• International Journal of Highway Engineering
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• v.10 no.4
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• pp.53-63
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• 2008

Even though accident frequencies in roadway segments have been decreasing since 2000, there has been increasing the number of vehicle crashes at intersections. Due to this increase, safety problems at intersection recently started to be regarded as significant issues. The purpose of this study is to analyze the effects of road conditions, traffic operational conditions, and other influencing condition on intersection safety. Then a traffic accident frequency prediction model to evaluate the safety at intersections was developed based on the correlations between influencing factors and vehicle crashes. In this research, critically significant factors affecting vehicle crashes at rural four-legs signalized intersections were investigated. It was found that Poisson regression was the best fit method to developing a accident frequency modeling using the collected data in this study. Through this study, it was concluded that exclusive left turn lane, crosswalk, posted speed, lighting, angle, and ADT are significant influencing factors on the intersection safety.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.266-279
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• 2014

For the evaluation of roadway safety, diverse methods, including before-after studies, simple comparison using historic traffic accident data, methods based on experts' opinion or literature, have been applied. Especially, many research efforts have developed traffic accident prediction models in order to identify critical elements causing accidents and evaluate the level of safety. A traffic accident prediction model must secure predictability and transferability. By acquiring the predictability, the model can increase the accuracy in predicting the frequency of accidents qualitatively and quantitatively. By guaranteeing the transferability, the model can be used for other locations with acceptable accuracy. To this end, traffic accident prediction models using non-linear regression, artificial neural network, and structural equation were developed in this study. The predictability and transferability of three models were compared using a model development data set collected from 90 signalized intersections and a model validation data set from other 33 signalized intersections based on mean absolute deviation and mean squared prediction error. As a result of the comparison using the model development data set, the artificial neural network showed the highest predictability. However, the non-linear regression model was found out to be most appropriate in the comparison using the model validation data set. Conclusively, the artificial neural network has a strong ability in representing the relationship between the frequency of traffic accidents and traffic and road design elements. However, the predictability of the artificial neural network significantly decreased when the artificial neural network was applied to a new data which was not used in the model developing.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.662-674
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• 2014

In this study, accident frequency prediction models were constructed by collecting variables such as geometric structures, safety facilities, traffic volume and weather conditions, land use, highway design-satisfaction criteria along 780km (4,372 sections) of 4 lane-highways over 8 areas. As for models, a fixed parameter model and a random parameter model were employed. In the random parameter model, some influences were reversed as the range was expressed based on specific probability in the case of no fixed coefficients. In the fixed parameter model, the influences of independent variables on accident frequency were interpreted by using one coefficient, but in the random parameter model, more various interpretations were took place. In particular, curve radius, securement of shoulder lane, vertical grade design criteria satisfaction showed both positive and negative influence, according to specific probability. This means that there could be a reverse effect depending on the behavioral characteristics of drivers and the characteristics of highway sections. Rather, they influence the increase of accident frequency through the all sections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.54-63
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• 2014

Traffic accident frequency and severity level in Korea are known to be very serious. Especially the number of pedestrian fatalities was much worse and 1.6 time higher than the OECD average. According to the National Police Agency, the flash signals are reported to have many safety benefits as well as travel time reduction, which is opposed to the foreign studies. With this background of expanding the flash signal, this research aims to investigate the overall impact of the flash signal operation on safety, investigating and comparing the accident occurrence on the flash signal and the full signal intersections. For doing this accident prediction models for both flash and full signal intersections were estimated using independent variables (geometric features and traffic volume) and 3-year (2011-2013) accident data collected in Seoul. Considering the rare and random nature of accident occurrence and overdispersion (variance > mean) of the data, the negative binomial regression model was applied. As a result, installing wider crosswalk and increasing the number of pedestrian push buttons seemed to increase the safety of the flash signal intersections. In addition, the result showed that the average accident occurrence at the flash signal intersections was higher than at the full signal-operated intersections, 9% higher with everything else the same.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.137-145
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• 2012

According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.145-155
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• 2012

At present, Seoul City is putting the bus exclusive lane system into practice according to mass transit revitalization policy. Starting with the installation of roadside bus exclusive lane in the past, at present, even the road sections for central- lane bus exclusive lane system are on the increase. The purpose of this research is to analyze the factors giving impacts on bus accident on central bus exclusive lane and roadside bus exclusive lane. In case of the central bus exclusive lane, the 6 variables, such as the number of bus routes, number of access & entrance to central lanes patterns, whether the stop line of central lanes retreats or not, separated distance between the stop line of central lanes and crosswalks, traffic volume, and number of bus routes stopping at bus stops on reversible lanes, were found to have a significant influence on bus accidents. In case of roadside bus exclusive lane sections, the four variables such as the number of right-turn bus routes, whether to be chronic illegal parking & stopping, time for the walk signal, and forms of land use, etc. were found to have a significant influence on bus accident.