• Proceedings of the KOR-KST Conference
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• 1997.12a
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• pp.33-69
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• 1997

This study was conducted to develop a methodology to predict utility pole accident rates and to evaluate cost-effectiveness for safety improvement for utility pole accidents. The utility pole accident rate prediction model was based on the encroachment rate approach introduced in the Transportation Research Board Special Report 214. The utility pole accident rate on a section of highway depends on the roadside encroachment rate and the lateral extent of encroachment. The encroachment rate is influenced by the horizontal and vertical alignment of the highway as well as traffic volume and mean speed. The lateral extent of encroachment is affected by the horizontal and vertical alignment, the mean speed and the roadside slope. An analytical method to generate the probability distribution function for the lateral extent of encroachment was developed for six kinds of encroachment types by the horizontal alignment and encroachment direction. The encroachment rate was calibrated with the information on highway and roadside conditions and the utility pole accident records collected on the sections of 55mph speed limit of the State Trunk Highway 12 in Wisconsin. The encroachment rate on a tangent segment was calibrated as a function of traffic volume with the actual average utility pole accident rates by traffic volume strategies. The adjustment factors for horizontal and vertical alignment were then derived by comparing the actual average utility pole accident rates to the estimations from the model calibrated for tangent and level sections. A computerized benefit-cost analysis procedure was then developed as a means of evaluating alternative countermeasures. The program calculates the benefit-cost ratio and the percent of reduction of utility pole accidents resulting from the implementation of a safety improvement. This program can be used to develop safety improvement: alternatives for utility pole accidents when a predetermined performance level is specified.

• Proceedings of the KOR-KST Conference
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• 1997.12b
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• pp.35-72
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• 1997

This study was conducted to develop a methodology to predict utility pole accident rates and to evaluate cost-effectiveness for safety improvement for utility pole accidents. The utility pole accident rate prediction model was based on the encroachment rate approach introduced in the Transportation Research Board special Report 214. The utility pole accident rate on a section of highway depends on the roadside encroachment rate and the lateral extent of encroachment. The encroachment rate is influenced by the horizontal and vertical alignment of the highway as well as traffic volume and mean speed. The lateral extent of encroachment is affected by the horizontal and vertical alignment, the mean speed and the roadside slope. An analytical method to generate the probability distribution function for the lateral extent of encroachment was developed for six kinds of encroachment types by the horizontal alignment and encroachment direction. The encroachment rate was calibrated with the information on highway and roadside conditions and the utility pole accident records collected on the sections of 55mph speed limit of the State Trunk Highway 12 in Wisconsin. The encroachment rate on tangent segment was calibrated as a function of traffic volume with the actual average utility pole accident rates by traffic volume strategies. The adjustment factors for horizontal and vertical alignment were when derived by comparing the actual average utility pole accident rates to the estimations from the model calibrated for tangent and level sections. A computerized benefit-cost analysis procedure was then developed as a means of evaluating alternative countermeasures. The program calculates the benefit-cost ratio and the percent of reduction of utility pole accidents resulting from the implementation of a safety improvement. This program can be used to develop safety improvement alternatives for utility pole accidents when a predetermined performance level is specified.

• International Journal of Highway Engineering
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• v.14 no.1
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• pp.85-94
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• 2012

The term "incident duration time" is defined as the time from the occurrence of incident to the completion of the handling process. Reductions in incident durations minimize damages by traffic accidents. This study aims to develop models to identify factors that influence incident duration by investigating traffic accidents on highways. For this purpose, four models were established including an integrated model (Model 1) incorporating all accident data and detailed models (Model 2, 3 and 4) analyzing accidents by location such as basic section, bridges and tunnels. The result suggested that the location of incident influences incident duration and the time of arrival of accident treatment vehicles is the most sensitive factor. Also, significant implications were identified with regard to vehicle to vehicle accidents and accidents by trucks, in night or in weekends. It is expected that the result of this study can be used as important information to develop future policies to manage traffic accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.5
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• pp.1135-1146
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• 2015

This thesis aims to improve the safety of bicycle traffic for activating the use of bicycle, main means of non-powered and non-carbon transportation in order to cope with worldwide crisis such as climate change and energy depletion and to implement sustainable traffic system. In this regard, I analyzed the problem of bicycle roads currently installed and operated, and developed the bicycle accident forecasting model. Following are the processes for this. First, this study presented the current status of bicycle road in Korea as well as accident data, collect the data on bicycle traffic accidents generated throughout the country for recent 3 years (2009~2011) and analyzed the features of bicycle traffic accidents based on the data. Second, this study selected the variable affecting the number of bicycle accidents through accident feature analysis of bicycle accidents at Jeollanam-do, and developed accident forecast model using the multiple regression analysis of 'SPSS Statistics 21'. At this time, the number of accidents due to extension per road types (crossing, crosswalk, other single road) was used. To verify the accident forecast model deduced, this study used the data on bicycle accident generated in Gwangju, 2011, and compared the prediction value with actual number of accidents. As a result, it was found out that reliability of accident forecast model was secured through reconciling with actual number of cases except certain data. Third, this study carried out field survey on the bicycle road as well as questionnaire on satisfaction of bicycle road and use of bicycle for analysis of bicycle road problems, and presented safety improvement measures for the problems deduced as well as bicycle activation plans. This study is considered to serve as the fundamental data for planning and reorganizing of bicycle road in the future, and expected to improve safety of bicycle users and to promote activation of bicycle use as the means of transportation.

