• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3D
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• pp.215-222
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• 2010

Average speed denotes a travel speed based on the average travel time of vehicles to traverse a segment of roadway, and average travel speed is used as a measure of effectiveness (MOE) suggested in the highway capacity manual (HCM) for evaluating the level of service (LOS) of roadway. Most of the urban freeways in our country are having congestion problem regardless of the rush hours as a high-speed highway with a speed limit of 80km/h or less. Especially traffic congestion within the ramp junction areas is becoming worse by the increased traffic and lack of links with the arterials around the urban freeway. So, the purpose in this study is to identify the traffic characteristics within the ramp junction areas of urban freeway, predict the average speed within the ramp junction areas based on the traffic characteristics identified, and finally prove the validity of the average speed predicted.

• Journal of Drive and Control
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• v.20 no.4
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• pp.71-79
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• 2023

In the field of agricultural machinery, various on-field tests are conducted to measure design load for optimal design of agricultural equipment. However, field test procedures are costly and time-consuming, and there are many constraints on field soil conditions due to weather, so research on utilizing simulation to overcome these shortcomings is needed. Therefore, this study aimed to model agricultural soils using discrete element method (DEM) software. To simulate draft force, predictions are made according to travel speed and compared to field test results to validate the prediction accuracy. The measured soil properties are used for DEM modeling. In this study, the soil property measurement procedure was designed to measure the physical and mechanical properties. DEM soil model calibration was performed using a virtual vane shear test instead of the repose angle test. The DEM simulation results showed that the prediction accuracy of the draft force was within 4.8% (2.16~6.71%) when compared to the draft force measured by the field test. In addition, it was confirmed that the result was up to 72.51% more accurate than those obtained through theoretical methods for predicting draft force. This study provides useful information for the DEM soil modeling process that considers the working speed from the perspective of agricultural machinery research and it is expected to be utilized in agricultural machinery design research.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of Welding and Joining
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• v.11 no.2
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• pp.74-85
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• 1993

Effect of Si metal powders addition with the plasma transferred arc(PTA) overlaying process on characteristics of the alloyed layer in aluminum alloy(A5083) has been investigated. The overlaying conditions were 175-250A in plasma arc current, 500mm/min in travel speed, the 5-20g/min in powder feeding rate. Main results obtained are summarized as follows. 1)Sufficient size of molten pool on surface of base metal was required for forming an alloyed layer; in a fixed travel, the formation of alloyed layer with clear and beautiful surface depend upon the plasma arc current and powder feeding rate; the greater plasma arc current and the smaller powder feeding rate were, the better bead was formed. Optimum alloyed conditions by which an excellent alloyed bead obtained was 225A in plasma arc current. PTA process made it possible to form an alloyed layer with up to 67wt% Si. 2)Microstructure in the alloyed layer was in accord with prediction from the Al-Si phase diagram 3)The hardness of the alloyed layer increased in proportion to Si content. 4)As volume fraction of primary Si increased, the specific wearness of the alloyed layer was significantly improved. However, no further improvement was found when the volume fraction was greater than about 30%. 5)Utilizing the PTA process, a crack free alloyed layer with maximum hardness of about Hv 310 could be obtained.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

• 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.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.256-267
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• 2015

The intelligent transportation systems allow us to have valuable opportunities for collecting wide-area coverage traffic data. The significant efforts have been made in many countries to provide the reliable traffic conditions information such as travel time. This study analyzes the impacts of the fog weather conditions on the traffic stream. Also, a strategy for predicting the long-term traffic speeds is developed under foggy weather conditions. The results show that the average of speed reductions are 2.92kph and 5.36kph under the slight and heavy fog respectively. The best prediction performance is achieved when the previous 45 pattern cases data is used, and the 14.11% of mean absolute percentage error(MAPE) is obtained. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.9-15
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• 2023

In this paper, we propose a method predicting the speed of each lane from the link speed using a neural network. We took three measures for configuring learning data to increase prediction accuracy. The first one is to expand the spatial range of the data source by including 14 links connected to the beginning and end points of the link. We also increased the time interval from 07:00 to 22:00 and included the data generation time in the feature data. Finally, we marked weekdays and holidays. Results of experiments showed that the speed error was reduced by 21.9% from 6.4 km/h to 5.0 km/h for straight lane, by 12.9% from 8.5 km/h to 7.4 km/h for right turns, and by 5.7% from 8.7 km/h to 8.2 km/h for left-turns. As a secondary result, we confirmed that the prediction accuracy of each lane was high for city roads when the traffic flow was congested. The feature of the proposed method is that it predicts traffic conditions for each lane improving the accuracy of prediction.

• 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.