• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.133-144
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• 2009

In Korea, the ITS project has been progressed to improve traffic mobility and safety. Further, it is to relieve traffic jam by supply real time travel information for drivers and to promote traffic convenience and safety. It is important that the traffic information is provided accurately. This study was conducted outlier elimination and missing data adjustment to improve accuracy of raw data. A method for raise reliability of travel time prediction information was presented. We developed Historical Profile model and adjustment formula to reflect quality of interrupted flow. We predicted travel time by developed Historical Profile model and adjustment formula and verified by comparison between developed model and existing model such as Neural Network model and Kalman Filter model. The results of comparative analysis clarified that developed model and Karlman Filter model similarity predicted in general situation but developed model was more accurate than other models in incident situation.

• Journal of Service Research and Studies
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• v.4 no.2
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• pp.49-56
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• 2014

Lately, We have experienced increasing of Smartphone. Therefore we have experienced increasing of Application for Smartphone. This study suggested Tour Management Application using the smartphone. When you travel, you need various information This study of Development for Application provide that guide of travel destination provide means of transportation, time of travel, traveling expensives management. This application will use conveniently This application provide various information of travel and this application will use tool of new mobile business.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.453-454
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• 2017

The travellers using social media leave their location history in the form of trajectories. These trajectories can be bridged for acquiring information, required for future recommendation for the future travelers, who are new to that location, providing all sort of information. In this paper, we propose a personalized travel path recommendation scheme based on social life log. By taking advantage of two kinds of social media such as travelogue and community contributed photos, the proposed scheme can not only be personalized to user's travel interest but also be able to recommend a travel path rather than individual Points of Interest (POIs). It also maps both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.10-21
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• 2013

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.23-29
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• 2003

This study is the empirical research about application and evaluation of AVI that is an essential technology for calculating and providing the travel time. Travel time calculation and provision is one of the technique for information collecting and providing in the ITS. Through the field test on a national highway, we proposed the travel time calculation technique from the data by non-contact vehicle detecting method and validated field application performance with field data. We proposed the technique of evaluating field application performance, then using this, analyzed recognition rate, detection rate and travel time with field data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1033-1041
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• 2017

Path travel time based on departure time (PTTDP) is key information in advanced traveler information systems (ATIS). Despite the necessity, forecasting PTTDP is still one of challenges which should be successfully conquered in the forecasting area of intelligent transportation systems (ITS). To address this problem effectively, a methodology to dynamically predict PTTDP between motorway interchanges is proposed in this paper. The method was developed based on the relationships between traffic demands at motorway tollgates and PTTDPs between TGs in the motorway network. Two different data were used as the input of the model: traffic demand data and path travel time data are collected by toll collection system (TCS) and dedicated short range communication (DSRC), respectively. The proposed model was developed based on k-nearest neighbor, one of data mining techniques, in order for the real applications of motorway information systems. In a feasible test with real-world data, the proposed method performed effectively by means of prediction reliability and computational running time to the level of real application of current ATIS.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.53-64
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• 2010

This study is aimed at identifying the association of change of travel frequencies with information and communications technologies, commuting behavior for 995 workers in the Korea Capital Region. The study surveyed total 995 commuters whose their individual character, commuting behavior, land use as well as ICTs. The measures of the commuting behavior was comprised of a main commuting mode, a use tern, total travel time, and those of land use was the distance from house/office to subway station, and those of ICTs was data and information collection, communication and leisure, online selling or purchases, finance and a civil application, cellular phone service using capacity and so on. The results indicate that commuting behavior, land use, and ICTs are positively associated to change of travel frequencies. Specifically, longer total travel time, or far from house/office to subway station, tend to reduce commuting behavior and collect data and information through internet

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.833-839
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• 2019

Bus Information System (BIS) collects information related to the operation of buses and provides information to users through predictive algorithms. Method of predicting through recent information in same section reflects the traffic situation of the section, but cannot reflect the characteristics of the target line. The method of predicting the historical data at the same time zone is limited in forecasting peak time with high volatility of traffic flow. Therefore, we developed a pattern recognition bus arrival time prediction algorithm which could be overcome previous limitation. This method recognize the traffic pattern of target flow and select the most similar past traffic pattern. The results of this study were compared with the BIS arrival forecast information history of Seoul. RMSE of travel time between estimated and observed was approximately 35 seconds (40 seconds in BIS) at the off-peak time and 40 seconds (60 seconds in BIS) at the peak time. This means that there is data that can represent the current traffic situation in other time zones except for the same past time zone.

• Convergence Security Journal
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• v.21 no.3
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• pp.67-73
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• 2021

According to a survey by the Korea Tourism Organization in 2018 and 2019, traveling to places recorded in TV programs or influential videos has become an important travel trend and several studies show that watching a travel program improves the intention to visit the places featured on the program. This study designed a travel program-linked data service that provides additional information on the places and events to the viewers of the travel program. Specifically, the additional information of the travel program was defined in a formal manner by dividing it into places and events, and a method of exposure of the additional information conformed to the contents of the program was designed. We also devised an international standard DVB-based data transmission method that provides the additional information to data services appropriately in time for program broadcasting. This study is significant in that it tested new applications of data services for travel programs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.