Development of Crash Reduction Factors: Methods, Problems, and Research Needs

2003 ◽  
Vol 1840 (1) ◽  
pp. 50-56 ◽  
Author(s):  
Joan Shen ◽  
Albert Gan
Keyword(s):  
Empirical Bayes ◽  
Group Method ◽  
Research Needs ◽  
Regression To The Mean ◽  
Before And After ◽  
Safety Studies ◽  
Development Methods ◽  
Crash Reduction ◽  
Before And After Study ◽  
Reduction Factors

Crash reduction factors (CRFs) are used in highway safety studies to predict safety benefits due to reduced numbers of crashes. NCHRP Report 162 identified the need for the development of a national comprehensive set of CRFs for each state to evaluate safety improvements. However, this need has not been met. As a result, many states have developed their own CRFs or have adopted CRFs from other states. A synthesis of the CRF development methods, the associated problems, and the research needs are provided. The emphasis is placed on the before-and-after study method because it has been, and still is, the method of choice for CRF development. Three before-and-after study methods were introduced and reviewed: ( a) the simple before-and-after method, ( b) the before-and-after study with comparison group method, and ( c) the before-and-after study with the empirical Bayes method. The problems associated with the simple before-and-after studies, including regression to the mean, crash migration, maturation, and external causal factor, are discussed. Several research needs related to crash migration and general CRF development are also identified. The information presented in the synthesis will be useful to states that plan to develop or update their CRFs.

scholarly journals Evaluating the Safety Impacts of Increased Speed Limits on Freeways in Kansas Using Before-And-After Study Approach

2018 ◽  
Vol 11 (1) ◽  
pp. 119 ◽  
Author(s):  
Reza Shirazinejad ◽  
Sunanda Dissanayake ◽  
Ahmed Al-Bayati ◽  
David York
Keyword(s):  
Traffic Safety ◽  
Economic Benefits ◽  
Group Method ◽  
Speed Limit ◽  
Speed Limits ◽  
Study Approach ◽  
Highway Safety Manual ◽  
Before And After ◽  
Crash Modification Factors ◽  
Before And After Study

In the summer of 2011, a change in the Kansas laws came into effect, increasing the speed limit on a selected set of freeway sections from 70 mph to 75 mph. Higher speeds were thought to have economic benefits, mostly because the travel time reduction means people reach their destinations more quickly. In this study, the sections where the speed limits remained unchanged, are compared to freeway sections that have been influenced by speed limit increase, to evaluate safety effectiveness. The study utilizes the before-and-after study with comparison group method to assess the safety effects provided in the Highway Safety Manual (HSM). Two crash datasets, obtained by considering three years before and three years after the speed limit increase, were compared in order to evaluate the safety effects of the speed limit change. The crash modification factors (CMFs) were estimated, which showed that there was a 27% increase in total crashes and a 35% increase in fatal and injury crashes across all sections after the speed limit change, and these increases were statistically significant at 95% confidence level. These confounding results show that the speed limit increase has not been beneficial for traffic safety in Kansas, and hence it is important to be cautious in such future situations. Also, additional data have been presented which would be beneficial in identifying and understanding any behavior change in drivers following a speed limit increase.

Long-Term Effect of Universal Helmet Law Changes on Motorcyclist Fatal Crashes: Comparison Group and Empirical Bayes Approaches

2017 ◽  
Vol 2637 (1) ◽  
pp. 27-37 ◽  
Author(s):  
Jaeyoung Lee ◽  
Mohamed Abdel-Aty ◽  
Jung-Han Wang ◽  
Chanyoung Lee
Keyword(s):  
Empirical Bayes ◽  
Brain Injuries ◽  
Comparison Group ◽  
The United States ◽  
Regression To The Mean ◽  
Fatal Crashes ◽  
Before And After ◽  
The Law ◽  
1960S And 1970S

A motorcyclist helmet is considered important safety equipment because it prevents or minimizes head and brain injuries, which are often fatal. Hence, in the 1960s and 1970s, most of the states in the United States enacted the universal helmet law (UHL) requiring all motorcyclists to wear helmets. Many researchers have examined the effect of the helmet law changes by using before-and-after studies and found that repealing the law had a negative effect on motorcyclists. In this study, the authors have attempted to explore the long-term impacts of repeal and reinstatement of the UHL by using 13 to 16 years of data. A before-and-after study with a comparison group and empirical Bayes methods was adopted to account for the passage of time and its effect on other factors such as exposure, maturation, trend, and regression-to-the-mean bias. A range of safety performance functions was developed on the basis of counties and parishes, and the expected fatal motorcycle crashes were calculated. The results showed that the UHL repeal still had significant effects on motorcycle fatal crash counts even 7 to 12 years after the repeal of the law. The crash modification factors showed that the UHL repeal increased the number of motorcycle fatal crashes by 15% to 41%, whereas reinstatement of the UHL decreased it by 21% to 27%. It is expected that the results from this study could be helpful for state policy makers to clearly understand the effects of the UHL on reducing motorcycle fatal crashes.

Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

2017 ◽  
Vol 2659 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Passant Reyad ◽  
Emanuele Sacchi ◽  
Shewkar Ibrahim ◽  
Tarek Sayed
Keyword(s):  
Road Safety ◽  
Empirical Bayes ◽  
Comparison Group ◽  
Treatment Effectiveness ◽  
Video Data ◽  
Safety Performance ◽  
Group Method ◽  
Safety Level ◽  
Before And After ◽  
Traffic Conflict

Road safety evaluations mainly rely on the analysis of crash data that are challenged by well-recognized availability and quality issues. The statistical models used to predict the safety level of road sites—that is, safety performance functions—have recently been successfully developed with the use of traffic conflict observations instead of crashes. As such, it is possible to adopt and transfer the statistical techniques used in crash-based road safety analysis to conflict-based analysis. The use of statistically rigorous techniques in crash-based before-and-after (BA) studies is essential for evaluation of the effectiveness of road safety countermeasures. In particular, the use of Bayesian methods, such as the empirical Bayes (EB) technique, is vital to control for confounding factors that can operate simultaneously with the countermeasure and may affect road safety performance. The main objective of this paper was to estimate the treatment effectiveness of two traffic signal (visibility) improvement projects in the city of Edmonton, Alberta, Canada, with a conflict-based BA study using the comparison group and the EB methods. More than 300 h of video data with traffic conflict observations was automatically collected and analyzed by computer vision techniques for two treatment intersections and two control (untreated) intersections before and after the signal improvement projects. The results of the comparison group method showed a statistically significant 24% reduction in the average number of rear-end conflicts per hour, whereas the EB method showed a statistically significant 24.5% reduction in the average number of total conflicts per hour.

scholarly journals Comparison of Crash Modification Factors for Engineering Treatments Estimated by Before–After Empirical Bayes and Propensity Score Matching Methods

2020 ◽  
pp. 036119812095377
Author(s):  
Bo Lan ◽  
Raghavan Srinivasan
Keyword(s):  
Propensity Score ◽  
Regression Models ◽  
Empirical Bayes ◽  
Regression To The Mean ◽  
Cross Sectional ◽  
Matching Methods ◽  
Before And After ◽  
Crash Modification Factors ◽  
The Mean ◽  
Cross Sectional Studies

Cross-sectional and the empirical Bayes (EB) before–after are two of the most common methods for estimating crash modification factors (CMFs). The EB before–after method has now been accepted as one way of addressing the potential bias caused by the regression to the mean problem. However, sometimes before–after methods may not feasible because of the lack of data from before and after periods. In those cases, researchers rely on cross-sectional studies to develop CMFs. However, cross-sectional studies may provide biased CMFs through confounding. The propensity score (PS) matching method, along with cross-sectional regression models, is one of the methods that can be used to address confounding. Though PS methods are widely used in epidemiology and other studies, there are only a few studies that have used PS matching methods to estimate CMFs. The intent of this study is to evaluate and compare the performance of cross-sectional regression models using PS matching methods with the results from the EB and traditional cross-sectional methods. The comparisons were conducted using two carefully selected simulated datasets. The results indicate that optimal propensity score distance (PSD) matching with maximum variable ratio of 5 performed quite well compared with the EB before–after and the traditional cross-sectional methods.

National-Level Safety Evaluation of Diverging Diamond Interchanges

2019 ◽  
Vol 2673 (7) ◽  
pp. 696-708
Author(s):  
Timothy S. Nye ◽  
Christopher M. Cunningham ◽  
Elizabeth Byrom
Keyword(s):  
Empirical Bayes ◽  
Comparison Group ◽  
National Level ◽  
Group Analysis ◽  
Safety Evaluation ◽  
The United States ◽  
Group Method ◽  
Regression To The Mean ◽  
Naive Method ◽  
Diamond Interchanges

A national-level safety evaluation of Diverging Diamond Interchanges (DDIs) in the United States was completed. This study aimed to update previous evaluations and to expand the treatment group size of previous studies to provide a more robust and reliable safety assessment of DDI deployments. For this particular treatment, it was determined that, of the observational before-and-after evaluation methodologies, the comparison group approach yields the best evaluation results. The naïve method can be influenced by outside factors that cannot be accounted for (weather, crash reporting tendencies, etc.). The empirical Bayes method is unnecessary as DDIs are installed for operational benefits, meaning that risk of selection bias and regression-to-the-mean is minimal. This study recommends a total crashes crash modification factor (CMF) of 0.633 based on the comparison group analysis of 26 DDIs in 11 states. The comparison group method was also applied to a variety of crash variables for this study. Angle, rear-end, and sideswipe crashes were found to have CMFs of 0.441, 0.549, and 1.139, respectively. Fatal-and-injury crashes provided a CMF of 0.461. Daytime and nighttime crashes provided CMFs of 0.648 and 0.638, respectively.

