• KSCE Journal of Civil and Environmental Engineering Research
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• v.3 no.2
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• pp.119-126
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• 1983

The purposes of this study are to analyze/model the mode choice behavior of the regional traveler by express bus/express train and to offer useful source in deciding the public transportation policy. The data analyzed were trips of both modes from March, 1980 to November, 1981, between Seoul and other nineteen cities; the data were grouped as five groups according to the change of service variables. Service variables were travel time(unit: minute), cost(:won), average allocation time(:won), service hour(:hour), and dummy variables by mode. As model Logit Model with linear or log utility function were postulated. As the result of this study, some reseanable models were constructed at Model Type I(eq. 2. of this paper) based on the above data except the dummy. It was judged that the parameters calibrated by Group III and Group IV data in table 4, were optimal. Among the parameters, the parameter of travel cost was most reliable. There was a tendency preferring express bus to train in October and November. With the constructed model and Pivot-Point Method. the demand change of express train caused by the service variables' change could be forecasted over 99%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2493-2501
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• 2013

Various schemes of transportation demand management(TDM) to discourage the use of cars and enhance public transit performance have been implemented in large cities. Nevertheless, policy effects in reducing car have not been satisfactory. Car-dependent travelers who tend to keep driving cars regardless of the change of the trip circumstances as such increase of travel time and cost according to car use or improvement of public transit service may be due to not according to utility reflecting mode-specific impedance and their own socio-economic characteristics. In this study, travelers were classified into four groups by their choice frequency of private car and public transit in unspecified multiple trip(car-dependent, car-choice, public transit-choice, public transit-dependent class). And the characteristics of each group were comparative analyzed. The results show that the group of a higher car-dependent is a higher priority on convenience and comfortability of the car when making decisions and the group of a lower of car-dependent is likely to change to public transit.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.2
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• pp.1-8
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• 2011

Traffic congestion is a source of significant economic and social costs in urban areas. Intelligent Transportation Systems (ITS) are a promising means to help alleviate congestion by utilizing advanced sensing, computing, and communication technologies. This paper proposes and investigates a basic and advanced ITS framework Advanced Traveler Information System (ATIS) using wireless Vehicle to Roadside (Centralized ATIS model: CA model) and Vehicle to Vehicle (DeCentralized ATIS model: DCA model) communication and assuming an ideal communication environment in the typical $6{\times}6$ urban grid traffic network. Results of this study indicate that an ATIS using wireless communication can save travel time given varying combinations of system characteristics: traffic flow, communication radio range, and penetration ratio. Also, all tested metrics of the CA and DCA models indicate that the system performance of both models is almost identical regardless of varying traffic demand and penetration ratios. Therefore, DCA model can be a reasonable alternative to the fixed infrastructure based ATIS model (CA model).

• Journal of Korean Society of Transportation
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• v.33 no.6
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• pp.575-583
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• 2015

The Korea Transport Institute (KOTI) builds the origin and destination(O-D) trip data with relatively smaller zone size such as Eup, Myeon, Dong administration unit districts in metropolitan area. Otherwise, O-D trip data was built by bigger size of traffic analysis zone(TAZ) such as Si, Gun, Gu administration unit districts for rural area. In some cases, it is needed to divide a zone into several sub-zones for rural area in order to analyze travel distribution pattern in detail for a certain highway and rail project. The study suggested a method to estimate O-D trips for sub-zones in the larger-size zones in rural area. Two different distribution models, direct demand model and gravity model, were calibrated for sub-zone's intra-zonal O-D trip pattern with metropolitan area O-D data which has smaller zone-size (sub-zone) data categorized by low, middle and high population density. The calibration results were compared between the two models. The gravity model with impedance function of power functional form was selected with better explanation for all groups in the metropolitan area. The adjusted $R^2$ was 0.7426, 0.6456 and 0.7194 for low, middle and high population density group, respectively. The suggested O-D trip estimating method is expected to produce enhanced trip patterns with sub-divided small zones.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.45-55
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• 1986

This study proposes the explanatory indices of urban traffic for the purpose of solving the ambiguity of selection of the explanatory variables, which always raises problems in case of the travel-demand forecasting in the urban transportation planning, and develops optimal urban traffic generation models. The multiple regression models for objective traffic generation are developed by using the proposed explanatory inidces. Objective variables that can be explained by one explanatory variable are modified into simple regression type (Y=bX) in order to ensure the nonnegativity of traffic generation. Similarities are noted in the generaton characteristics of generated traffic from homogeneous land-use activity. Objective variables that can not be explained by multiple variable, such as trip attraction of school and trip generation of social-recreation, are classified by the characteristics of each zone. And traffic generation forecasting models are built as homogeneous zone group, the validity of each model being tested by a statistical method. It is desired that the forecasting precision is in improved by easy and simple method. Accordingly, trip generation rates are calculated from each land-use activity, and trip generation rates for practical application are proposed by considering their stability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.667-677
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• 2013

The introduction of a new transit facility affects mode share of travel alternatives. The multinomial logit model, which has been the most commonly used for estimating mode share, has difficulty in reflecting heterogeneity of travelers' choices, and it has a limitation on grasping their characteristics of mode choice. The limitation may lead to over- or under-estimation of the new transit facility and bring about significant social costs. This paper aims to find a methodology to overcome the problem of preference homogeneity. It also applies market segmentation structure of separating the whole population into direct and indirect beneficiary to consider their preference heterogeneity. A mode choice model is estimated on data from Jeju Province and statistically tested. The results show that mode transfer rate of direct beneficiaries that inhabit in downtown areas increases as the new transit facility provides more advanced services with higher costs. The results and the model suggested in this study can contribute to improving the accuracy of demand forecasting of new transit facilities by reflecting heterogeneity of mode-transfer patterns.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.