• Korean Journal of Construction Engineering and Management
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• v.15 no.4
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• pp.150-158
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• 2014

A bunch of asphalt roads have been damaged frequently in relation to the rapid climate change. To solve and prevent this type of problems, many nationalities in the world have performed various researches. In this regard, the objective of this study is to develop prediction model as to the number of potholes occurred in seoul. At the same time, we have utilized empirical and statistical approaches in order for us to identify factors which is affecting the actual occurrence. The predictive model was determinded by using BHS (Basic Harmony Search) algorithm. Prediction was based on the weather and traffic data as well as data occurrence data of porthole. To assess the influences which are PAR(Pitch Adjusting Rate) and HMCR(Harmony Memory Considering Rate), we determined suitability by changing the values. In the process of the determining a predictive model, the predictive model composed Training data (2011, 2012 and 2013yrs data). To determine the suitability of the model, we have utilized Testing Set (2009 and 2010 yrs data). The suitability of the basic prediction model has been from RMSE(Root Mean Squared Error), MAE(Mean Absolute Error) and Coefficient of determination.

• Journal of the Korean Society for Railway
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• v.15 no.6
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• pp.623-630
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• 2012

Formation used as trackbed foundation for providing vertical bearing capacity onto rail foundation are composed of crushed stones usually with certain type of grain size distribution. Permanent deformation in trackbed foundation can be generated by increasing number of load repetition due to train traffic increases, causing track irregularity. In this study, a specially prepared trackbed foundation materials (M-40) used in Korea has been tested using a large repetitive triaxial compression apparatus in order to understand resilient and permanent deformation characteristics of the material. From these test results, resilient and permanent deformation characteristic are analyzed so that a permanent deformation model is developed which can consider number of load repetition N, confining stress (${\sigma}_3$), shear stress ratio(${\tau}/{\tau}_f$) and stiffness of the material.

• KNOM Review
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• v.23 no.1
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• pp.34-42
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• 2020

Network Function Virtualization (NFV) environment can deal with dynamic changes in traffic status with appropriate deployment and scaling of Virtualized Network Function (VNF). However, determining and applying the optimal VNF deployment is a complicated and difficult task. In particular, it is necessary to predict the situation at a future point because it takes for the process to be applied and the deployment decision to the actual NFV environment. In this paper, we randomly generate service requests in Multiaccess Edge Computing (MEC) topology, then obtain training data for machine learning model from an Integer Linear Programming (ILP) solution. We use the simulation data to train the machine learning model which predicts the optimal VNF deployment in a predefined future point. The prediction model shows the accuracy over 90% compared to the ILP solution in a 5-minute future time point.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.1-15
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• 2016

This study aims to develop a model by which city bus drivers who are likely to cause an accident can be figured out based on the information about their actual driving records. For this purpose, from the information about the actual driving records of the drivers who have caused an accident and those who have not caused any, significance variables related to traffic accidents are drawn, and the accuracy between models is compared for the classification models developed, applying a discriminant analysis and logistic regression analysis. In addition, the developed models are applied to the data on other drivers' driving records to verify the accuracy of the models. As a result of developing a model for the classification of drivers who are likely to cause an accident, when deceleration ($X_{deceleration}$) and acceleration to the right ($Y_{right}$) are simultaneously in action, this variable was drawn as the optimal factor variable of the classification of drivers who had caused an accident, and the prediction model by discriminant analysis classified drivers who had caused an accident at a rate up to 62.8%, and the prediction model by logistic regression analysis could classify those who had caused an accident at a rate up to 76.7%. In addition, as a result of the verification of model predictive power of the models showed an accuracy rate of 84.1%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.347-362
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• 2019

