• Journal of the Korean Geographical Society
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• v.40 no.5 s.110
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• pp.514-532
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• 2005

The purpose of this study is to critically examine conventional spatial measures of individual accessibility, which are based on the notion of spatial proximity, the single reference location, and the unlinked travel model. Using space-time accessibility measures with the travel-activity diary data set of Portland Metro, US, three expectations from spatial measures on spatial patterns of individual accessibility were empirically examined: (1) does individual accessibility decrease with an increase of distance from the CBD?; (2) does the spatial pattern of accessibility resemble that of urban opportunity density pattern?; and (3) are spatial patterns of individual accessibility of different socio- demographic population groups basically similar as people in the same area share the same geographic characteristics regardless of gender, race, age, and so on? First of all, the results showed that spatial variations in individual accessibility were not directly determined by spatial proximity and opportunity density as suggested by previous accessibility measures. The spatial pattern of individual accessibility was dramatically different from that of urban opportunity density High peaks of accessibility level were found far away from the CBD and regional centers. This finding might be associated with the importance of multi-reference locations and linked travels in shaping accessibility in reality. Furthermore, this study found that spatial patterns of accessibility clearly differ between men and women. These findings suggest that access requires more than proximity, and that the interaction between person-specific space-time constraints and the consequential availability of urban opportunities in space-time renders different accessibility experiences to people even in the same region, which would be one of the key ingredients missing from conventional spatial measures of accessibility.

• The Journal of Economics, Marketing and Management
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• v.11 no.1
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• pp.45-52
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• 2023

Purpose: The purpose of this study is to investigate the relationship between service quality, destination image, and tourist satisfaction for foreign tourists visiting Korea, and in particular, to analyze the moderating effect of psychological power in the relationship. Research design, data, and methodology: A research model was derived through existing literature research and a survey was conducted on foreign tourists visiting Korea. Structural equation model of SPSS and AMOS24.0 were used for data analysis and hypothesis testing. Results: The tourism service quality affects the image of tourist attractions. Images of tourist attractions were found to affect tourism satisfaction. It was found that the lower the psychological power, the greater the effect on the relationship between service quality and image, and the relationship between image and satisfaction. Conclusions: This study demonstrated that service quality at travel destinations is a key factor in order to enhance the image and satisfaction of Korean tourist destinations for foreigners visiting Korea. On the other hand, since the image and satisfaction level of the tourism destination vary depending on personal factors such as psychological power, it suggests that travel industry workers or researchers should develop and operate services tailored to the individual characteristics of the tourists.

• Journal of Korean Society of Transportation
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• v.23 no.4 s.82
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• pp.71-79
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• 2005

Existing arterial link travel time estimation methods relying on either aggregate point-based or individual section-based traffic data have their inherent limitations. This paper demonstrates the utility of data fusion for improving arterial link travel time estimation. If the data describe traffic conditions, an operator wants to know whether the situations are going better or worse. In addition, some traffic information providing strategies require predictions of what would be the values of traffic variables during the next time period. In such situations, it is necessary to use a prediction algorithm in order to extract the average trends in traffic data or make short-term predictions of the control variables. In this research. a multi-step ahead prediction algorithm using Data fusion was developed to predict a link travel time. The algorithm performance were tested in terms of performance measures such as MAE (Mean Absolute Error), MARE(mean absolute relative error), RMSE (Root Mean Square Error), EC(equality coefficient). The performance of the proposed algorithm was superior to the current one-step ahead prediction algorithm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.275-276
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• 2022

VTS operators instruct ships to wait for entry and departure to sail in one-way to prevent ship collision accidents in ports with narrow routes. Currently, the instructions are not based on scientific and statistical data. As a result, there is a significant deviation depending on the individual capability of the VTS operators. Accordingly, this study built a 1d-convolutional neural network model by collecting ship and weather data to predict the exact travel time for ship entry/departure waiting for instructions in the port. It was confirmed that the proposed model was improved by more than 4.5% compared to other ensemble machine learning models. Through this study, it is possible to predict the time required to enter and depart a vessel in various situations, so it is expected that the VTS operators will help provide accurate information to the vessel and determine the waiting order.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.55-65
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• 2008

For obtaining telematics traffic information(travel time or speed in an individual link), there are many kinds of devices to collect traffic data. Since the GPS satellite signals have been released to civil society, thank to the development of GPS technology, the GPS has become a very useful instrument for collecting traffic data. GPS can reduce the cost of installation and maintenance in contrast with existing traffic detectors which must be stationed on the ground. But. there are Problems when GPS data is applied to the existing filtering techniques used for analyzing the data collected by other detectors. This paper proposes a method to provide users with correct traffic information through filtering abnormal data caused by the unusual driving in collected data based on GPS. We have developed an algorithm that can be applied to real-time GPS data and create more reliable traffic information, by building patterns of past data and filtering abnormal data through selection of filtering areas using Quartile values. in order to verify the proposed algorithm, we experimented with actual traffic data that include probe cars equipped with a built-in GPS receiver which ran through Gangnam Street in Seoul. As a result of these experiments, it is shown that link travel speed data obtained from this algorithm is more accurate than those obtained by existing systems.

