• Journal of Urban Science
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• v.6 no.2
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• pp.59-72
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• 2017

Studies on the calculation of accident costs include the approach on calculating damage costs covering all accidents regardless of first or secondary party and the one calculating damage costs generated by a single victim. These two approaches have a limitation of considering a subject for costs analysis as a single entity. In addition, research on estimating the interaction effects caused in the relationship between diverse traffic accident features and factors remains inadequate since most studies focused on calculating costs incurred in a single entity such as a victim, damaged building, or social organization in charge of managing car accident. This study intends to identify the expected range of old age where a specific interaction effect would remain, compare accidents between old age section and the entire age section, and discover an exogenous variable to be applied in accident drop effects in senior people and reduced benefits by calculating and testing additional accident costs in case the first party and the second party all pertain to the senior age section. By classifying the entire accidents caused by old drivers according to the types of cars, significant coefficients representing the influence that affects car accidents according to the characteristics are calculated and set them as the representative variables by selecting top variable in accordance with from low to high order. Furthermore, characteristics on five age groups such as a group of over 65 and less than 70, a group of over 70 and less than 75, a group of over 75 and less than 80, a group of over 80 and less than 85, and a group of over 85 are elicited and compared them with these preselected accident characteristics variables, thereby identifying what changing effects come out.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.851-865
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• 2019

Discovering various features from one time series is complicated. In this paper, we introduce a multi aggregation prediction algorithm (MAPA) that uses the concepts of temporal aggregation and combining forecasts to find multiple patterns from one time series and increase forecasting accuracy. Temporal aggregation produces multiple time series and each series has separate properties. We use exponential smoothing methods in the next step to extract various features of time series components in order to forecast time series components for each series. In the final step, we blend predictions of the same kind of components and forecast the target series by the summation of blended predictions. As an empirical example, we forecast traffic accident counts using MAPA and observe that MAPA performance is superior to conventional methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of the Korean Society of Marine Environment & Safety
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• v.7 no.2
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• pp.23-37
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• 2001

Marine environment has been polluted and damaged by many sources such as industrial waste, natural seepage, oil spill and offshore production etc. Among them, oil spill from oil tankers is one of most harmful sources to marine lives as its input amount is huge at a limited area for short period. In Korea, the port of Yeosu is known to be very vulnerable to oil spill with large amount of petroleum transported across the harbour. In this regards, the author analyzed and reviewed marine environment and appropriate response to marine oil spill at this area. For these purposes, oceanographic and meteorologic features as well as regional characteristics of harbor facilities, shipping routes, marine traffic and fisheries were investigated, also traffic densities were analyzed in order to check marine accident probabilities making direct observations for 72 hours visual1y and by portable radar.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.99-105
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• 2014

Since traffic accidents account for the highest proportion of the artificial disasters which occur in urban areas along with fire, more scientific an analysis on the causes of traffic accidents and various prevention measures against traffic accidents are needed. In this study, the research selected Jinju-si, which belongs to local small and medium-sized cities as a research target to analyze the characteristics of temporal and spacial distribution of traffic accidents by associating the data of traffic accidents, occurred in 2013 with the causes of traffic accidents and location information that includes occurrence time and seasonal features. It subsequently examines the spatial correlation between traffic accidents and the characteristics of urban space development according to the plans of land using. As a result, the characteristics of accident distribution according to the types of accidents reveal that side right-angle collisions (car versus car) and pedestrian-crossing accident (car versus man) showed the highest clustering in the density analysis and average nearest neighbor analysis. In particular, traffic accidents occurred the most on roads which connect urban central commercial areas, high-density residential areas, and industrial areas. In addition, human damage in damage conditions, clear day in weather condition, dry condition in the road condition, and three-way intersection in the road way showed the highest clustering.

• 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 the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• The Journal of Korean Society for School & Community Health Education
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• v.13 no.2
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• pp.107-118
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• 2012

Backgrounds: In order to prevent dental injuries that often occur in child and adolescent, it is intended to investigate and assess actual state of the injury development, present epidemiological background, and consider and discuss for preparing preventive means against the injury development. Purpose: It was attempted to understand major features of dental injuries developing in child and adolescent and indentify high risk factors of dental injuries in child and adolescent. Methods: In this study, 523 cases of computerized data collected as disease entities of dental injuries among 1-18 years old patient visiting S university hospital located in Seoul in 2009 were analyzed and following results were obtained. Results: It was found that the ratio of dental injuries by genders in child and adolescent was 66.14% of male and 33.86% of female. It was shown also that causes of dental injuries by ages were more in order of falling, bumping, chewing, traffic accident, sports, violence, and crash. In addition places where dental injuries occur by ages were home in less than 5 year old group, park, playground, and play yard in 6-11 year old group, park, playground, and play yard also in 12-14 year old group, and stairs, road, and outdoor places such as mountain climbing, beach, and camping in 15-18 year old group. It was found that time rages when dental injuries in child and adolescent often develop were 15-19 o'clock for falling, 15-19 o'clock for crash, 15-19 o'clock for bumping, 19-03 o'clock for violence, 15-19 o'clock for traffic accident, 15-19 o'clock for sports activity, and 15-19 o'clock for chewing. Conclusion: Background of dental injury inducing factors are very complicated and diversified, so deep study and analysis are required for its prediction. Therefore, it seems necessary to identify risk factors by phases such as before, at, and after accident, establish strategies to reduce injury development, and develop and utilize necessary programs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.1
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• pp.16-26
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• 2011

Variable speed limit(VSL) is one of the highway ITS techniques designed to prevent accidents and traffic slow down by reducing congestion or speed variation between vehicles and lanes prior to arrive at the accident location by limiting speed. In Korea, while people have recognized the need for variable speed limit beginning with Seoul's urban expressway and installed facilities in order to provide guide for speed limit per lane and lane use, there has not been enough development of algorithm for internal administration as well as research on the basic principles behind administering variable speed limit. This study is for modeling and evaluating the VSL strategies based on the traffic flow theory. Supply-Demand method of the Cell Transmission Model is applied to demonstrate the traffic features and shockwaves to upstream of the bottleneck with/without VSL. We verified the explanation of Cell Transmission Model for the numerical example. and as the result, it is found that VSL strategies can reduce the total travel time in the congested section and variation of the speed. It means VSL is useful to improve the traffic condition and the safety on highway

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.