Underground excavation using TBM machines has been increasing to reduce complaints caused by noise, vibration, and traffic congestion resulted from the urban underground construction in Korea. However, TBM excavation design and construction still need improvement because those are based on standards of the technologically advanced countries (e.g., Japan, Germany) that do not consider geological environment in Korea at all. Above all, although TBM performance is a main factor determining the TBM machine type, duration and cost of the construction, it is estimated by only using UCS (uniaxial compressive strength) as the ground parameters and it often does not match the actual field conditions. This study was carried out as part of efforts to predict penetration rate suitable for Korean ground conditions. The effective parameters were defined through the correlation analysis between the penetration rate and the geotechnical parameters or TBM performance parameters. The effective parameters were then used as variables of the multiple regression analysis to derive a regression model for predicting TBM penetration rate. As a result, the regression model was estimated by UCS and joint spacing and showed a good agreement with field penetration rate measured during TBM excavation. However, when this model was applied to another site in Korea, the prediction accuracy was slightly reduced. Therefore, in order to overcome the limitation of the regression model, further studies are required to obtain a generalized prediction model which is not restricted by the field conditions.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.485-490
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• 2005

With the historical opening of the express rail, Korea has joined the league of France, Japan, Germany and Spain and entered into the super high-speed train era. Opening of the express rail will not only bring about enormous changes to the lives of Koreans, but it will also have a huge influence on the economic, social and cultural aspects of the country. With construction of the Seoul - Busan KTX line, railway passenger transportation capacity and freight transportation capacity will increase. Fast, safe, convenient and environmentally friendly, the express rail is a product of the latest technology and will secure its position as the newest and most preferred method of transportation for the next generation. As the traffic noise, train noise from KTX will become a social problems with the acceleration of speed and increase in the lines. In order to predict the train noise propagation from KTX, this paper presents the sound source system, the calculation model and the scale model experiment. Noise level unit patterns of a KTX that take the rolling noise, the motor noise and aerodynamic noise into consideration are simulated by the scale model experiment and numerical analysis.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.145-152
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• 2002

Real-time queueing information and/or predictive queue built-up information can be a good criterion in selecting travel options, such as routes, both for users, and for operators in operating transportation system. Provided properly, it will be a key information for reducing traffic congestion. Also, it helps drivers be able to select optimal roues and operators be able to manage the system effectively as a whole. To produce the predictive queue information, this paper proposes a predictive model for estimating and predicting queue lengths, mainly based on Kalman Filter. It has a structure of having state space model for predicting queue length which is set as observational variable. It has been applied for the Namsan first tunnel and the application results indicate that the model is quite reasonable in its efficacy and can be applicable for various ATIS system architecture. Some limitations and future research agenda have also been discussed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.14 no.6
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• pp.607-616
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• 2012

The need for management of tunnel air quality is imminent considering the rapid increase of number and span of tunnels in Korea. To investigate spatial distribution of $CO_2$ within tunnels, $CO_2$ were measured and model simulations were performed in Namsan 1 tunnel. Results show that $CO_2$ concentrations were 250 ppm to 400 ppm higher in the exit than tunnel entrance. Also, $CO_2$ concentrations were 200 ppm to 300 ppm lower inside no ventilating vehicle than in the tunnel. Both experimental and model simulation results show that spatial distribution and concentration gradient of air pollutant inside tunnel are highly dependent on traffic density.

• ETRI Journal
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• v.44 no.2
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• pp.220-231
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• 2022

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet-of-Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi-parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people-parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real-time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi-parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.321-330
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• 2020

The purpose of this study is to present the appropriate standby space for the mechanical parking lot considered parking queue. This analysis is based on the field-study by results of the influential factors on the parking queue of mechanical parking lots in the commercial area of Gwang-ju metropolitan city. In this study, the parking queue was analized through the simple modeling using Vissim based on average service rate and average arrival rate from the result of field-study. As a result of applying the field survey products to the theoretical queue model, no significant result was obtained when the traffic intensity exceeded 1. Therefore, parking queue was analyzed through simple modeling using Vissim, and the model for calculating the proper parking queue size of the mechanical parking lot by size was derived. The model for estimating of an appropriate mechanical parking standby space considering parking queue presented in this study is expected to be a criterion for considering the appropriate parking space of a new building, and also it can be used to minimized the traffic impact due to the parking queue by the lack of standby space.