Collision modification functions: Incorporating changes over time

Sacchi, Emanuele; Sayed, Tarek; El-Basyouny, Karim

doi:10.1016/j.aap.2014.03.003

Cited by 29 publications

(10 citation statements)

References 14 publications

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“…The demonstration of crash hazards can help visualize the treatment effects and track the trend of crash hazard. Sacchi, Sayed, and El-Basyouny (2014) found the treatment effectiveness could change over time and they proposed to use collision modification functions (CMFs) to represent this change. The crash hazard curves in Fig.…”

Section: Modeling Resultsmentioning

confidence: 99%

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: Modeling Resultsmentioning

confidence: 99%

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

“…Recently, researchers have also introduced the use of the full Bayesian (FB) method to evaluate the effect of safety countermeasures (8)(9)(10)(11)(12)(13)(14). The FB approach was shown to have several advantages over the EB approach, including the ability to account for greater uncertainty in the data; provide more detailed inference; allow inference at more than one level for hierarchical models; and integrate the estimation of the safety model and treatment effects efficiently in a single step, while these are separate tasks in the EB method (15).…”

Section: Safety Evaluation Methodsmentioning

confidence: 99%

“…The two parameters ω and ω* are impact multipliers, which account for the intervention effects across treated and comparison sites respectively, while δ is a decay parameter controlling the rate at which the weights decline. An extensive review of the Koyck model applied to road safety can be found in other works (13,14,16). Recently, a comparison of several Bayesian evaluation techniques has shown the advantages of using the nonlinear intervention model for BA studies (15).…”

Section: Estimating the Safety Impactsmentioning

confidence: 99%

Evaluating Safety Benefits of the Insurance Corporation of British Columbia Road Improvement Program Using a Full Bayes Approach

Sayed

Sacchi

deLeur³

2016

Transportation Research Record

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The objective of this study was to conduct a time-series (before-to-after) evaluation of the safety performance of a sample of locations that have been improved under the Insurance Corporation of British Columbia (ICBC) Road Improvement Program. The program started in 1989 when ICBC established partnerships with local road authorities in British Columbia, Canada, and works cooperatively to make sound investments in road safety improvements. The overall effectiveness of the road improvement program was assessed by determining whether the frequency or severity of collisions at the improvement sites has been reduced after implementation of the improvement, and by quantifying the program costs versus the economic safety benefits to determine the return on ICBC’s road safety investment. Seventy-two urban intersections were included in the evaluation. The methodology adopted for estimating the safety benefits was a before–after study with the full Bayesian method, while the benefit–cost analysis was carried out by using two indicators: net present value and benefit–cost ratio (B/C) with a payback period of 5 years. Overall, the total reductions of severe (fatal plus injury) and nonsevere (property damage only) collision frequency for intersections with new pedestrian signal installations were found equal to −24.54% and −6.21%, respectively; −22.95% and −10.78%, respectively, for intersections with geometric design improvements; and −13.76% and −5.04%, respectively, for intersections with traffic signal upgrades. Finally, an overall B/C ratio of 4.32:1 was achieved.

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“…It was concluded that the CMFunctions can be used to identify general relationships between the CMFs and the roadway characteristics. Sacchi et al (2014) also claimed that using a single value of CMF may not be suitable to represent the variation in safety effects of the treatment over time. Thus, the authors developed CMFunctions to incorporate changes over time for the safety effectiveness of treatment.…”

Section: Development and Estimation Of Cmfunctionsmentioning

confidence: 97%

“…To overcome this limitation, crash modification functions (CMFunctions) have been developed to predict the variation in CMFs based on the site characteristics. However, although previous studies (Elvik, 2009(Elvik, , 2011(Elvik, , 2013Park et al, 2014;Sacchi et al, 2014) assessed the effect of a specific single variable such as AADT on the CMFs, there is a lack of prior studies on variation in the safety effects of adding a bike lane among treated sites with different multiple roadway characteristics. Also, although socioeconomic characteristics such as population density and bike commuter rate of the treated sites are potentially associated with bike travel patterns, their effects on crashes have not been investigated.…”

Section: Introductionmentioning

confidence: 93%

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Park

Abdel-Aty

Lee

et al. 2015

Accident Analysis & Prevention

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Collision modification functions: Incorporating changes over time

Cited by 29 publications

References 14 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

Evaluating Safety Benefits of the Insurance Corporation of British Columbia Road Improvement Program Using a Full Bayes Approach

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

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