Examining the effects of site selection criteria for evaluating the effectiveness of traffic safety countermeasures

Lord, Dominique; Kuo, Pei Fen

doi:10.1016/j.aap.2011.12.008

Cited by 38 publications

(20 citation statements)

References 23 publications

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“…We should be careful when using the survival analysis method if treated sites are highly ranked hotspots in the before period, due to the RTM and site selection effects. However, it should be noted that according to the simulation study in Section 3.4, the consequence of the RTM and site selection effects gets smaller as the effect of site‐specific unobserved heterogeneity increases, which is consistent with the finding in Lord and Kuo (). On the other hand, temporal changes of unobserved factors could lead to the fluctuation of crash observations over time.…”

Section: Discussionsupporting

confidence: 85%

“…When the dispersion

κ = 0.6

, the true CRRs are within 1 SD of the means estimated by the SARE model. Lord and Kuo () also found that the reduction in bias was associated with the increase in the dispersion parameter. The survival analysis can be an acceptable method even for treated sites top ranked in crash frequency when the dispersion of crash distribution is high and the unobserved heterogeneity effect dominates.…”

Section: Validation Of Survival Analysismentioning

confidence: 87%

“…This indicates that in the EB and FB analyses, improper selection of reference sites could lead to biased statistical inferences. Lord and Kuo () also found that when the reference sites did not have the same sample mean as the treated sites, it would lead to estimation bias. In scenario 3, where the number of reference sites is restricted, the means of CRRs estimated by the FB method are still close to the true values.…”

Section: Validation Of Survival Analysismentioning

confidence: 99%

“…Without addressing the RTM issue, the naïve method is likely to confound the natural variation with the true treatment effects. In addition, treated sites are generally not selected randomly but based on certain entry criteria, and this could lead to site selection bias (Davis, 2000;Kuo & Lord, 2017;Lord & Kuo, 2012;Wood & Donnell, 2017). Entry criteria would transfer the original population distribution to a truncated sample distribution with different parameters and thus the naïve method could result in biased estimation of safety effectiveness (Lord & Kuo, 2012).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

Xie

Özbay

Yang³

et al. 2018

Risk Analysis

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The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.

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Section: Discussionsupporting

confidence: 85%

“…When the dispersion

κ = 0.6

Section: Validation Of Survival Analysismentioning

confidence: 87%

Section: Validation Of Survival Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

Xie

Özbay

Yang³

et al. 2018

Risk Analysis

View full text Add to dashboard Cite

show abstract

“…One widely applied approach to help performing both tasks is the popular empirical Bayesian (EB) method. This method can correct for the regression-to-the mean bias (if the site selection bias is not present, see (14)), refines the predicted mean of an entity, and is relative simple to manipulate compared to the fully Bayesian approach (1). As discussed by Hauer (15,16) and Cheng and Washington (17), the EB method rests on two assumptions: (1) at a given location, crash occurrence obeys the Poisson probability law with mean  ; (2) the probability distribution of the  's of the population of sites is gamma distributed.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Sichel and Negative Binomial Models in Estimating Empirical Bayes Estimates

Zou

Lord

Zhang

et al. 2013

Transportation Research Record: Journal of the Transportation R

Self Cite

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Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites and to identify hotspots locations.The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash prediction models, and (2) the observed number of crashes at individual sites. Crash prediction models have extensively been estimated using a negative binomial (NB) modeling framework due to the over-dispersion commonly found in crash data.Recent studies have shown that the Sichel (SI) distribution provides a promising avenue for developing crash prediction models. The objective of this study is to examine the application of the SI model in calculating EB estimates. To accomplish the objective of the study, the SI models with a fixed/varying dispersion term are developed using the crash data collected at 4-lane undivided rural highways in Texas. The important conclusions can be summarized as follows: (1) the selection of the crash prediction model (i.e., the SI or NB model) will affect the value of weight factor used for estimating the EB output; (2) the identification of hazardous sites, using the EB method, can be different when the SI model is used. Finally, a simulation study designed to examine which crash prediction model can better identify the hotspot is recommended as our future research.Zou et al.

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Assessing the safety effect of red-light camera deactivation: a geographically weighted negative binomial regression approach

Silva

2022

Comput.Urban Sci.

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Municipalities across the country have debated the safety effect of automatic red-light cameras (RLC) and their political and financial implications. Most empirical studies have used the Empirical Bayesian (EB) approach to assess the safe effects to facilitate policy debates. While popular, the EB method has several limitations in data requirement, reference site selection, and control of confounding factors. Moreover, empirical studies of the RLC deactivation effects are limited. This study fills these gaps using the Moran’s I statistic and the Geographically Weighted Negative Binomial Regression (GWNBR) approach for data in the City of Arlington, Texas. The results indicate that the total, injury, and angle crashes in Arlington are on the rise over the study period and that crashes are higher at RLC deactivation intersections than those at other intersections. The direct safety effect of removing RLCs is statistically significant. The spillover effect is observed but statistically insignificant. Speed limit plays an important role in road safety. The findings have significant implications for safety research and practices.

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Examining the effects of site selection criteria for evaluating the effectiveness of traffic safety countermeasures

Cited by 38 publications

References 23 publications

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

Comparison of Sichel and Negative Binomial Models in Estimating Empirical Bayes Estimates

Assessing the safety effect of red-light camera deactivation: a geographically weighted negative binomial regression approach

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