• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.699-708
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• 2005

It becomes easy and generalized to track the cellular phone users and vehicles according to the Progress of wireless telecommunication, the spread of network, and the miniaturization of terminal devices. It has been constantly studied to provide location based services to furnish suitable services depending on the positions of customers. Various vehicle tracking and management systems are developed to utilize and manage the vehicle locations to relieve the congestion of traffic and to smooth transportation. However the designed previous work can not evaluated in real world, because most of previous work is only designed not implemented and it is developed for simple model to handle a point, a line, a polygon object. Therefore, we design a moving object query language and implement a vehicle management system to search the positions and trajectories of vehicles and to analyze the cost of transportation effectively. The designed query language based on a SQL can be utilized to get the trajectories between two specific places, the departure time, the arrival time of vehicles, and the predicted uncertainty positions, etc. In addition, the proposed moving object query language for managing transportation vehicles is useful to analyze the cost of trajectories in a variety of moving object management system containing transportation.

• Journal of Korean Society of Transportation
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• v.31 no.1
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• pp.47-56
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• 2013

The present study proposed a methodology to analyze the networking efficiency of arterial road networks. The methodology was motivated to design three-dimensional networks and to analyze the networking ability quantitatively, which is a novel approach compared to existing methods depending on the two-dimensional network definition and the qualitative analysis for improving arterial road networks. The method considered the interdependence between high-level freeways and low-level highways, the ITS-based information for traffic and road conditions, and the physical networking. These three factors were quantified by a networking index (NI), and the networking efficiency was measured by a networking rate (NR). The present study proved that the networking efficiency (NR) was influenced by travel information sharing (i.e., ITS) and physical factors. This supports the fact that the integrated improvements of physical and ITS factors are necessary for an arterial road. The proposed method was applied for an actual arterial road network. It was found that the nation-wide NR was higher than that for the metropolitan area, which might be due to the difficulty in switching between high- and low-level networks and the lack of ITS functions in the metropolitan area.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.197-209
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• 2017

With the advent of age that people spend more time and money on leisure activities, there is increasing interest in professional sport games. The location of large scale sport facilities has substantial impacts on existing transportation pattern because the facility attracts and generates massive traffic volume within a short period of time. This study aims to develop a mode choice model of leisure trips of which the destinations are a sport facility. A structured SP (stated preference) survey questionnaires were developed through an experimental design, and professional sport spectators were asked to state their preference in the choice of transport mode to the sport facility. The survey results show that public transportation is preferred to passenger cars for their trip to big sports event, implying that the convenience of back home trip after the event is an important factor of their mode choice. This study is a rare research on the trip pattern to sports complex in Korea, which provides policy implications on the provision of mass transit including subway system to large scale sport complexes. And it is also expected that this study contributes to future researches on leisure trip pattern.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.195-202
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• 2021

The purpose of this study is to classify national ships into passenger ships, cargo ships, oil tankers, tugboats, barges, and other ships by use, and compare the rate of change and direction respectively. In this study, a total of 123 monthly data were searched from January 2011 to March 2021 in Statistics Korea's KOSIS "Traffic Logistics => Operating Vessel Statistics => Possesion State of National Vessels". To this end, we calculated the rate of change from the previous month for each ship and performed numerical analysis and model analysis. In the correlation analysis, the Total showed a high relationship in the order of Towing, Barge, Oil, Cargo and Ferry. In the regression analysis, each ship was found to be statistically significant and varied independently of each other. The increase rate was highest in the order of Ferry, Oil, Towing, Barge and Cargo during the last analysis period. In the analysis of Scatter Charts, Towing and Barge showed more than a certain level of synchronization with respect to the Total. The synchronization phenomenon for each ships was calculated to be rather low, indicating that the correlation between each ships was low. However, the correlation between Oil and Towing, Oil and Barge and Towing and Barge is relatively high, indicating a relatively large correlation.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1255-1263
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• 2021

