• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Journal of the Society of Disaster Information
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• v.13 no.4
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• pp.570-576
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• 2017

Trucks take up more portions than cars on highways. Due to this, road use relatively diminish and it serves locally as a threatening factor to nearby drivers. Baggage car accident has distinct characteristics so that it needs the application of different analysis opposed to ordinary accidents. Accident prediction model, one of accident analyses, is used to predict the numbers of accident in certain parts, establish traffic plans as well as accident prevention methods, and diagnose the danger of roads. Thus, this study aims to apply the accident rate of baggage car on highways and calculate the correction factor to be put in the accident prediction models. Accident data based on highway was collected and traffic amounts and accident documents between 2014 and 2016 were utilized. The author developed an accident prediction model based on numbers of annual accidents and set mean annual and daily traffic amounts. This study intends to identify the practical accident prediction model on highway and present an appropriate solution by comparing the prediction model in accords with the accident rate between baggage cars.

• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.108-114
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• 2012

Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

• International Journal of Highway Engineering
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• v.10 no.1
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• pp.31-39
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• 2008

Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. The accurate traffic accident prediction model requires not only understanding of the factors that cause the accident but also having the transferability of the model. So, this paper suggest the traffic accident diagram using CART(Classification And Regression Tree) analysis, developed Model is compared with the existing accident prediction models in order to test the goodness of fit. The results of this study are summarized below. First, traffic accident prediction model using CART analysis is developed. Second, distance(D), pedestrian shoulder(m) and traffic volume among the geometrical factors are the most influential to the traffic accident. Third. CART analysis model show high predictability in comparative analysis between models. This study suggest the basic ideas to evaluate the investment priority for the road design and improvement projects of the traffic accident blackspots.

• Journal of the Korean Society of Safety
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• v.26 no.6
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• pp.130-136
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• 2011

Since out of a lot of traffic problems traffic accidents cause damage to life and properties of people, it stands out as one of traffic problems which needs improvement, and the loss due to traffic accident negatively affects not only the parties to the accident but also the national economy. Thus, continual concern of the government toward traffic safety is getting bigger and lately each local government is preparing a basic plan for traffic safety and vitalizing traffic safety policies. As expanding the responsibility and role of local governments for traffic safety, traffic safety measures which are based on the characteristics of each local government should be studied. Most of analytical methods in the existing traffic accidents prediction models with macroscopic vision focus on socioeconomic variables such as local population and the number of registered vehicles, and present a great deal of prediction error when they are applied in practice. In this context, this study proposed a traffic accident prediction model in respect of macroscopic level for autonomous districts (administrative districts) of Seoul City. The model development was not based on the entire city but on the type of local land usage (development density) whose relationship with traffic accident frequency was analyzed.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.101-111
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• 2012

PURPOSES: Because expressway ramps are very complex segments where diverse roadway design elements dynamically change within relatively short length, drivers on ramps are required to drive their cars carefully for safety. Especially, ramps on expressways are designed to guarantee driving at high speed so that the risk and severity of traffic accidents on expressway ramps may be higher and more deadly than other facilities on expressways. Safe deceleration maneuvers are required on off-ramps, whereas safe acceleration maneuvers are necessary on onramps. This difference in required maneuvers may contribute to dissimilar patterns and severity of traffic accidents by ramp types. Therefore, this study was aimed at developing prediction models of the severity of traffic accidents on expressway on- and off-ramps separately in order to consider dissimilar patterns and severity of traffic accidents according to types of ramps. METHODS: Four-year-long traffic accident data between 2007 and 2010 were utilized to distinguish contributing design elements in conjunction with AADT and ramp length. The prediction models were built using the negative binomial regression model consisting of the severity of traffic accident as a dependent variable and contributing design elements as in independent variables. RESULTS: The developed regression models were evaluated using the traffic accident data of the ramps which was not used in building the models by comparing actual and estimated severity of traffic accidents. Conclusively, the average prediction error rates of on-ramps and offramps were 30.5% and 30.8% respectively. CONCLUSIONS: The prediction models for the severity of traffic accidents on expressway on- and off-ramps will be useful in enhancing the safety on expressway ramps as well as developing design guidelines for expressway ramps.

• 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 Korean Society of Transportation
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• v.33 no.5
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• pp.497-507
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• 2015

Nonrecurrent congestion of freeway was primarily caused by incident. The main cause of incident was known as a traffic accident. Therefore, accurate prediction of traffic incident clearance time is very important in accident management. Traffic accident data on freeway during year 2008 to year 2014 period were analyzed for this study. KNN(K-Nearest Neighbor) algorithm was hired for developing incident clearance time prediction model with the historical traffic accident data. Analysis result of accident data explains the level of accident significantly affect on the incident clearance time. For this reason, incident clearance time was categorized by accident level. Data were sorted by classification of traffic volume, number of lanes and time periods to consider traffic conditions and roadway geometry. Factors affecting incident clearance time were analyzed from the extracted data for identifying similar types of accident. Lastly, weight of detail factors was calculated in order to measure distance metric. Weight was calculated with applying standard method of normal distribution, then incident clearance time was predicted. Prediction result of model showed a lower prediction error(MAPE) than models of previous studies. The improve model developed in this study is expected to contribute to the efficient highway operation management when incident occurs.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.39-56
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• 2011

This study proposed a traffic accident prediction model developed based on administrative districts of Seoul. The model was to find the relationship between accident rates and the representative land usage of the districts (development density) - the higher the development density (building floor area) is, the higher the traffic accident rate is. The findings showed that traffic accident statistics differ from (1) residential building floor area, (2) commercial building floor area and (3) business building floor area.