• Journal of Korean Society of Transportation
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• v.14 no.2
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• pp.223-237
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• 1996

Based on the traffic and accident data collected on a 4.2km (2.6mile) section of Interstate highway 35W in Minneapolis the relationship between traffic operation variables and safety measures is investigated. An aggregate specification that could be integrated into an urban freeway safety prediction methodology is proposed as a multiple regression model. The specification includes lane occupancy and volume data, which are the control parameters commonly used because they can be measured in real time. The primary variables that appear to affect the safety of urban freeway are : vehicle-miles of travel, entrance ramp volumes and the dynamic effect of queue building. The potential benefits of freeway traffic control strategies on freeway safety are also investigated via a simulation study. It was concluded that improvement of urban freeway safety is achievable by traffic control strategies which homogenize traffic conditions areound critical occupancy values.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.1-12
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• 2017

Traffic accidents are caused by a combination of human factors, vehicle factors, and environmental factors. In the case of traffic accidents where rental cars are involved, the possibility and the severity of traffic accidents are expected to be different from those of other traffic accidents due to the unfamiliar environment of the driver. In this study, we developed a model to forecast the severity of rental car accidents by using Naive Bayes classifier for Busan, Gangneung, and Jeju city. In addition, we compared the prediction accuracy performance of two models where one model uses the variables of which statistical significance were verified in a prior study and another model uses the entire available variables. As a result of the comparison, it is shown that the prediction accuracy is higher when using the variables with statistical significance.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.101-116
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• 2012

Accident prediction models have been utilized to predict accident possibilities in existing or projected freeways and to evaluate programs or policies for improving safety. In this study, a traffic accident prediction model for freeways was developed for the above purposes. When selecting variables for the model, the highest priority was on the ease of both collecting data and applying them into the model. The dependent variable was set as the number of total accidents and the number of accidents including casualties in the unit of IC(or JCT). As a result, two models were developed; the overall accident model and the casualty-related accident model. The error structure adjusted to each model was the negative binomial distribution and the Poisson distribution, respectively. Among the two models, a more appropriate model was selected by statistical estimation. Major nine national freeways were selected and five-year dada of 2003~2007 were utilized. Explanatory variables should take on either a predictable value such as traffic volumes or a fixed value with respect to geometric conditions. As a result of the Maximum Likelihood estimation, significant variables of the overall accident model were found to be the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volume to the number of curved segments between ICs(or JCTs). For the casualty-related accident model, the link length between ICs(or JCTs), the daily volumes(AADT), and the ratio of bus volumes had a significant impact on the accident. The likelihood ratio test was conducted to verify the spatial and temporal transferability for estimated parameters of each model. It was found that the overall accident model could be transferred only to the road with four or more than six lanes. On the other hand, the casualty-related accident model was transferrable to every road and every time period. In conclusion, the model developed in this study was able to be extended to various applications to establish future plans and evaluate policies.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.135-148
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• 2007

Understanding the relation between characteristics of an accident and its duration is crucial for the efficient response of accidents and the reduction of total delay caused by accidents. Thus the objective of this study is to model accident duration using an AFT metric model. Although the log-logistic and log-normal AFT models were selected based on the previous studies and statistical theory, the log-logistic model was better fitted. Since the AFT model is commonly used for the purpose of prediction, the estimated model can be also used for the prediction of duration on freeways as soon as the base accident information is reported. Therefore, the predicted information will be directly useful to make some decisions regarding the resources needed to clear accident and dispatch crews as well as will lead to less traffic congestion and much saving the injured.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.173-185
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• 2015

This study aims to investigate the characteristics and types of car accidents and establish a prediction model by analyzing 456 car accidents having occurred in the 11 tunnels in Busan, through statistical analysis techniques. The results of this study can be summarized as below. As a result of analyzing the characteristics of car accidents, it was found that 64.9% of all the car accidents took place in the tunnels between 08:00 and 18:00, which was higher than 45.8 to 46.1% of the car accidents in common roads. As a result of analyzing the types of car accidents, the car-to-car accident type was the majority, and the sole-car accident type in the tunnels was relatively high, compared to that in common roads. Besides, people at the age between 21 and 40 were most involved in car accidents, and in the vehicle type of the first party to car accidents, trucks showed a high proportion, and in the cloud cover, rainy days or cloudy days showed a high proportion unlike clear days. As a result of analyzing the principal components of car accident influence factors, it was found that the first principal components were road, tunnel structure and traffic flow-related factors, the second principal components lighting facility and road structure-related factors, the third principal factors stand-by and lighting facility-related factors, the fourth principal components human and time series-related factors, the fifth principal components human-related factors, the sixth principal components vehicle and traffic flow-related factors, and the seventh principal components meteorological factors. As a result of classifying car accident spots, there were 5 optimized groups classified, and as a result of analyzing each group based on Quantification Theory Type I, it was found that the first group showed low explanation power for the prediction model, while the fourth group showed a middle explanation power and the second, third and fifth groups showed high explanation power for the prediction model. Out of all the items(principal components) over 0.2(a weak correlation) in the partial correlation coefficient absolute value of the prediction model, this study analyzed variables including road environment variables. As a result, main examination items were summarized as proper traffic flow processing, cross-section composition(the width of a road), tunnel structure(the length of a tunnel), the lineal of a road, ventilation facilities and lighting facilities.

• 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.