While it could become an alternative water resource, fog could undermine traffic safety and operational performance of infrastructures. To reduce such adverse impacts, it is necessary to have spatially continuous fog risk information. In this work, tree-based machine-learning models were developed in order to quantify fog risks with routine meteorological observations alone. The Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGB), and Random Forests (RF) were chosen for the regional fog models using operational weather and visibility observations within the Jeollabuk-do province. Results showed that RF seemed to show the most robust performance to categorize between fog and non-fog situations during the training and evaluation period of 2017-2019. While the LGB performed better than in predicting fog occurrences than the others, its false alarm ratio was the highest (0.695) among the three models. The predictability of the three models considerably declined when applying them for an independent period of 2020, potentially due to the distinctively enhanced air quality in the year under the global lockdown. Nonetheless, even in 2020, the three models were all able to produce fog risk information consistent with the spatial variation of observed fog occurrences. This work suggests that the tree-based machine learning models could be used as tools to find locations with relatively high fog risks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.25-39
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• 2018

The purpose of this study is to develop an efficient incident detection algorithm by applying machine learning, which is being widely used in the transport sector. As a first step, network of the target site was constructed with micro-simulation model. Secondly, data has been collected under various incident scenarios produced with combination of variables that are expected to affect the incident situation. And, detection results from both McMaster algorithm, a well known incident detection algorithm, and the Naive Bayes algorithm, developed in this study, were compared. As a result of comparison, Naive Bayes algorithm showed less negative effect and better detect rate (DR) than the McMaster algorithm. However, as DR increases, so did false alarm rate (FAR). Also, while McMaster algorithm detected in four cycles, Naive Bayes algorithm determine the situation with just one cycle, which increases DR but also seems to have increased FAR. Consequently it has been identified that the Naive Bayes algorithm has a great potential in traffic incident detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.188-197
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• 2019

This study analyzed empirically the effects of the perception of outbound passengers, who flew from Korea to overseas tourist destinations, on the cabin service quality, tourist experience, and tourist satisfaction. For empirical analysis, a survey was given to Korean passengers using a national carrier of South Korea. Based on empirical analysis, the researchers judged that the analysis results and implications could be applied to inbound foreign passengers who visited Korea for the tourism purposes if researchers generated significant results from empirical analysis. This paper designed a research model that represented the meaningful relationship among, airline cabin-service quality, tourist experience, and tourist satisfaction following preceding research. According to the analysis, that factors that consisted of the cabin service quality had a significant effect on the tourist satisfaction with a mediating effect of both the cognitive and emotional tourist experience. Therefore, the perception on airline's cabin service quality of the national carrier would be a starting point that can help improve the tourist satisfaction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.64-78
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• 2019

This paper explores key factors affecting taxi travel using global positioning system(GPS) data in Seoul, Korea, considering spatial dependence. We first analyzed the travel characteristics of taxis such as average travel time, average travel distance, and spatial distribution of taxi trips according to the time of the day and the day of the week. As a result, it is found that the most taxi trips were generated during the morning peak time (8 a.m. to 9 a.m.) and after the midnight (until 1 a.m.) on weekdays. The average travel distance and travel time for taxi trips were 5.9 km and 13 minutes, respectively. This implies that taxis are mainly used for short-distance travel and as an alternative to public transit after midnight in a large city. In addition, we identified that taxi trips were spatially correlated at the traffic analysis zone(TAZ) level through the Moran's I test. Thus, spatial regression models (spatial-lagged and spatial-error models) for taxi trips were developed, accounting for socio-demographics (such as the number of households, the number of elderly people, female ratio to the total population, and the number of vehicles), transportation services (such as the number of subway stations and bus stops), and land-use characteristics (such as population density, employment density, and residential areas) as explanatory variables. The model results indicate that these variables are significantly associated with taxi trips.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.55-64
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• 2021

Public libraries are a space where residents learn a wide range of knowledge and ideologies, and as they are directly connected to life, various related studies have been conducted. In most previous studies, variables such as population, traffic accessibility, and environment were found to be highly relevant to library use. In this study, it can be said that the difference from previous studies is that the book borrow demand and relevance were analyzed by reflecting the variables of cultural characteristics based on the book borrow history (1,820,407 cases) and member information (297,222 persons). As a result of the analysis, it was analyzed that as the increase in borrows for social science and literature books compared to technical science books, the demand for book borrows increased. In addition, various descriptive statistical analyzes were used to analyze the characteristics of library book borrow demand, and policy implications and limitations of the study were also presented based on the analysis results. and considering that cultural characteristics change depending on the location and time of day, it is believed that related research should be continued in the future.