety performance. It is fully based on the existing conditions of the intersection, including geometrics, sight distance, pavement surface conditions, traffic control devices, traffic signal timing, and phasing. The non-accident-based approach is based on field surveys under the conditions mentioned previously. The approach will also result in a safety index to indicate the safety performance of the intersection. Corresponding countermeasures are ranked and recommended based on cost–benefit analysis. This paper is based on research results from part of a project (entitled Safety Design of Highway Intersections) sponsored by the China Department of Transportation. In this paper, the approach (called a diagnostic approach) is practically applied to evaluate the safety performance of some intersections in Shan Dong Province. Results from the real application indicate that the approach has good applicability and can be used by field safety engineers in real applications.

Safety Performance of One-Way Arterials

2020 ◽  
Vol 2674 (10) ◽  
pp. 548-559
Author(s):  
Srinivas R. Geedipally ◽  
Dominique Lord ◽  
Michael P. Pratt ◽  
Kay Fitzpatrick ◽  
Eun Sug Park
Keyword(s):  
Predictive Models ◽  
Traffic Control ◽  
Injury Severity ◽  
Negative Binomial ◽  
Prediction Models ◽  
Distribution Functions ◽  
Safety Performance ◽  
Safety Performance Function ◽  
Traffic Characteristics ◽  
Wide Range

Safety analysts are generally interested in understanding the differences in the safety performance when a two-way street is converted to a one-way operation or vice-versa. Literature exists to understand and predict the safety of two-way streets. However, safety prediction procedures are currently not available for assessing the safety performance of one-way arterials. This research was undertaken to develop safety prediction models for one-way arterials. To accomplish this objective, data collected in California, Illinois, Michigan, Oregon, and Texas were assembled that included a wide range of geometric design features, traffic control features, traffic characteristics, and crash records. The data were used to calibrate predictive models, each of which included a safety performance function (SPF) and several crash modification factors (CMFs). Separate SPFs were developed for fatal and injury crashes (i.e., fatal, incapacitating injury, non-incapacitating injury, and possible injury crash) and property-damage-only crashes. The SPFs were estimated using the negative binomial modeling structure. Severity distribution functions (SDFs) were also calibrated using the fatal and injury data. These functions can be used with the predictive models to estimate the expected crash frequency for each of four injury severity levels.

scholarly journals Performance of Right-Turn Lane Designs at Intersections

2021 ◽  
Author(s):  
Satish Ukkusuri ◽  
Lu Ling ◽  
Tho V. Le ◽  
Wenbo Zhang
Keyword(s):  
Influencing Factors ◽  
Traffic Safety ◽  
Traffic Control ◽  
Negative Binomial ◽  
Random Effect ◽  
Research Literature ◽  
Crash Risk ◽  
Related Factors ◽  
Crash Frequency ◽  
The Impact

Right-turn lane (RTL) crashes are among the most key contributors to intersection crashes in the US. Different right turn lanes based on their design, traffic volume, and location have varying levels of crash risk. Therefore, engineers and researchers have been looking for alternative ways to improve the safety and operations for right-turn traffic. This study investigates the traffic safety performance of the RTL in Indiana state based on multi-sources, including official crash reports, official database, and field study. To understand the RTL crashes' influencing factors, we introduce a random effect negative binomial model and log-linear model to estimate the impact of influencing factors on the crash frequency and severity and adopt the robustness test to verify the reliability of estimations. In addition to the environmental factors, spatial and temporal factors, intersection, and RTL geometric factors, we propose build environment factors such as the RTL geometrics and intersection characteristics to address the endogeneity issues, which is rarely addressed in the accident-related research literature. Last, we develop a case study with the help of the Indiana Department of Transportation (INDOT). The empirical analyses indicate that RTL crash frequency and severity is mainly influenced by turn radius, traffic control, and other intersection related factors such as right-turn type and speed limit, channelized type, and AADT, acceleration lane and AADT. In particular, the effects of these factors are different among counties and right turn lane roadway types.

scholarly journals Safety Evaluation of Freeway Speed-Change Lanes Based on Crashes and Simulated Conflicts

2021 ◽  
Author(s):  
Lei Qin
Keyword(s):  
Traffic Control ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Negative Binomial ◽  
Prediction Models ◽  
Control Strategies ◽  
Safety Evaluation ◽  
Safety Performance ◽  
Assessment Model ◽  
Crash Prediction Models

