• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.209-230
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• 2007

This paper analyzed the social cost function of a congestion-prone service system, which is developed from the social cost minimization problem. The analysis focused on the following two issues that have not been explicitly explored in the previous studies: the effect of the heterogeneity of value-of-travel-times among customers on the structure of cost functions; and the structure of the supplier cost function constituting the social cost function. The analysis gave a number of findings that could be summarized as follows. First, the social marginal cost for one unit increase in system output having a certain value-of-travel-time is the sum of the service time cost for that value-of-travel-time and the marginal congestion cost for the average value-of-service-time of all the system outputs. Second, the marginal congestion cost equals the marginal supplier cost of system output under the condition that supplier compensates the customers for the changed service time costs which is incurred by the marginal capacity increase necessary for economically facilitating an additional system output. Third, the compensated marginal cost is the multiple of the marginal capacity cost and the inverse of system utilization ratio, if the service time function is homogeneous of degree zero in its inputs.

• Journal of Korean Society of Transportation
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• v.25 no.5
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• pp.209-231
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• 2007

This paper suggested an approach to characterize travel choice behaviors using the implicit price instead of the indirect utility. The choice criterion to compare the implicit prices of available trip options was developed from the utility maximization problem of a trip maker which is supposed to choose the best option from the available ones differentiated by only by the quantitative attributes such as travel cost and time but also by qualitative attributes such as comfort and safety. The utility maximization problem is constructed under household production theory, and is incorporated with a special kind of joint homogeneous production functions. The implicit price of a certain trip option is the sum of the monetary price and the multiple of travel time and the value-of-travel-time, and the value-of-travel-time refers to the portion of wage, which can be assignable to the trip-making activity. This choice criterion is statistically identifiable, and behaviorally plausible. Moreover, this criterion has the expression simpler than the indirect utility, and therefore could be an effective target of the statistical estimation for travel choice behaviors.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2270-2277
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• 2010

Recently the role of a railroad is emphasized as the imprortant part for growth for the low carbon green growth and the social consensus about the railroad investment is actively progressed. The various social economic value in which the travel demand change over of the environment-friendly railroad system can bring becomes with public debate as much as a discussion is not any more necessary. In this research, the influence that it recognized that the travel time improvement by the high speed rail was the most important element among many method that it can increase the railroad use efficiency in which it connects the interregional major cities and the railway travel time improvement reaches to the rate of railway and road travel time, and the environment like the carbon emission quantity was comprehensively inquired.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.6
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• pp.1873-1879
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• 2014

There are various methodologies to forecast the travel time using real-time data but the K-nearest neighborhood (KNN) method in general is regarded as the most one in forecasting when there are enough historical data. The objective of this study is to evaluate applicability of KNN method. In this study, real-time and historical data of toll collection system (TCS) traffic flow and the dedicated short range communication (DSRC) link travel time, and the historical path travel time data are used as input data for KNN approach. The proposed method investigates the path travel time which is the nearest to TCS traffic flow and DSRC link travel time from real-time and historical data, then it calculates the predicted path travel time using weight average method. The results show that accuracy increased when weighted value of DSRC link travel time increases. Moreover the trend of forecasted and real travel times are similar. In addition, the error in forecasted travel time could be further reduced when more historical data could be available in the future database.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.47-59
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• 2000

The purpose of this study is to develop travel-time estimation model using neural networks and prediction model using neural networks and kalman-filtering technique. The data used in this study are travel speed collected from inductive loop vehicle detection systems(VDS) and travel time collected from the toll collection system (TCS) between Seoul and Osan toll Plaza on the Seoul-Pusan Expressway. Two models, one for travel-time estimation and the other for travel-time Prediction were developed. Application cases of each model were divided into two cases, so-called, a single-region and a multiple-region. because of the different characteristics of travel behavior shown on each region. For the evaluation of the travel time estimation and Prediction models, two Parameters. i.e. mode and mean were compared using five-minute interval data sets. The test results show that mode was superior to mean in representing the relationship between speed and travel time. It is, however shown that mean value gives better results in case of insufficient data. It should be noted that the estimation and the Prediction of travel times based on the VDS data have been improved by using neural networks, because the waiting time at exit toll gates can be included for the estimation of travel time based on the VDS data by considering differences between VDS and TCS travel time Patterns in the models. In conclusion, the results show that the developed models decrease estimation and prediction errors. As a result of comparing the developed model with the existing model using the observed data, the equality coefficients of the developed model was average 88% and the existing model was average 68%. Thus, the developed model was improved minimum 17% and maximum 23% rather then existing model .

