• Journal of Korean Society of Water and Wastewater
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• v.12 no.2
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• pp.76-82
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• 1998

It is necessary to calculate the accurate velocity from the hydraulic model for the reliable prediction of water quality changes in water supply system. To verify the hydraulic analysis of the water supply system, fluoride was used as a tracer to calculate the travel time from the injection point to the sampling points. Results from this field experiment indicate that fluoride can be a good conservative tracer while it showed a little longitudinal dispersion along the pipe lines. And the velocity from the model was verified by these travel times and calibrated by changing the ratio of the unaccountable water. When the ratio of the unaccountable water. When the ratio of the unaccountable water was 20%, the error between the estimation of hydraulic model and the real travel time was minimum.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1162-1181
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• 2023

Tourism flow is not only the manifestation of tourists' special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.91-102
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• 2003

There are two goals in this paper. The one is development of existing CA(Cellular Automata) model to explain more realistic deceleration process to stop. The other is the application of the updated CA model to forecasting simulation to predict short term link travel time that takes a key rule in finding the shortest path of route guidance system of ITS. Car following theory of CA models don't makes not response to leading vehicle's velocity but gap or distance between leading vehicles and following vehicles. So a following vehicle running at free flow speed must meet steeply sudden deceleration to avoid back collision within unrealistic braking distance. To tackle above unrealistic deceleration rule, “Slow-to-stop” rule is integrated into NaSch model. For application to interrupted traffic flow, this paper applies “Slow-to-stop” rule to both normal traffic light and random traffic light. And vehicle packet method is used to simulate a large-scale network on the desktop. Generally, time series data analysis methods such as neural network, ARIMA, and Kalman filtering are used for short term link travel time prediction that is crucial to find an optimal dynamic shortest path. But those methods have time-lag problems and are hard to capture traffic flow mechanism such as spill over and spill back etc. To address above problems. the CA model built in this study is used for forecasting simulation to predict short term link travel time in Kangnam district network And it's turned out that short term prediction simulation method generates novel results, taking a crack of time lag problems and considering interrupted traffic flow mechanism.

• 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.

• Tunnel and Underground Space
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• v.11 no.1
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• pp.42-58
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• 2001

In order to protect the environment from waste disposal activities, the prediction of the flux and flow paths of the contaminants from underground facilities should be assessed as accurately as possible. Especially, the prediction of the pathways and travel times of the nuclides from high level radioactive wastes in a deep repository to biosphere is one of the primary tasks for assessing the ultimate safety and performance of the repository. Since the contaminants are mainly transported with groundwater along the discontinuities developed within rock mass, the characteristics of groundwater flow through discontinuities is important for the prediction of contaminant fates as well as safety assessment of a repository. In this study, the actual fracture network could be effectively generated based on in situ data by separating geometric parameter and hydraulic parameter. The calculated anisotropic hydraulic conductivity was applied to a 3D porous medium model to calculate the path flow and travel time of the large studied area with the consideration of the complex topology in the area. Using the model, the pathways and travel times for groundwater were analyzed. From this study, it was concluded that the suggested techniques and procedures for predicting the pathways and travel times of groundwater from underground facilities to biosphere is acceptable and those can be applied to the safety assessment of a repository for radioactive wastes.

• 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 Korean Society of Transportation
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• v.13 no.1
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• pp.5-33
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• 1995

This paper addresses the outlines of the traffic signal timing principles engaged in TRACS and the results of field test. Research team, encompassing research institute, university, and electronic company, conducted the three-year project for developing the new system, named TRACS(Traffic Adaptive Control System). The project was successfully completed in 1994. TRACS aims at accomplishing the objectives of better traffic adaptability and more reliable travel time prediction. TRACS operates in real-time adjusting signal timings throughout the system in response to variations in traffic demand and system capacity. The purpose of TRACS is to control traffic on an area basis rather than on an isolated intersection basis. An other purpose of TRACS is to provide real-time road traffic information such as volume, speed, delay , travel time, and so on. The performance of the first version of TRACS was compared to the conventional TOD control through field test. The test result was promi ing in that TRACS consistantly outperformed the conventional control method. The change of signaltiming reacted timely to the variation of traffic demand. Extensive operational test of TRACS will be conducted this year, and some functions will be enhanced.

• 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.

• 한국ITS학회:학술대회논문집
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• 2004.11a
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• pp.331-337
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• 2004

• 한국ITS학회:학술대회논문집
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• 2004.11a
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• pp.154-162
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• 2004