To facilitate the evaluation of the safety performance of freeway merge, diverge, and weave areas, conventional crash-based Safety Performance Functions (SPFs) were developed using generalized linear models (GLM) with a negative binomial (NB) error structure. However, crash-based SPFs may not take into account all factors that contribute to the crashes. The use of simulated conflicts as a surrogate safety measure to predict crashes can address this issue and provide recommendations for the designs and traffic control strategies. This approach was explored by using Surrogate Safety Assessment Model (SSAM) and VISSIM software to generate and analyze conflicts for merge areas on Ontario freeways. Crash-conflict integrated SPFs with different Time to Collision (TTC) thresholds were then developed and compared. Their predictive capabilities were also evaluated. To complement this analysis, the transferability of US crash prediction models to Ontario data was evaluated and the goodness-of-fit of these models was explored.

scholarly journals Safety Evaluation of Freeway Speed-Change Lanes Based on Crashes and Simulated Conflicts

2021 ◽  
Author(s):  
Lei Qin
Keyword(s):  
Traffic Control ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Negative Binomial ◽  
Prediction Models ◽  
Control Strategies ◽  
Safety Evaluation ◽  
Safety Performance ◽  
Assessment Model ◽  
Crash Prediction Models

To facilitate the evaluation of the safety performance of freeway merge, diverge, and weave areas, conventional crash-based Safety Performance Functions (SPFs) were developed using generalized linear models (GLM) with a negative binomial (NB) error structure. However, crash-based SPFs may not take into account all factors that contribute to the crashes. The use of simulated conflicts as a surrogate safety measure to predict crashes can address this issue and provide recommendations for the designs and traffic control strategies. This approach was explored by using Surrogate Safety Assessment Model (SSAM) and VISSIM software to generate and analyze conflicts for merge areas on Ontario freeways. Crash-conflict integrated SPFs with different Time to Collision (TTC) thresholds were then developed and compared. Their predictive capabilities were also evaluated. To complement this analysis, the transferability of US crash prediction models to Ontario data was evaluated and the goodness-of-fit of these models was explored.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

scholarly journals Dyslipidaemia and mortality in COVID-19 patients - a meta-analysis

QJM ◽  
2021 ◽  
Author(s):  
Marco Zuin ◽  
Gianluca Rigatelli ◽  
Claudio Bilato ◽  
Carlo Cervellati ◽  
Giovanni Zuliani ◽  
...  
Keyword(s):  
Random Effects ◽  
Mortality Risk ◽  
Meta Analysis ◽  
Random Effect ◽  
Final Analysis ◽  
Short Term ◽  
Random Effects Models ◽  
Pooled Prevalence ◽  
Statistical Heterogeneity ◽  
Trim And Fill

Abstract Objective The prevalence and prognostic implications of pre-existing dyslipidaemia in patients infected by the SARS-CoV-2 remain unclear. To perform a systematic review and meta-analysis of prevalence and mortality risk in COVID-19 patients with pre-existing dyslipidaemia. Methods Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in abstracting data and assessing validity. We searched MEDLINE and Scopus to locate all the articles published up to January 31, 2021, reporting data on dyslipidaemia among COVID-19 survivors and non-survivors. The pooled prevalence of dyslipidaemia was calculated using a random effects model and presenting the related 95% confidence interval (CI), while the mortality risk was estimated using the Mantel-Haenszel random effects models with odds ratio (OR) and related 95% CI. Statistical heterogeneity was measured using the Higgins I2 statistic. Results Eighteen studies, enrolling 74.132 COVID-19 patients [mean age 70.6 years], met the inclusion criteria and were included in the final analysis. The pooled prevalence of dyslipidaemia was 17.5% of cases (95% CI: 12.3-24.3%, p < 0.0001), with high heterogeneity (I2=98.7%). Pre-existing dyslipidaemia was significantly associated with higher risk of short-term death (OR: 1.69, 95% CI: 1.19-2.41, p = 0.003), with high heterogeneity (I2=88.7%). Due to publication bias, according to the Trim-and-Fill method, the corrected random-effect ORs resulted 1.61, 95% CI 1.13-2.28, p < 0.0001 (one studies trimmed). Conclusions Dyslipidaemia represents a major comorbidity in about 18% of COVID-19 patients but it is associated with a 60% increase of short-term mortality risk.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

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