• Journal of Korean Society of Transportation
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• v.24 no.1 s.87
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• pp.29-38
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• 2006

After the first opening of the KTX in April 2004, travel time between major cities has been dramatically reduced. The reduction rates range from 32% to 47%. Considering travel time reduction between major cities, this study concerned about the intercity travel impact of the KTX operation. This study aimed to analyze intercity mode choice behavior of Daegu Citizens according to the first opening of the KTX. This study takes place in two sections. These are (i) the section of KTX between Daegu and Seoul, and (ii) the section of KTX between Daegu and Daejeon. This study estimated empirical models for analyzing intercity mode choice behavior according to the first opening of the KTX. This study makes use of the data from travel survey from Daegu metropolitan area. The main part of the survey was carried out in the KTX Dong-Daegu station. The survey data includes the information on travel from Daegu to Daejeon and from Daegu to Seoul. In order to analyze intercity choice behavior according to the frist opening of the KTX, multinomial model structure is used. For the model specification, a variety of behavioral assumptions about the factors which affect the mode choice, were considered. From the empirical model estimation, it is found that OVTT(Out-of-Vehicle Travel Time), OVTC(Out-of-Vehicle Travel Cost), IVTT(In-Vehicle Travel Time), IVTC(In-Vehicle Travel Cost), travel frequency, travel purpose, sex, age, occupation. household income, individual income are significant in choosing intercity travel mode. However, it is found that the intercity nde choice behavior is different between (i) the section of KTX between Daegu and Seoul, and (ii) the section of KTX between Daegu and Daejeon. Furthermore, some policy implications are discussed in conclusion.

• Journal of the Korean Society for Railway
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• v.19 no.5
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• pp.656-662
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• 2016

Based on time dependent location data of mobile phone users, users' ODs were produced after tracing their travel route and inducing their origins and destinations. System considered average signalizing frequency, which means that the longer the travel length is the more frequent the signal is. This is a home-based OD and is limited to the Seoul Metropolitan area. The OD matrix from the mobile phone data which was aggregated to the cell and transformed to the 'Dong' area, was compared to the KTDB OD. The results can be analyzed and it was determined that they are highly correlated because individual coefficients are 0.98 and 0.85, the former between the OD of this study and the KTDB Si/Gun/Gu unit area OD and the latter between the OD of this study and the Dong unit area KTDB OD.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.54-63
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• 2017

With the aim of promoting the use of metropolitan railways, the present research developed a mode choice model for evaluating its competitiveness against passenger cars. A case study was carried out with Gyeongeui and Joongang line, and the area of interest was the direct operating railway between Ilsan and Guri station where the two lines intersect. The mode choice model was a disaggregate behavior model which used Stated Preference (SP) survey data, and the plot of competition was between private passenger cars and express trains. As a result, the mode choice model was established, and this model was used to analyze characteristics of passengers' time value and elasticity. It was shown that reducing travel time is more efficient than reducing travel cost when it comes to operating express trains in metropolitan railways. Therefore, policies designed for activating the use of metropolitan railways should expand direct operating service of individual lines and run more express trains in order to minimize transfer and in-vehicle time.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.1
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• pp.29-48
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• 2014

The recent keyword advertising does not reflect the individual customer searching pattern because it is focused on each keyword at the aggregate level. The purpose of this research is to observe processes of customer searching patterns. To be specific, individual deal-proneness is mainly concerned. This study incorporates location as a control variable. This paper examines the relationship between customers' searching patterns and probability of purchase. A customer searching session, which is the collection of sequence of keyword queries, is utilized as the unit of analysis. The degree of deal-proneness is measured using customer behavior which is revealed by customer searching keywords in the session. Deal-proneness measuring function calculates the discount of deal prone keyword leverage in accordance with customer searching order. Location searching specificity function is also calculated by the same logic. The analyzed data is narrowed down to the customer query session which has more than two keyword queries. The number of the data is 218,305 by session, which is derived from Internet advertising agency's (COMAS) advertisement managing data and the travel business advertisement revenue data from advertiser's. As a research result, there are three types of the deal-prone customer. At first, there is an unconditional active deal-proneness customer. It is the customer who has lower deal-proneness which means that he/she utilizes deal-prone keywords in the last phase. He/she starts searching a keyword like general ones and then finally purchased appropriate products by utilizing deal-prone keywords in the last time. Those two types of customers have the similar rates of purchase. However, the last type of the customer has middle deal-proneness; who utilizes deal-prone keywords in the middle of the process. This type of a customer closely gets into the information by employing deal-prone keywords but he/she could not find out appropriate alternative then would modify other keywords to look for other alternatives. That is the reason why the purchase probability in this case would be decreased Also, this research confirmed that there is a loyalty effect using location searching specificity. The customer who has higher trip loyalty for specificity location responds to selected promotion rather than general promotion. So, this customer has a lower probability to purchase.