scholarly journals Regression to the mean correction for Collision Modification Factors

2021 ◽  
Author(s):  
Bernard James
Keyword(s):  
Standard Deviation ◽  
Road Safety ◽  
Relative Number ◽  
Highway Safety ◽  
Regression To The Mean ◽  
Simple Method ◽  
Highway Safety Manual ◽  
The Mean ◽  
Site Population

Collision Modification Factors (CMFs) are a simple method of representing the effectiveness of road safety treatments. With the release of the Highway Safety Manual (HSM) and the recent launching of a CMF Clearinghouse website, CMFs are likely to become more widely used for estimating the effects of potential road safety treatments. The presence of regression to the mean (RTM) bias has long been shown to affect the accuracy of CMFs that did not account for the RTM in their development. The purpose of this research was to study how the RTM depends on the number of years of data used for selecting high collision sites for treatment and on the relative number of sites selected. From this analysis, a function based on the number of years, percentage of high collision sites selected, and the mean and standard deviation of the site population from which the treated sites are drawn was developed to more accurately estimate the magnitude of the RTM effect. This function can be used to adjust CMFs that do not account for RTM, complementing the procedure developed and used to correct CMFs included in the HSM.

scholarly journals Regression to the mean correction for Collision Modification Factors

2021 ◽  
Author(s):  
Bernard James
Keyword(s):  
Standard Deviation ◽  
Road Safety ◽  
Relative Number ◽  
Highway Safety ◽  
Regression To The Mean ◽  
Simple Method ◽  
Highway Safety Manual ◽  
The Mean ◽  
Site Population

Collision Modification Factors (CMFs) are a simple method of representing the effectiveness of road safety treatments. With the release of the Highway Safety Manual (HSM) and the recent launching of a CMF Clearinghouse website, CMFs are likely to become more widely used for estimating the effects of potential road safety treatments. The presence of regression to the mean (RTM) bias has long been shown to affect the accuracy of CMFs that did not account for the RTM in their development. The purpose of this research was to study how the RTM depends on the number of years of data used for selecting high collision sites for treatment and on the relative number of sites selected. From this analysis, a function based on the number of years, percentage of high collision sites selected, and the mean and standard deviation of the site population from which the treated sites are drawn was developed to more accurately estimate the magnitude of the RTM effect. This function can be used to adjust CMFs that do not account for RTM, complementing the procedure developed and used to correct CMFs included in the HSM.

scholarly journals Comparison of Different Road Segmentation Methods

2019 ◽  
Vol 31 (2) ◽  
pp. 163-172
Author(s):  
Maen Qaseem Ghadi ◽  
Árpád Török
Keyword(s):  
Road Safety ◽  
Road Network ◽  
Highway Safety ◽  
Segmentation Method ◽  
Constant Length ◽  
Highway Safety Manual ◽  
Segmentation Methods ◽  
Average Annual Daily Traffic ◽  
Clustering Approach ◽  
Road Segmentation

In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

scholarly journals A Simple Method to Retrieve Cloud Properties from Atmospheric Transmittance and Liquid Water Column Measurements

2011 ◽  
Vol 50 (2) ◽  
pp. 283-295 ◽  
Author(s):  
Salvador Matamoros ◽  
Josep-Abel González ◽  
Josep Calbó
Keyword(s):  
Standard Deviation ◽  
Optical Depth ◽  
Liquid Water ◽  
Surface Albedo ◽  
Effective Radius ◽  
Droplet Radius ◽  
Similar Data ◽  
Simple Method ◽  
The Mean ◽  
Atmospheric Transmittance

