• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.49-59
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• 2017

In this study, We analyzed the characteristics of carsharing use and revealed the environmental factors influencing carsharing use using the records of carsharing had been operated in Incheon during 2016. The records show a higher ratio of male users, a big portion of 20s and 30s, and ascending trends in the use of elderly people and short distances compared to the past. We analyzed the relationship between carsharing use and neighborhood environmental factors using the negative binomial regression model. It was found that carsharing was more active in areas where have many public transportation users and higher portion of residential buildings. Therefore, it was concluded that these areas can be suitable candidates for placing new carsharing rental sites to expect more active carsharing use.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.81-89
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• 2015

Crashes that occur at tollgates have different characteristics compared to those of the mainline on expressways in terms of crash cause, crash type, and vehicle type. Due to this fact, the safety performance function (SPF) focused on the expressway tollgates, apart from the mainline, should be developed. The aim of this study is, therefore, to identify the influential factors and develope a SPF for crashes at tollgates. Firstly, we established independent variables affecting crashes at tollgates through literature review and descriptive statistical analysis. Based on these variables, two negative binomial regression models with different form of independent variables were developed and goodness-of-fits of each model were compared. According to the results, the number of crashes increases i) as AADT, Hi-pass rate, and heavy vehicle rate increase, ii) as average lane width decreases, iii) on the mainline tollgate type. The safety performance function developed in this study could be applied to select hot-spots for expressway tollgates.

• Journal of Information Science Theory and Practice
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• v.7 no.4
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• pp.33-44
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• 2019

Altmetrics measure the frequency of references about an article on social media platforms, like Twitter. This paper studies a variety of factors that affect the popularity of articles (i.e., the number of article mentions) in the field of psychology on Twitter. Firstly, in this study, we classify Twitter users mentioning research articles as academic versus non-academic users and experts versus non-experts, using a machine learning approach. Then we build a negative binomial regression model with the number of Twitter mentions of an article as a dependant variable, and nine Twitter related factors (the number of followers, number of friends, number of status, number of lists, number of favourites, number of retweets, number of likes, ratio of academic users, and ratio of expert users) and seven article related factors (the number of authors, title length, abstract length, abstract readability, number of institutions, citation count, and availability of research funding) as independent variables. From our findings, if a research article is mentioned by Twitter users with a greater number of friends, status, favourites, and lists, by tweets with a large number of retweets and likes, and largely by Twitter users with academic and expertise knowledge on the field of psychology, the article gains more Twitter mentions. In addition, articles with a greater number of authors, title length, abstract length, and citation count, and articles with research funding get more attention from Twitter users.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.33-40
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• 2009

This study deals with the traffic accidents of elderly drivers. The objectives are to comparatively analyze the characteristics of accident between the elderly and other drivers, and to develop the models of traffic accidents. In pursuing the above, this paper gives particular attentions to testing the differences between the above two groups, and developing the models(Poisson and negative binomial regression) using the data of Cheongju 4-legged signalized intersections. The main results analyzed are as follows. First, the differences between the elderly and other drivers' accidents were clearly defined by the time of day, accident type, etc. Second, 3 accident models which were all statistically significant were developed. Finally, the differences between elderly and other drivers' models were comparatively analyzed using the common and specific variables.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.373-381
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• 2018

The floating population that is index to figure out dynamic activities in urban area will be important in urban railway planning, but it is not useful because it is collected by posterior method. This study aims to investigate factors influencing floating population. The floating population data that was collected in Seoul for a month in December 2013 is used as dependent variable, and the negative binomial regression analysis is used in modelling. The number of households, number of employees, number of subway stations, and number of bus lines variables are statistically significant in predicting floating population.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.54-63
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• 2023

Pedestrian traffic needs to be accurately quantified to predict effectively pedestrian traffic accidents, however, pedestrian traffic is more difficult to measure than vehicle traffic. In this study, we suggest the time-and cost-effective application of mobile closed-circuit television (CCTV) using a smartphone as an alternative that can collect and analyze real-time data with little. In the present investigation, the pedestrian-vehicle conflict that can develop into an accident was defined as the pedestrian accident exposure. After installing mobile CCTV in 40 sections of Dongseong-ro, Daegu, the pedestrian accident exposure was estimated through negative binomial regression analysis using the collected data. The results of the analysis showed statistically significant changes in the pedestrian accident exposure variables. Based on the present results, a pedestrian accident exposure estimation model was developed which can be used in sections where pedestrian accidents may occur.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2609-2626
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• 2023

The hotel industry is an example of experiential services. As consumers cannot fully evaluate the online review content and quality of their services before booking, they must rely on several online reviews to reduce their perceived risks. However, individuals face information overload owing to the explosion of online reviews. Therefore, consumer cognitive fluency is an individual's subjective experience of the difficulty in processing information. Information complexity influences the receiver's attitude, behavior, and purchase decisions. Individuals who cannot process complex information rely on the peripheral route, whereas those who can process more information prefer the central route. This study further discusses the influence of the complexity of review information on hotel ratings using online attraction review data retrieved from TripAdvisor.com. This study conducts a two-level empirical analysis to explore the factors that affect review value. First, in the Peripheral Route model, we introduce a negative binomial regression model to examine the impact of intuitive and straightforward information on hotel ratings. In the Central Route model, we use a Tobit regression model with expert reviews as moderator variables to analyze the impact of complex information on hotel ratings. According to the analysis, five-star and budget hotels have different effects on hotel ratings. These findings have immediate implications for hotel managers in terms of better identifying potentially valuable reviews.

• Asia Marketing Journal
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• v.24 no.2
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• pp.62-77
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• 2022

Seeded marketing campaign (SMC) is a newly created type of marketing activities with the widespread use of social media. Previous research has examined to find out the optimal seeding strategy that yields the best outcome from the campaign. This research explores the relationships between the characteristics of the seeded influencer and user engagement. The data consists of information from 1062 seeded Instagram posts posted in September 2020 in Korea and 778 seeded influencers who posted those contents. Analyzed by negative binomial regression, our quadratic model suggests that the relationship between user engagement and the number of followers of the seeded influencer draws an inverted U-shape, indicating influencers with greater number of followers may not always be the best choice for the marketers. Moreover, this research shows that the negative marginal impact coming from the huge number of followers can be attenuated when the influencer is an expert of the seeded product.

• Journal of the Korean Society of Safety
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• v.26 no.6
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• pp.118-123
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• 2011

This study deals with the traffic accidents of the arterial link sections. The purpose is to comparatively analyze the characteristics and models by accident type using the data of 24 arterial links in Cheongju. In pursuing the above, this study gives particular emphasis to modeling such the accidents as the side-right-angle collision, rear-end collision and side-swipe collision. The main results are the followings. First, six accident models are developed, which are all analyzed to be statistically significant. Second, the models are comparatively evaluated using the common and specific variables by accident type.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.