• The Journal of Industrial Distribution & Business
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• v.9 no.7
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• pp.61-70
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• 2018

Purpose - The purpose of this research is reflected on the rapid development of online tourism industries. The study was to establish the strategy for Korean tourism enterprises to develop tourist commodities suitable for Chinese tourists and attract them to visit Korea by the empirical analysis of the relation between repurchase intention of tourists and its premise variables (e-service quality, perceived value and satisfaction). Research design, data, and methodology - This research carried out a questionnaire survey on Chinese tourists who visited Korea with experience of using the online travel agency web sites. A total 398 answers were recovered, 41 of them were excluded due to the dishonest answers and 357 of them were finally analyzed. The data was analyzed with IBM SPSS AMOS 22.0. Results - The research results show that in the online travel agency web site e-service quality, convenience, interactivity, information validity, credibility had a positive impacts on perceived value and satisfaction. The perceived value of online travel agency website users has positive impart on satisfaction and repurchase intention. Satisfaction of online travel agency web site users have positive impacts on repurchase intention. But safety has no impact on perceived value while positive impacts on satisfaction was affected. Conclusions - First, in the online travel agency web site e-service quality, safety has no impact on perceived value while it was shown to have positive impacts on satisfaction because the users of online travel agency web sites believe that the protection of personal information, the defense of cracker and the safeguard of payment security are the basic premises of website operation. Although safety does not have impacts on perceived value, users benefits will suffer damage when hacker intrusion and other accidents occur so that online travel agency web sites should not ignore the security concerns. Second, credibility is a major concern for online travel agency web site users. At this time, it is necessary for the web site to establish a system to display both the commodity information and the using experience published on the user's SNS, thus improving the credibility of the website information.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.69-82
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• 2000

When new subway lines are considered to construct in a large city, there is a need to establish the appropriate transfer systems between subway and other transit modes, so as to increase the use of subway system. In this study, a multi-nominal logit model is developed to analyze the travel characteristics and the mode choice of subway Passengers transferring to and from the buses, minibuses and taxi. These passengers represent a large Portion of transit Passengers in Pusan city It shows that the explanatory variables that affect Passengers\` mode choice are in order of OVIT(Out-of-Vehicle Travel Time), IVTT(In-Vehicle Travel Time), transit fare,. income, gender, and age in modeling. In particular, OVTT is shown to have more significant impact on the mode choice than IYTT due to the fact that transfer trip is involved only in a short distance. Variables associated with the travel costs, however, do have an insignificant impact on the mode choice. It shows that it would be a better Policy to improve the quality of transit service using additional financial resource by increasing transit fare rather than by reducing the fare to increase travel demand. It also shows that value of travel time of OVTT is remarkably higher than that of IVTT and value of travel time of taxi Passengers is much higher than that of minibus Passengers .

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.1 no.1
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• pp.41-51
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• 2002

As Advanced Traveler Information System(ATIS) is the kernel of the Intelligent Transportation System, it is very important how to manage data from traffic information collectors on a road and have at borough grip of the travel time's change quickly and exactly for doing its part. Link travel time can be obtained by two method. One is measured by area detection systems and the other is estimated by point detection systems. Measured travel time by area detection systems has the limitation for real time information because it Is calculated by the probe which has already passed through the link. Estimated travel time by point detection systems is calculated by the data on the same time of each. section, this is, it use the characteristic of the various cars of each section to estimate travel time. For this reason, it has the difference with real travel time. In this study, Artificial Neural Networks is used for estimating link travel time concerned about the relationship with vehicle detector data and link travel time. The method of estimating link travel time are classified according to the kind of input data and the Absolute value of error between the estimated and the real are distributed within 5$\~$15minute over 90 percent with the result of testing the method using the vehicle detector data and AVI data of Namsan Tunnel $\#$1. It also reduces Time lag of the information offered time and draws late delay generation and dissolution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.599-604
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• 2010

In this study, the value of travel time was estimated with reflecting the fuel cost according to travel distance. The main objective of this study is whether the addition of the fuel cost as a factor for route choice behavior is appropriate or not, through the stated preference survey. The route choice model was developed using SP survey technique with the consideration of level difference and the value of travel time, toll and fuel costs. Consequently, the fuel cost is identified as a main factor like travel time and toll cost in choosing routes from drivers' viewpoints. Nevertheless, since toll costs are recognized as out-of-pocket expenses whereas fuel costs as periodical expenses, it seems drivers are more sensitive to toll than fuel costs.