Abstract A deeper knowledge of the effects and interactions of clouds in the climatic system requires developing both satellite and ground-based methods to assess their optical properties. A simple method based on a parameterized inversion of a radiative transfer model is proposed to estimate the optical depth of thick liquid water clouds from the atmospheric transmittance at 415 nm, solar zenith angle, surface albedo, effective droplet radius, and aerosol load. When concurrent measurements of atmospheric transmittance and liquid water path are available, the effective radius of the droplet size distribution can also be retrieved. The method is compared with a reference algorithm from Min and Harrison, which uses similar data, except aerosol load. When applied to measurements performed at the Southern Great Plains site of the Atmospheric Radiation Measurement Program, the mean bias deviation between the proposed method and the reference method is only −0.08 in units of optical depth, whereas the standard deviation is only 0.46. For the effective droplet radius estimations, the mean bias deviation is −0.13 μm, and the standard deviation is 0.14 μm. Maximum relative deviations are lower than 5% and 8% for cloud optical depth and effective radius, respectively. The effects on these retrievals of the assumed aerosol optical depth and surface albedo are also analyzed.

scholarly journals Exploration Of Theoretical And Application Issues In Using Fully Bayesian Methods For Road Safety Analysis

2021 ◽  
Author(s):  
Bo Lan
Keyword(s):  
Treatment Effect ◽  
Road Safety ◽  
Safety Analysis ◽  
Regression To The Mean ◽  
Effect Analysis ◽  
Black Spots ◽  
Hypothetical Treatment ◽  
The Mean ◽  
Log Normal ◽  
Fully Bayesian

The Fully Bayesian (FB) approach to road safety analysis has been available for some time, but it is largely unevaluated and untested. This study is trying to bridge the gap by conducting a thorough evaluation of FB method for black spots identification and treatment effect analysis. First, an evaluation is conducted on the univariate FB versus the empirical Bayesian (EB) method for single level severity data through the development of various models, and multivariate FB versus univariate FB for multilevel severity data, as well as the performance of various ranking and evaluation criteria for black spots identification. It is confirmed that the FB method is superior to the EB with respect to key ranking criteria (expected rank, mode rank and median rank of posterior PM, etc.). The multivariate FB method is better than univariate FB for the multilevel severity crashes. Then a teat of the FB before-after method for treatment effect analysis is performed. Two FB testing frameworks were employed. First the univariate before-after fully Bayesian (FB) method was examined using three simulated datasets. Then multivariate Poisson log normal (MVPLN), univariate Poisson log normal (PLN) and PB (Poisson gamma) models were evaluated using two groups of California unsignalized intersections. Hypothetical treatment sites were selected from these datasets such that a significant effect would be estimated by the naive before-after method that does not account for regression to the mean. This study confirmed that FB methods can indeed provide valid results, in that they correctly estimate a treatment effect of zero at these hypothetical treatment sites after accounting for regression to the mean. Finally the EB and the validated FB before after methods were applied to evaluation of two treatments: the conversion of rural intersections from unsignalized to signalized control; and the conversion of road segments from a four-lane to a three-lane cross-section with two-way left turn lanes (also known as road diets). The result indicates that both FB and EB method can provide comparable treatment effect estimates. This would suggest it is still appropriate to conduct treatment effect analysis using the EB method for univariate crash data, but that it is essential in so doing to account for temporal trends in crash frequency.

Linear regression and correlation

2017 ◽  
Author(s):  
Andrew Gelman ◽  
Deborah Nolan
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Regression Line ◽  
World Population ◽  
Regression To The Mean ◽  
Straight Line ◽  
The Mean ◽  
Line Fitting ◽  
Single Predictor ◽  
Over Time

This chapter addresses the descriptive treatment of linear regression with a single predictor: straight-line fitting, interpretation of the regression line and standard deviation, the confusing phenomenon of “regression to the mean,” correlation, and conducting regressions on the computer. These concepts are illustrated with student discussions and activities. Many examples are of the sort commonly found in statistics textbooks, but the focus here is on how to work the examples into student-participation activities rather than simply examples to be read or shown on the blackboard. Topics include the following relationships: height and income, height and hand span, world population over time, and exam scores.

scholarly journals Calibration of the Highway Safety Manual and the SafetyAnalyst Methodologies for Ontario Highways

