• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.207-218
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• 2001

As a city and industries are developed rapidly, a traffic accident and congestion take places on the road link become serious and it can be a large problem of the society in the future. Especially, most of the traffic accidents on the signalized intersection are caused by the human factor, vehicle and environmental factor mutually. The relation of the traffic accident and volume is acting on the outbreak of the traffic accident and the mistake of driver altogether as a major cause. The purpose of this paper is to develop a model for the forecasting of the traffic accident and to use research data gained to predict many traffic accidents. The data of this study were used with real one of the 73 areas of the four-legged signalized intersection in Kwang-ju city from 1996 to 1998 for three years to develop a model for the forecasting of the traffic accident. The statistical methods used in this paper are the principal component, regression and correlation analysis. We studied accident models to find out useful data from the statistics method and applied the data to the different area of the Choun-La province for the verification of the model. So, the result of this paper showed a reasonable model for the forecasting or the traffic accident and possibility of the model for simulating on real case. Finally, This study would be made of a study continually for the safe design and plan for the four-legged signalized intersection.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.91-98
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• 2011

We develop a model to predict traffic accidents in Korea. In contrast to the classical approach that mainly uses regression analysis, Bayesian approach is adopted. A dependent model that incorporates the data from different kinds of accidents is introduced. The rate of severe accident can be updated even with no data of the same kind. The data of minor accident that can be obtained frequently is efficiently used to predict the severe accident.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.575-584
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• 2005

The judgement model to warn of possible pollution accident is constructed by multi-perceptron, multi layer neural network, neuro-fuzzy and it is trained stability, notice, and warming situation due to developed standard axis. The water quality forecasting model is linked to the runoff forecasting model, and joined with the judgement model to warn of possible pollution accident, which completes the artificial intelligence warning system. And GUI (Graphic User Interface) has been designed for that system. GUI screens, in order of process, are main page, data edit, discharge forecasting, water quality forecasting, warming system. The application capability of the system was estimated by the pollution accident scenario. Estimation results verify that the artificial intelligence warning system can be a reasonable judgement of the noized water pollution data.

• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.73-88
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• 1990

This study aims to develop a traffic accident forecasting model using the data, which are based on the past accidents in Korea. The regression analysis was used in conjuction with the variables of the traffic accidents and social behaviours. The objectives of this study are as follows; 1. The number of behicles has given a strong affect to increase the traffic accidents in Korea since a factor of vehicles has shown 86% over of total accidents. 2. The forecasting model regarding the traffic accidents, deaths and injuries, which was formulated for this study, proved to be useful in light of the results of the regression diagnostics. 3. It is expected that the traffic accidents in Korea in 1991 may take place as follows on condition that the traffic environment would worsen ; 274,000 cases of accidents with 13,600 deaths and 367,000 injuries, in 1994, 451,000 cases with 24,900 deaths and 71,500 injuries respectively.

• Journal of the Korean Society of Safety
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• v.7 no.2
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• pp.5-13
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• 1992

If zero-accident movement is to be successful, the objective goal period should be surely obtainable, and much more in our country where frequency rate of injury are remarkably fluc-tuating. However In our country, as far as we know, no method to establish a reasonable zero-accident goal period is guaranteed. In thls paper, a new establishing-method of reasonable goal period for individual industry with considering recent accident trend is presented. A mathematical model for industrial accidents generation was analyzed, and a stochastic process model for the accident generation inteual was formulated. This model could tell the accident generation rate in future by understanding the accident tendency through the time-series analysis and search for the distribution of numbers of accidents and accident interval. On the basis of this, the forecasting method of goal achievement probability by the size and the establishment method of reasonable goal period were developed.

