• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.114-131
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• 2023

Personal Mobility (PM)-related accidents have increased rapidly since PM use was activated. In response to the increase in these accidents, the government strengthened regulations for PM users on May 13, 2021. The number of the accidents in which the PM user was a victim decreased significantly. In contrast, the increasing number of accidents in which PM user was the offender did not decrease significantly. In most of these accidents, the PM user was the offender who crashed into pedestrians. Hence, the safety of pedestrians is threatened. Therefore, this study analyzed the factors, such as the regulations, urban built environment, and personal characteristics, affecting the severity of PM-pedestrian accidents by focusing on PM-pedestrian crashes. This study analyzed the PM-pedestrian accidents in Seoul from 2020 to 2021 using binary logistic regression model. Through these results, this study proposed the policy implications.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.29-42
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• 2016

Speeding is an important factor in traffic safety. Speed not only affects crash severity, but is also related to the possibility of crash occurrence. This study presents results from an analysis of 27,968 speed violation cases collected from 36 red light and speed enforcement cameras at signalized intersections in the city of Cheongju. Data included details of their violation history such as speeding tickets within a recent 3-year span and their demographic characteristics. The goal of this analysis is to understand the correlation between speed violations and various factors in terms of humans, vehicles and road environments. This study used descriptive statistics and Binary Logistics Regression(BLR) analysis with SPSS 20.0 software. The major results of this study are as follows. First, speed violations occurred at rural and suburban area. Second, about 25.6% of the violators committed to more than 20km/h over a speed limit. Third, the difference between speed violators and normal drivers clearly appeared in location of intersection(urban/rural/suburban area), gender and age. Finally, a statistically significant model(Hosmer and Lemeshow test: 11.586, p-value: 0.171) was developed through the BLR.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.887-894
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• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.43-51
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• 2011

Since a variety of factors are associated with crash occurrence, the analysis of causes of crash is a hard task for traffic researchers and engineers. Among contributing factors leading to crash, the characteristics of driver is of keen interest. This study attempted to identify factors affecting the severity of pedestrian in the collision between pedestrian and vehicle. In particular, our analyses were focused on the novice driver. A binary logistic regression technique was adopted for the analyses. The results showed that driver's age, crash location, and the frequency of violations were dominant factors for the severity. Findings are expected to be useful information for deffective policy- and education-based countermeasures.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.1
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• pp.1-16
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• 2013

This study investigates the spatial distribution characteristics of Korean fashion industries during the last decade, in which the economic geography of fashion industries has changed dynamically with economic globalization and "thus resulted in increased" demand "of" diversification. In particular, this study examines the spatial distribution patterns of fashion industries in the Seoul metropolitan area where fashion industries are highly agglomerated. For the purpose, this study applies Moran's I Index of spatial autocorrelation analysis for seven functional sectors of fashion industries related to fashion production. The global and local agglomeration patterns are examined for each functional sector. The results clarify the distinction in the spatial agglomeration patterns among the seven functional sectors of fashion industries in the Seoul Metropolitan area. Logit models are developed to examine the interrelationships among functional sectors in their spatial agglomeration distribution patterns. By conducting binary logistic regression analysis, we find out how the spatial agglomeration of each functional sector is related to the others.

• Journal of the Economic Geographical Society of Korea
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• v.19 no.1
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• pp.104-121
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• 2016

This study was conducted in order to investigate the factors affecting the elderly employment and especially focused on the factor of their residential areas. This paper performs binary logistic regression analysis with the micro data of 2014 Survey of Living Conditions and Welfare Needs of Older Koreans. To reveal the influence on the elderly employment, various dependent variables was used such as demographical, health, household, economic, lifelong job, living environment, and residential area characteristics. The elderly in rural areas have higher possibility of currently working than those in urban areas. Based on the results, more various and complex factors affected on the employment of the urban elderly. This suggested that differentiated policy supports were needed in the urban and rural areas.

• International Journal of Highway Engineering
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• v.12 no.1
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• pp.39-46
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• 2010

Delineator is a useful device to support driver's safer maneuver. Effective placement and operation of the delineator would lead to prevent traffic accidents on the roads. This study evaluates the effectiveness of parameters associated with delineator placement and operation, which include spacing, height and size, from the point of user's satisfaction. Also, this study devises a methodology for determining such parameters using binary logistic regression technique. The proposed model is capable of producing probabilistic measure of user's satisfaction according to the various parameters. The outcome of this study would be useful fundamentals for more effective placement and operation of delineators.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1105-1113
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• 2013

Understanding the characteristics of truck-involved crashes is of keen interest because such crashes are highly associated with greater potential leading to severer injury. The purpose of this study is to identify factors affecting injury severity of truck-involved crashes on freeways. In addition, a binary logistic regression technique is applied to identify causal factors affecting truck crash severity under normal and adverse weather conditions. Major findings from the analyses are discussed with truck operations strategies including speed enforcement, variable speed limit, and truck lane restriction, from the safety enhancement point of view. The results of this study would be useful for developing traffic control and operations strategies to reduce truck-involved crashes and injury severity in practice.