2021 ◽  
Author(s):  
Ali Sabbaghi
Keyword(s):  
Road Safety ◽  
Goodness Of Fit ◽  
Highway Safety ◽  
Safety Performance ◽  
Data Set ◽  
Rural Highways ◽  
Part C ◽  
Highway Safety Manual ◽  
Safety Performance Functions ◽  
Central Regions

SafetyAnalyst and the Highway Safety Manual (HSM) are two tools that are expected to revolutionize highway safety analyses. A key issue that allows SafetyAnalyst and HSM to become the new standards in road safety engineering is the calibration of their safety performance functions (SPFs) across time and jurisdictions. In this study, the methodologies of SafetyAnalyst and HSM are calibrated for Ontario to evaluate the effective transferability of their SPFs to local topographical conditions. A SafetyAnalyst calibration has been completed for Ontario highways and freeways, intersections, and ramps for six years (1998-2003) of traffic and accident counts. A data set which consists of 78 kilometres of rural two-lane two-way highways and 71 three- and four-legged stop controlled intersections located in the eastern and central regions of the Ministry of Transportation of Ontario (MTO) with six years (2002 to 2007) of traffic volume and collision counts has been used to evaluate the HSM SPFs to Ontario data. Several goodness-of-fit (GOF) measures are computed to assess the transferability and suitability of the crash models for applicability in Ontario. The study suggests that while most of the SafetyAnalyst SPFs for highways and ramps are not adaptable to Ontario data, the recalibrated SafetyAnalyst SPFs for intersections and also the recalibrated HSM Part C predictive models for two-lane rural highways and intersections provide satisfactory results in comparison to the crash models developed specifically for Ontario. Finally, this research highlights the substantial need for future improvements in data quality for more reliable safety performance estimations and evaluations.

A Simple Linear Discrimination Algorithm for AD Patients and Normal Controls

2012 ◽  
Vol 155-156 ◽  
pp. 18-22
Author(s):  
Yun Yi Yan ◽  
Guo Zhang Hu ◽  
Bao Long Guo ◽  
Yu Jie He
Keyword(s):  
Standard Deviation ◽  
Confidence Intervals ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Simple Method ◽  
Linear Discrimination ◽  
Significant Difference ◽  
The Mean ◽  
Normal Controls ◽  
Discrimination Method

One simple but effective discrimination method was presented in this paper to separate AD from normal controls. After detecting the thickness of cortex with highly significant difference, the mean and standard deviation of these vertices are computed to construct confidence intervals. We introduced one relax coefficients to control the width of intervals and by experiments the coefficients was optimized. Experiments results showed that using this simple method, the classification accuracy, sensitivity and specificity of Alzheimer’s disease versus normal controls could be as high as 85%, 88.89% and 93.84% respectively.

scholarly journals Superspreading of SARS-CoV-2 in the USA

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248808
Author(s):  
Calvin Pozderac ◽  
Brian Skinner
Keyword(s):  
United States ◽  
Standard Deviation ◽  
Early Time ◽  
The United States ◽  
Simple Method ◽  
Early Stages ◽  
Secondary Infections ◽  
The Usa ◽  
Mean And Variance ◽  
The Mean

A number of epidemics, including the SARS-CoV-1 epidemic of 2002-2004, have been known to exhibit superspreading, in which a small fraction of infected individuals is responsible for the majority of new infections. The existence of superspreading implies a fat-tailed distribution of infectiousness (new secondary infections caused per day) among different individuals. Here, we present a simple method to estimate the variation in infectiousness by examining the variation in early-time growth rates of new cases among different subpopulations. We use this method to estimate the mean and variance in the infectiousness, β, for SARS-CoV-2 transmission during the early stages of the pandemic within the United States. We find that σβ/μβ ≳ 3.2, where μβ is the mean infectiousness and σβ its standard deviation, which implies pervasive superspreading. This result allows us to estimate that in the early stages of the pandemic in the USA, over 81% of new cases were a result of the top 10% of most infectious individuals.

scholarly journals Calibration of the Highway Safety Manual and the SafetyAnalyst Methodologies for Ontario Highways