Safety Effects of Flashing Yellow Arrows Used in Protected Permitted Phasing: Comparison of Full Bayes and Empirical Bayes Results

2018 ◽  
Vol 2672 (21) ◽  
pp. 20-29 ◽  
Author(s):  
Justice Appiah ◽  
F. Adam King ◽  
Michael D. Fontaine ◽  
Benjamin H. Cottrell
Keyword(s):  
Empirical Bayes ◽  
Safety Evaluation ◽  
Left Turn ◽  
Flashing Yellow Arrow ◽  
Before And After ◽  
Credible Intervals ◽  
Degree Of Confidence ◽  
Crash Reduction ◽  
Better Than ◽  
Bayes Approach

Using the flashing yellow arrow (FYA) signal indication for the permissive portion of protected-permissive left-turn (PPLT) phasing has become an increasingly popular treatment for left-turn signals as drivers are believed to understand the FYA better than the traditional circular green indication. A before-and-after safety evaluation of deploying FYA at PPLT signals at 28 intersections in Virginia was conducted. Each of the study intersections had FYA for the permitted portion of the phase on at least one left-turn approach. The focus was on left-turns that operated in the protected-permissive mode (with circular green indication for the permissive portion) before being converted to PPLT operations with the FYA indication for the permissive portion (PPLT-FYA). Crash records from before and after the activation of FYA were compared using both the full Bayes and empirical Bayes approaches. The results indicate that using the FYA signal indication instead of the circular green indication had a statistically significant effect in reducing overall frequency and severity of crashes. For the intersections studied in this research, total crashes reduced by 12% following conversion from PPLT to PPLT–FYA. The results also indicated that the full Bayes approach to safety effectiveness evaluation can, at a minimum, provide similar results to the well-established empirical Bayes approach. The 95% credible intervals for the expected crash reduction rates estimated with the full Bayes method were generally narrow, suggesting a good degree of confidence in the estimates.

Effects on Accidents of Automatic Speed Enforcement in Norway

1997 ◽  
Vol 1595 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Rune Elvik
Keyword(s):  
Percent Level ◽  
Regression To The Mean ◽  
Property Damage ◽  
Accident Rate ◽  
Before And After ◽  
The Mean ◽  
Photo Radar ◽  
Speed Enforcement ◽  
Before And After Study

Automatic speed enforcement by means of photo radar was introduced in Norway in 1988. The results of a before-and-after study of the effects of automatic speed enforcement on accidents are reported in this paper. The study controlled for general trends in the number of accidents and regression to the mean. A statistically significant reduction of 20 percent in the number of injury accidents was found. The number of property-damage-only accidents was reduced by 12 percent. This change was not statistically significant at the 5 percent level. The effect of automatic speed enforcement on the number of injury accidents varied according to the level of conformance with official warrants for its use. The warrants refer to accident rate (accidents per vehicle kilometer) and accident density (accidents per kilometer of road). A decline of 26 percent in injury accidents was found on road sections conforming with both warrants. On road sections not conforming with any of the warrants, injury accidents declined by 5 percent. The results of this study confirm the results of previous studies of the effects of automatic speed enforcement on accidents.

Only problem gamblers spend less money when loot boxes are removed from a game: A before and after study of Heroes of the Storm

2019 ◽  
Author(s):  
David Zendle
Keyword(s):  
Video Games ◽  
Problem Gambling ◽  
Video Game ◽  
Real World ◽  
Complex Pattern ◽  
Problem Gamblers ◽  
Before And After ◽  
Gambling Severity ◽  
Reliable Correlation ◽  
Before And After Study

Loot boxes are items in video games that may be paid for with real-world money, but which contain randomised contents. There is a reliable correlation between loot box spending and problem gambling severity: The more money gamers spend on loot boxes, the more severe their problem gambling tends to be. However, it is unclear whether this link represents a case in which loot box spending causes problem gambling; a case in which the gambling-like nature of loot boxes cause problem gamblers to spend more money; or whether it simply represents a case in which there is a general dysregulation in in-game spending amongst problem gamblers, nonspecific to loot boxes.The multiplayer video game Heroes of the Storm recently removed loot boxes. In order to better understand links between loot boxes and problem gambling, we conducted an analysis of players of Heroes of the Storm (n=112) both before and after the removal of loot boxes.There were a complex pattern of results. In general, when loot boxes were removed from Heroes of the Storm, problem gamblers appeared to spend significantly less money in-game in contrast to other groups. These results suggest that the presence of loot boxes in a game may lead to problem gamblers spending more money in-game. It therefore seems possible that links between loot box spending and problem gambling are not due to a general dysregulation in in-game spending amongst problem gamblers, but rather are to do with specific features of loot boxes themselves.

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