• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.1-15
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• 2023

In this study, the number of truck traffic accidents was predicted by using Poisson and negative binomial regression analysis to understand what factors affect accidents using expressway data. Significant variables in the truck traffic accident prediction model were continuous driving time, link length, truck traffic volume. number of bridges and number of drowsy shelters. The calculated LOSS rating was expressed on the national expressway network to diagnose the risk of truck accidents. This is expected to be used as basic data for policy establishment to reduce truck accidents on expressways.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.147-157
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• 2014

Women's roles have changed substantially in economically developed countries; subsequently, the ratio of female drivers has also increased. In such countries, there has been considerable interest in assessing gender differences in vehicle accident risks and reasons to explain the gender differences. This study investigates the gender differences in vehicle accident risk based on 500,000 drivers randomly selected from a population sample. A Heckman model is used for accident damage and a negative binomial model is used for the accident frequency. Empirical results show that male drivers are 8.3% riskier than female drivers in terms of accident damage; however, female drivers are 113% risker than male drivers in term of accident frequency. We can implement more practical policies to reduce vehicle accidents if we can understand the reasons for the gender differences.

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

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.69-77
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• 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 the Korea Society for Simulation
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• v.25 no.4
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• pp.109-115
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• 2016

This study intended to testify the causality among three variables such as accident, depression and cognitive failure of the train drivers. For this purpose, two research models were suggested. Model 1 hypothesized the causality among three variables as 'depression ${\rightarrow}$ cognitive failure ${\rightarrow}$ accident'. On the other hand, model 2 hypothesized the causality among three variables as 'accident ${\rightarrow}$ depression ${\rightarrow}$ cognitive failure'. Results based on AMOS using 416 train drivers' questionnaire showed that model 2 is more valid than model 1. The statistical result of model 1 showed that depression has a positive effect on cognitive failure, however no significant relationship between depression and accident as well as between cognitive failure and accident. In model 2, the result showed that the accident has a positive effect on cognitive failure mediated by depression. This result suggests the necessity for establishment of countermeasures to mitigate mistake and cognitive failure caused by train drivers in a wider context, considering the causality between accident and depression.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.21-40
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• 2003

A traffic accident analysis method was developed and tested based on the highway alignment risk indices using geographic information systems(GIS). Impacts of the highway alignment on traffic accidents have been identified by examining accidents occurred on different alignment conditions and by investigating traffic accident risk indices(TARI). Evaluative criteria are suggested using geometric design elements as an independent variable. Traffic accident rates were forecasted more realistically and objectively by considering the interaction between highway alignment factors and the design consistency. And traffic accident risk indices and risk ratings were suggested based on model estimation results and accident data. Finally, forecasting traffic accident rates, evaluating the level of risk and then visualizing information graphically were combined into one system called risk assessment system by means of GIS. This risk assessment system is expected to play a major role in designing four-lane highways and developing remedies for highway sections susceptible to traffic accidents.

• Journal of Korean Society of Transportation
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• v.29 no.3
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• pp.93-101
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• 2011

In 2008, Korea has observed total 215,822traffic accidents Although the number has decreased since then, the crash rate is still higher than those of other advanced countries. In particular, high rate of pedestrian accidents occurred on urban streets is recognized as a serious problem. The previous studies, however, are not entirely considerate of accident factors by accident type. Inspired by the fact, this study analyzes factors affecting traffic accident by accident type. Using the accident data collected on urban streets in Seodaemun-gu, this paper classifies the accidents into two groups (i.e., vehicle-vs-vehicle and vehicle-vs-person crashes), and analyzes relationships between severity and exogenous variables. For the analysis, Structural Equation Modeling (SEM) is employed to estimate relationships among exogenous factors of traffic accident by each type on urban streets. The resulting model reveals that roadway related factors are highly correlated with the severity of vehicle-vs-vehicle crashes whereas environment factors are with vehicle-vs-person crashes.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.211-221
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• 2008

This dissertation studied the psychological cost, which converted the mental pain suffered by the victim of a traffic accident and his/her family, friends and people around him/her into social costs. Three methodologies - Choice Experiments, Direct Question and Dichotomous Choice Question - were used to design questionnaires, and models were built for each questionnaire design method. When building models, a logit model was used, which is used most frequently in probabilistic choice model. And the tobitmodel was used to make direct questionnaires. When verifying these models, although there were some differences in each model, suitability of most models and credibility of each coefficient were meaningful around the credibility level of 95%. According to the analysis, domestic psychological cost produced through the assessment model of psychological cost was 15.63 million won per person or 5.1 trillion in total, assuming 37.1% of total traffic accident cost.

• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.123-135
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• 2007

The objective of this study is to analyze the relationship between traffic accidents occurring at trumpet interchange ramps according to accident type as well as the relevant factors that led to the traffic accidents, such as geometric design elements and traffic volumes. In the process of analysis of the distribution of traffic accidents, negative binomial distribution was selected as the most appropriate model. Negative binomial regression models were developed for total trumpet interchange ramps, direct ramps, loop ramps and semi-direct ramps based on the negative binomial distribution. Based upon several statistical diagnostics of the difference between observed accidents and predicted accidents with four previously developed models, the fit proved to be reasonable. Understanding of statistically significant variables in the developed model will enable designers to increase efficiency in terms of road operations and the development of traffic accident prevention policies in accordance with road design features.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.157-166
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• 2008

This study proposed a model to estimate traffic accident likelihood using real-time traffic data obtained from freeway traffic surveillance systems. Traffic variables representing spatio-temporal variations of traffic conditions were utilized as independent variables in the proposed models. Binary logistics regression modelings were conducted to correlate traffic variables and accident data that were collected from the Seohaean freeway during recent three years, from 2004 to 2006. To apply more reliable traffic variables, outlier filtering and data imputation were also performed. The outcomes of the model that are actually probabilistic measures of accident occurrence would be effectively utilized not only in designing warning information systems but also in evaluating the effectiveness of various traffic operations strategies in terms of traffic safety.