2021 ◽  
Author(s):  
Ali Sabbaghi
Keyword(s):  
Road Safety ◽  
Goodness Of Fit ◽  
Highway Safety ◽  
Safety Performance ◽  
Data Set ◽  
Rural Highways ◽  
Part C ◽  
Highway Safety Manual ◽  
Safety Performance Functions ◽  
Central Regions

SafetyAnalyst and the Highway Safety Manual (HSM) are two tools that are expected to revolutionize highway safety analyses. A key issue that allows SafetyAnalyst and HSM to become the new standards in road safety engineering is the calibration of their safety performance functions (SPFs) across time and jurisdictions. In this study, the methodologies of SafetyAnalyst and HSM are calibrated for Ontario to evaluate the effective transferability of their SPFs to local topographical conditions. A SafetyAnalyst calibration has been completed for Ontario highways and freeways, intersections, and ramps for six years (1998-2003) of traffic and accident counts. A data set which consists of 78 kilometres of rural two-lane two-way highways and 71 three- and four-legged stop controlled intersections located in the eastern and central regions of the Ministry of Transportation of Ontario (MTO) with six years (2002 to 2007) of traffic volume and collision counts has been used to evaluate the HSM SPFs to Ontario data. Several goodness-of-fit (GOF) measures are computed to assess the transferability and suitability of the crash models for applicability in Ontario. The study suggests that while most of the SafetyAnalyst SPFs for highways and ramps are not adaptable to Ontario data, the recalibrated SafetyAnalyst SPFs for intersections and also the recalibrated HSM Part C predictive models for two-lane rural highways and intersections provide satisfactory results in comparison to the crash models developed specifically for Ontario. Finally, this research highlights the substantial need for future improvements in data quality for more reliable safety performance estimations and evaluations.

scholarly journals Exploration Of Theoretical And Application Issues In Using Fully Bayesian Methods For Road Safety Analysis

2021 ◽  
Author(s):  
Bo Lan
Keyword(s):  
Treatment Effect ◽  
Road Safety ◽  
Safety Analysis ◽  
Regression To The Mean ◽  
Effect Analysis ◽  
Black Spots ◽  
Hypothetical Treatment ◽  
The Mean ◽  
Log Normal ◽  
Fully Bayesian

The Fully Bayesian (FB) approach to road safety analysis has been available for some time, but it is largely unevaluated and untested. This study is trying to bridge the gap by conducting a thorough evaluation of FB method for black spots identification and treatment effect analysis. First, an evaluation is conducted on the univariate FB versus the empirical Bayesian (EB) method for single level severity data through the development of various models, and multivariate FB versus univariate FB for multilevel severity data, as well as the performance of various ranking and evaluation criteria for black spots identification. It is confirmed that the FB method is superior to the EB with respect to key ranking criteria (expected rank, mode rank and median rank of posterior PM, etc.). The multivariate FB method is better than univariate FB for the multilevel severity crashes. Then a teat of the FB before-after method for treatment effect analysis is performed. Two FB testing frameworks were employed. First the univariate before-after fully Bayesian (FB) method was examined using three simulated datasets. Then multivariate Poisson log normal (MVPLN), univariate Poisson log normal (PLN) and PB (Poisson gamma) models were evaluated using two groups of California unsignalized intersections. Hypothetical treatment sites were selected from these datasets such that a significant effect would be estimated by the naive before-after method that does not account for regression to the mean. This study confirmed that FB methods can indeed provide valid results, in that they correctly estimate a treatment effect of zero at these hypothetical treatment sites after accounting for regression to the mean. Finally the EB and the validated FB before after methods were applied to evaluation of two treatments: the conversion of rural intersections from unsignalized to signalized control; and the conversion of road segments from a four-lane to a three-lane cross-section with two-way left turn lanes (also known as road diets). The result indicates that both FB and EB method can provide comparable treatment effect estimates. This would suggest it is still appropriate to conduct treatment effect analysis using the EB method for univariate crash data, but that it is essential in so doing to account for temporal trends in crash frequency.

Sign in / Sign up

Export Citation Format

Share Document