• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.91-102
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• 2009

A traffic accident prediction model developed using various design variables(road design variables, geometric variables, and traffic environmental variables) is one of the most important factors to safety design evaluation system for roads. However, statistical accident models have a crucial problem not applicable for all intersections. To make up this problem, this study developed AMFs(Accident Modification Factors) through statistical modeling methods, historical accident databases, judgment from traffic experts, and literature review by considering design variable's characteristics, traffic accident rates, and traffic accident frequency. AMFs developed in this study include exclusive left-turn lane, exclusive right-turn lane, sight distance, and intersection angle. Predictabilities of the developed AMFs and the existing accident prediction models are compared with real accident historical data. The results showed that performances of the developed AMFs are superior to the existing statistical accident prediction models. These findings show that AMFs should be considered as a important process to develop safety design evaluation algorithms. Additionally, AMFs could be used as an index that can judge the impact of corresponding design variables on accidents in rural intersections.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.131-143
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• 2010

This study has aimed to develop accident modification factor(AMF) for rural two-lane roadway segments. Accident Modification Factor is a coefficient to assess roadway safety as reflecting characteristics of homogeneous roadway. It estimates accident frequency of roadway segments with developed base model and exposure. We found on items of such factors as crosswalk, driveway density, topography characteristic, land use and median through statistical models and literature review. To develop accident modification factors, we used statistical model methods and analyses of applicability and expert judgement method were practiced to validate it. Although expert judgement for land use item was questionable, most items were rated acceptable. Result of comparative analysis revealed crash frequencies of IHSDM and KHSEM were most similar with actual. However, accident distribution of KHSEM was more proper than IHSDM. Also overall estimated values of RSDS were found to be overestimated.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.31-42
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• 2004

This study develops Accident Modification Factors (AMF) of countermeasures with Baysian method which are newly proposed for reducing Roadway-Rail grade crossing accidents. This study proposes a new "Bayesian Analytical Framework" for countermeasure assessment which combines "Subjective" Prior Information with "Logical" based Information. The newly proposed "Bayesian Analytical Framework" consists of the following three steps: The 1st step - Countermeasure Selection, Choice of Participants, Selection of Crashes; The 2nd step-Development of Crash History Manual and Countermeasure Evaluation Manual; The 3rd step-Development of AMFs through sound statistical tests. This study used the Komogorov-Smirnov(K-S) Test to determine whether two unknown distribution functions associated with the two populations are identical. The results of the study are that individual responses did not meet the K-S test of identical distributions. while individual vs. group distributions are identical. This indicates that combining the input of several people reduces the impact of individual subjectivity and assumptions and is important for developing a repeatable distribution to develop sound AMFs of countermeasures for reducing Roadway-Rail grade crossing accidents. The procedures of the AMF development conducted in this study can be used to estimate the safety effects of countermeasures for road segments and intersections, in addition to Roadway-Rail grade crossings.

• International Journal of Highway Engineering
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• v.15 no.6
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• pp.113-123
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• 2013

PURPOSES : Although numerous researches have been studied to reveal accident causations for road intersections, there are still many research gaps for road segments. It is mainly because of difficulty of data and lack of analytical method. This study aims to study accident causations for rural road segments and develop accident modification factors for safety evaluation. The accident modification factors can be used to improve road safety. METHODS : Methods for developing AMF are diverse. This study developed AMFs using accident prediction models and selected explanatory variables from the accident models. In order to select final AMFs, three different methods were applied in the study. RESULTS : As a result of the study, many AMFs such as horizontal curves or vertical curves were developed and explained the meanings of the results. CONCLUSIONS : This study introduced meaningful methods for developing significant AMFs and also showed several AMFs. It is expected that traffic or road engineers will be able to use the AMFs to improve road segment safety.