• International Journal of Highway Engineering
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• v.15 no.6
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• pp.103-111
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• 2013

PURPOSES : Previous Speed Profile reflects the patterns of speeds in sections of tangents to curves in the roads. However these patterns are uniform of speeds and Acceleration/Deceleration. In oder to supplement these shortcomings. this study made a new profile which can contain factors of Acceleration/Deceleration through theories of Previous Speed Profiles. METHODS : For sakes, this study developed the speed prediction model of Rural Multi-Lane Highways and calculated Acceleration/Deceleration by appling a Polynomial model based on developed speed prediction model. Polynomial model is based on second by second. Acceleration/Deceleration Profile is developed with the various scenarios of road geometric conditions. RESULTS : The longer an ahead tangent length is, The higher an acceleration rate in curve occurs due to wide sight distance. However when there are big speed gaps between two curves, the longer tangent length alleviate acceleration rate. CONCLUSIONS : Acceleration/Deceleration Profile can overview th patterns of speeds and Accelerations/Decelerations in the various road geometric conditions. Also this result will help road designer have a proper guidance to exam a potential geometric conditions where may occur the acceleration/deceleration states.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.141-153
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• 2010

The study is about the development of operating speed prediction models aimed for an evaluation of design consistency of four lane rural roads. The main differences of this study relative to previous research are the method of data collection and classification of road alignments. The previous studies collected speed data at several points in the horizontal curve and approaching tangent. This method of collection is based on the assumption that acceleration and deceleration only occurs at horizontal tangents and the speed is kept constant at horizontal curves. However, this assumption leads to an unreliable speed estimation, so drivers' behavior is not well represented. Contrary to the previous approach, speed data were collected with one and data analysis using a speed profile is made for data selection before building final models. A total of six speed prediction models were made according to the combination of horizontal and vertical alignments. The study predicts that the speed data analysis and selection for model building employed in this study can improve the prediction accuracy of models and be useful to analyze drivers' speed behavior in a more detailed way. Furthermore, it is expected that the operating speed prediction models can help complement the current design-speed-based guidelines, so more benefits to drivers as real road users, rather than engineers or decision makers, can be achieved.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1681-1688
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• 2000

A dynamic model for the prediction of surface topography in high speed end milling process is developed. In this model the effect of tool runout, tool deflection and spindle vibration were taken in to account. An equivalent diameter of end mill is obtained by finite element method and tool deflection experiment. A modal parameter of machine tool is extracted by using frequency response function. The tool deflection, spindle vibration chip thickness and cutting force were calculated in dynamic cutting condition. The tooth pass is calculated at the current angular position for each point of contact between the tool and the workpiece. The new dynamic model for surface predition are compared with several investigated model. It is shown that new dynamic model is more effective to predict surface topography than other suggested models. In high speed end milling, the tool vibration has more effect on surface topography than the tool deflection.

• 한국전산유체공학회:학술대회논문집
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• 2004.03a
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• pp.17-22
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• 2004

A space launch vehicle departs the ground in a low speed, soon reaches a transonic and a supersonic speed, and then flies in a hypersonic speed into the space. Therefore, the design of a launch vehicle should include the prediction of aerodynamic characteristics for all speed regimes, ranging from subsonic to hypersonic speed. Generally, Empirical and analytical methods and wind tunnel tests are used for the prediction of aerodynamic characteristics. This research presents considerable factors for aerodynamic analysis of a launch vehicle using CFD. This investigation was conducted to determine effects of wake over the base section on the aerodynamic characteristics of a launch vehicle and also performed to determine effects of the sting which exist to support wind tunnel test model.

• 한국기계연구소 소보
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• s.9
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• pp.183-191
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• 1982

This report describes the analysis method of the full-scale propulsive performance by using the data of model test and the full-scale speed trial. The model test data were analyzed by the computer program "PPTT" based on "1978 ITTC Performance Prediction Method for Single Screw Ships." Also the full-scale speed trial data were analyzed by the computer program "SSTT" based on the newly proposed “SRS-KIMM Standard Method of Speed Trial Analysis." An analysis of model and full-scale test data was carried out for a 60.000 DWT Bulk Carrier and the correlation between model and full-scale ship was stuied.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.