• Journal of the Korean Regional Science Association
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• v.11 no.1
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• pp.21-30
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• 1995

This paper deals with the forecasting models for traffic accident by region. Its objectives are to develop the appropriate model for projecting the accident and to analyze the regional characteristics of the accident model. The main results are as follow. First, the literature review, statistical tests and sensitivity analyses show that the joint model combined both PTM and Exponential functions is appropriate to project the traffic accidents by region. Second, the statistical analyses by region. Second, the statistical analyses on the regional accident models indicate that the levels of significance in terms of t-value, $R^2$ and F-value are very high. Finally, the comparative analyses among regions show that the regional differences on the accident patterns can be explained by the joint models and the accident indices (parameters, $P_{max}$, 1/b, $\eta$ etc.) of each region.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.1
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• pp.72-75
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• 2006

Event trees are widely used graphical tool to denote the accident inintiation and escalation to more severe accident. But they have some drawbacks in that they do not have efficient way of updating model parameters and also they can not contain the information about dependency or independency among model parameters. A tool that can cure such drawbacks is an influence diagram. We introduce influence diagrams and explain how to update model parameters and obtain predictive distributions. We show that an event tree can be converted to a statistically equivalent influence diagram, and bayesian prediction can be made more effectively through the use of influence diagrams.

• Journal of the Korea Safety Management & Science
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• v.9 no.5
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• pp.1-5
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• 2007

We construct the procedure to predict safety accidents following Bayesian approach. We make a model that can utilize the data to predict other levels of accidents. An event tree model which is a frequently used graphical tool in describing accident initiation and escalation to more severe accident is transformed into an influence diagram model. Prior distributions for accident occurrence rate and probabilities to escalating to more severe accidents are assumed and likelihood of number of accidents in a given period of time is assessed. And then posterior distributions are obtained based on observed data. We also points out the advantages of the bayesian approach that estimates the whole distribution of accident rate over the classical point estimation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.65-78
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• 2017

Even though cars have a good effect on modern society, traffic accidents do not. There are traffic laws that define the regulations and aim to reduce accidents from happening; nevertheless, it is hard to determine all accident causes such as road and traffic conditions, and human related factors. If a traffic accident occurs, the traffic law classifies it as 'Negligence of Safe Driving' for cases that are not defined by specific regulations. Meanwhile, as Korea is already growing rapidly elderly population with more than 65 years, so are the number of traffic accidents caused by this group. Therefore, we studied predictive and comparative analysis of the number of traffic accidents caused by 'Negligence of Safe Driving' by dividing it into two groups : All-ages and Elderly. In this paper, we used empirical monthly data from 2007 to 2015 collected by TAAS (Traffic Accident Analysis System), identified the most suitable ARIMA forecasting model by using the four steps of the Box-Jenkins method : Identification, Estimation, Diagnostics, Forecasting. The results of this study indicate that ARIMA $(1, 1, 0)(0, 1, 1)_{12}$ is the most suitable forecasting model in the group of All-ages; and ARIMA $(0, 1, 1)(0, 1, 1)_{12}$ is the most suitable in the group of Elderly. Then, with this fitted model, we forecasted the number of traffic accidents for 2 years of both groups. There is no large fluctuation in the group of All-ages, but the group of Elderly shows a gradual increase trend. Finally, we compared two groups in terms of the forecast, suggested a countermeasure plan to reduce traffic accidents for both groups.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.54-61
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• 2019

Recently, IITS (intelligent integrated transportation system) has been important topic in Smart City related industry. As a main objective of IITS, prevention of traffic jam (due to car accidents) has been attempted with help of advanced sensor and communication technologies. Studies show that car accident has certain correlation with some factors including characteristics of location, weather, driver's behavior, and time of day. We concentrate our study on observing auto correlativity of car accidents in terms of time of day. In this paper, we performed the ARIMA tests including ADF (augmented Dickey-Fuller) to check the three factors determining auto-regressive, stationarity, and lag order. Summary on forecasting of hourly car crash counts is presented, we show that the traffic accident data obtained in Korea can be applied to ARIMA model and present a result that traffic accidents in Korea have property of being recurrent daily basis.