• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.4
• /
• pp.178-185
• /
• 2023

Rapid urbanization and advancements in technology have led to a surge in the number of automobiles, resulting in frequent traffic accidents, and consequently, an increase in human casualties and economic losses. Therefore, there is a need for technology that can predict the risk of traffic accidents to prevent them and minimize the damage caused by them. Traffic accidents occur due to various factors including traffic congestion, the traffic environment, and road conditions. These factors give traffic accidents spatiotemporal characteristics. This paper analyzes traffic accident data to understand the main characteristics of traffic accidents and reconstructs the data in a time series format. Additionally, an LSTM-MLP based model that excellently captures spatiotemporal characteristics was developed and utilized for traffic accident prediction. Experiments have proven that the proposed model is more rational and accurate in predicting the risk of traffic accidents compared to existing models. The traffic accident risk prediction model suggested in this paper can be applied to systems capable of real-time monitoring of road conditions and environments, such as navigation systems. It is expected to enhance the safety of road users and minimize the social costs associated with traffic accidents.

• International Journal of Highway Engineering
• /
• v.13 no.1
• /
• pp.13-19
• /
• 2011

The purpose of this study is to analyze the characteristics of traffic accidents and to develop the models by day and night-time in the case of the arterial link sections. In pursuing the above, this study uses the 224 accident data occurred at the 24 arterial link sections in Cheongju. The main results analyzed are as follows. First, it was analyzed that the number of accidents during day was more than night, but the accidents rate during night was higher than day. Second, four models which were all statistically significant were developed. Finally, the differences between the day and night models were comparatively analyzed using independent variables.

• Journal of the Korean Society of Safety
• /
• v.32 no.3
• /
• pp.130-135
• /
• 2017

This study deal with the traffic accident. The purpose of this study is to develop the accident density models reflecting the transportation and socioeconomic characteristics based on 230 zones of Korea. In this study, The models which are tested to be statistically significant are developed through multiple linear regression analysis. The main research results are as follows. First, in the transportation-based model, road length, avenue ratio, number of intersections and tunnels are analyzed to be positive to the model, however, school zone is analyzed to be negative to the model. Second, in the socioeconomic-based model, population density, transportation vulnerable ratio, children and truck ratio are analyzed to be positive to the model. Finally, in the integrated models, road ratio, population density, transportation vulnerable ratio, children ratio, truck ratio and number of companies are analyzed to be positive, however, school zone is analyzed to be negative to the model. This results could be expected to give good implications to accident-reduction policy-making.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.169-183
• /
• 2010

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a freeway and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, existing technologies, this freeway attribute, velocity changes, volume changes, occupancy changes reflect judge the primary. Furthermore, We pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian Mixture model analytical method which has been considered the best among well-known environmental obstacle reduction methods. Therefore, in this way, the accident was the final decision. Also, environmental factors occur frequently, and with the index finger situations, effectively reducing that can actively and environmentally adaptive techniques through accident final judgment. This implementation of the evaluate performance of the experiment road of 12 incidents in simulated and the jang-hang IC's real-time accident experiment. As a result, the do well detection 93.33%, false alarm 6.7% as showed high reliability.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1123-1130
• /
• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• Journal of Korean Society of Transportation
• /
• v.29 no.6
• /
• pp.67-74
• /
• 2011

This study deals with the traffic accidents data from the Korean rotaries (circular intersections) to verify their characteristics affected by different vehicle types. This paper categorized the data into three groups based on vehicle types, and developed a set of accident models. The paper proposed two ZIP models and one negative binomial model through a statistical analysis for three vehicle types: automobile, truck and van, and others. The differences among those models were then statistically compared.

• International Journal of Highway Engineering
• /
• v.13 no.1
• /
• pp.69-77
• /
• 2011

This study is to suggest a methodology to overcome the uncertainty and lack of reliability of data. The fuzzy reasoning model and the neural network model were developed in order to overcome the potential lack of reliability which may occur during the process of data collection. According to the result of comparison with the Poisson regression model, the suggested models showed better performance in the accuracy of the accident frequency prediction. It means that the more accurate accident frequency prediction model can be developed by the process of the uncertainty of raw data and the adjustment of errors in data by learning. Among the suggested models, the performance of the neural network model was better than that of the fuzzy reasoning model. The suggested models can evaluate the safety of signalized intersections in operation and/or planning, and ultimately contribute the reduction of accidents.

• Journal of Korean Society of Transportation
• /
• v.25 no.5
• /
• pp.149-160
• /
• 2007

Automobile black boxes store and provide accident and driving information. The accident and driving information can be utilized to build scientific traffic-event database and can be applied in various industries. The objective of this study is to develop a recognition system of dangerous driving through analyzing the driving characteristic patterns. In this paper, possible dangerous driving models are classified into four models on the basis of vehicle behaviors(acceleration, deceleration, rotation) and accident types from existing statistical data. Dangerous driving data have been acquired through vehicle tests using automobile black boxes. Characteristics of driving patterns have been analyzed in order to classify dangerous driving models. For the recognition of dangerous driving, this study selected critical value of each dangerous driving model and developed the recognition algorithm of dangerous driving. The study has been verified by the application of recognition algorithm of dangerous driving and vehicle tests using automobile black boxes. The presented recognition methods of dangerous driving can be used for on-line/off-line management of drivers and vehicles.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.4
• /
• pp.1279-1287
• /
• 2014

The purpose of this study is to develop the U-turn accident model at signalized intersections in urban areas. The characteristics of the accidents which are associated with U-turn operation at 3 and 4-legged signalized intersections was analyzed and the U-turn accident model was developed by regression analysis in Changwon city. First, in order to analyze the effectiveness on traffic accidents by U-turn installation, the difference of mean of traffic accident number are measured between two groups which are composed by whether or not U-turn installation the groups by Mann-Whitney U test. The result of significance test showed that intergroup comparison on mean by accident types made difference except rear-end accident type and by accident locations exit section only showed difference in significance level at 4-legged intersections, so the accident number have more where the U-turn is permitted than not. Response measures about the number of accidents were classified by whether accidents occurred and accident model were constructed using binomial logistic regression analysis method. The developed models show that the variables of conflict traffic, number of opposing lane are adopted as independent variable for both intersections. The variables of longitudinal grade for 3-legged signalized intersection and number of crosswalk for 4-legged signalized intersection at which the U-turn is permitted is adopted as independent variable only. These study results suggest that U-turn would be permitted at the intersection where the number of opposing lane is more than 3.5 each, the longitudinal grade of opposing road is upward flow and there is need to establish the U-turn traffic sign at signalized intersections.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.6 no.1
• /
• pp.48-57
• /
• 1988

In a traditional method to clarify causes of traffic accidents by using tapes, there appeared its limitation of reliability on the inspection of accident. With only limitted number of people and equipments, it is almost impossible to inspect and examine the causes of traffic accidents while not resulting a traffic complexity and its impediment. In this paper, as an approach to such problems, a rationalization in treatment of the traffic accidents was tried to be found, as accurately measuring 3-dimensional co-ordinates between the needed points by the stereo-models pictured by a stereo-camera, then composing some plane figures which show places and vehicles concerning the accidents, abstracting some needed informations from the resultants, and supplementing to solve problems on the pre-existing method, and sometimes when needed, enabling to treat the troubled points by reappearing those points.